bulldozer-price-regression.ipynb

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  {
   "cell_type": "markdown",
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    "# Predicting Bulldozer Sales Price\n",
    "\n",
    "In this notebook, we will explore a dataset and build machine learning models to predict the auction sale price of bulldozers 🚜. This idea and data were taken from this [Kaggle Competition](https://www.kaggle.com/c/bluebook-for-bulldozers/).\n",
    "\n",
    "## 1. Problem definition\n",
    "\n",
    "Fast Iron company is creating a \"blue book for bulldozers\", to inform their customers about what the company's heavy equipment is worth at auction. They need a model to predict this price, based on usage, equipment type and configuration.\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "As mentioned before, the data was taken from [Kaggle](https://www.kaggle.com/c/bluebook-for-bulldozers/).\n",
    "\n",
    "There are 3 main datasets:\n",
    "\n",
    "- **Train.csv:** Training set, which contains data through the end of 2011.\n",
    "- **Valid.csv:** Validation set, which contains data from January 1, 2012 - April 30, 2012.\n",
    "- **Test.csv:** Test set, which contains data from May 1, 2012 - November 2012.\n",
    "\n",
    "Key fields:\n",
    "\n",
    "- SalesID: Unique identifier of the sale.\n",
    "- MachineID: Unique identifier of a machine. One machine can be sold multiple times.\n",
    "- saleprice: What the machine sold for at auction.\n",
    "- saledate: Date of the sale.\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "The key evaluation metric will be the Root Mean Squared Log Error (RMSLE), between the actual and predicted auction prices. As for most regression models, the goal is to minimize the error of the prediction, when compared to the actual value.\n",
    "\n",
    "## 4. Features and EDA\n",
    "\n",
    "| Variable                 \t| Description                                                                                                                                                                                                                     \t|\n",
    "|--------------------------\t|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\t|\n",
    "| SalesID                  \t|   unique identifier of a particular sale of a machine at auction                                                                                                                                                                \t|\n",
    "| MachineID                \t|   identifier for a particular machine;  machines may have multiple sales                                                                                                                                                        \t|\n",
    "| ModelID                  \t|   identifier for a unique machine model (i.e. fiModelDesc)                                                                                                                                                                      \t|\n",
    "| datasource               \t|   source of the sale record;  some sources are more diligent about reporting attributes of the machine than others.  Note that a particular datasource may report on multiple auctioneerIDs.                                    \t|\n",
    "| auctioneerID             \t|   identifier of a particular auctioneer, i.e. company that sold the machine at auction.  Not the same as datasource.                                                                                                            \t|\n",
    "| YearMade                 \t|   year of manufacturer of the Machine                                                                                                                                                                                           \t|\n",
    "| MachineHoursCurrentMeter \t|   current usage of the machine in hours at time of sale (saledate);  null or 0 means no hours have been reported for that sale                                                                                                  \t|\n",
    "| UsageBand                \t|   value (low, medium, high) calculated comparing this particular Machine-Sale hours to average usage for the fiBaseModel;  e.g. 'Low' means this machine has less hours given it's lifespan relative to average of fiBaseModel. \t|\n",
    "| Saledate                 \t|   time of sale                                                                                                                                                                                                                  \t|\n",
    "| Saleprice                \t|   cost of sale in USD                                                                                                                                                                                                           \t|\n",
    "| fiModelDesc              \t|   Description of a unique machine model (see ModelID); concatenation of fiBaseModel & fiSecondaryDesc & fiModelSeries & fiModelDescriptor                                                                                       \t|\n",
    "| fiBaseModel              \t|   disaggregation of fiModelDesc                                                                                                                                                                                                 \t|\n",
    "| fiSecondaryDesc          \t|   disaggregation of fiModelDesc                                                                                                                                                                                                 \t|\n",
    "| fiModelSeries            \t|   disaggregation of fiModelDesc                                                                                                                                                                                                 \t|\n",
    "| fiModelDescriptor        \t|   disaggregation of fiModelDesc                                                                                                                                                                                                 \t|\n",
    "| ProductSize              \t| The size class grouping for a product group. Subsets within product group.                                                                                                                                                      \t|\n",
    "| ProductClassDesc         \t|   description of 2nd level hierarchical grouping (below ProductGroup) of fiModelDesc                                                                                                                                            \t|\n",
    "| State                    \t|   US State in which sale occurred                                                                                                                                                                                               \t|\n",
    "| ProductGroup             \t|   identifier for top-level hierarchical grouping of fiModelDesc                                                                                                                                                                 \t|\n",
    "| ProductGroupDesc         \t|   description of top-level hierarchical grouping of fiModelDesc                                                                                                                                                                 \t|\n",
    "| Drive_System             \t| machine configuration;  typcially describes whether 2 or 4 wheel drive                                                                                                                                                          \t|\n",
    "| Enclosure                \t| machine configuration - does machine have an enclosed cab or not                                                                                                                                                                \t|\n",
    "| Forks                    \t| machine configuration - attachment used for lifting                                                                                                                                                                             \t|\n",
    "| Pad_Type                 \t| machine configuration - type of treads a crawler machine uses                                                                                                                                                                   \t|\n",
    "| Ride_Control             \t| machine configuration - optional feature on loaders to make the ride smoother                                                                                                                                                   \t|\n",
    "| Stick                    \t| machine configuration - type of control                                                                                                                                                                                         \t|\n",
    "| Transmission             \t| machine configuration - describes type of transmission;  typically automatic or manual                                                                                                                                          \t|\n",
    "| Turbocharged             \t| machine configuration - engine naturally aspirated or turbocharged                                                                                                                                                              \t|\n",
    "| Blade_Extension          \t| machine configuration - extension of standard blade                                                                                                                                                                             \t|\n",
    "| Blade_Width              \t| machine configuration - width of blade                                                                                                                                                                                          \t|\n",
    "| Enclosure_Type           \t| machine configuration - does machine have an enclosed cab or not                                                                                                                                                                \t|\n",
    "| Engine_Horsepower        \t| machine configuration - engine horsepower rating                                                                                                                                                                                \t|\n",
    "| Hydraulics               \t| machine configuration - type of hydraulics                                                                                                                                                                                      \t|\n",
    "| Pushblock                \t| machine configuration - option                                                                                                                                                                                                  \t|\n",
    "| Ripper                   \t| machine configuration - implement attached to machine to till soil                                                                                                                                                              \t|\n",
    "| Scarifier                \t| machine configuration - implement attached to machine to condition soil                                                                                                                                                         \t|\n",
    "| Tip_control              \t| machine configuration - type of blade control                                                                                                                                                                                   \t|\n",
    "| Tire_Size                \t| machine configuration - size of primary tires                                                                                                                                                                                   \t|\n",
    "| Coupler                  \t| machine configuration - type of implement interface                                                                                                                                                                             \t|\n",
    "| Coupler_System           \t| machine configuration - type of implement interface                                                                                                                                                                             \t|\n",
    "| Grouser_Tracks           \t| machine configuration - describes ground contact interface                                                                                                                                                                      \t|\n",
    "| Hydraulics_Flow          \t| machine configuration - normal or high flow hydraulic system                                                                                                                                                                    \t|\n",
    "| Track_Type               \t| machine configuration - type of treads a crawler machine uses                                                                                                                                                                   \t|\n",
    "| Undercarriage_Pad_Width  \t| machine configuration - width of crawler treads                                                                                                                                                                                 \t|\n",
    "| Stick_Length             \t| machine configuration - length of machine digging implement                                                                                                                                                                     \t|\n",
    "| Thumb                    \t| machine configuration - attachment used for grabbing                                                                                                                                                                            \t|\n",
    "| Pattern_Changer          \t| machine configuration - can adjust the operator control configuration to suit the user                                                                                                                                          \t|\n",
    "| Grouser_Type             \t| machine configuration - type of treads a crawler machine uses                                                                                                                                                                   \t|\n",
    "| Backhoe_Mounting         \t| machine configuration - optional interface used to add a backhoe attachment                                                                                                                                                     \t|\n",
    "| Blade_Type               \t| machine configuration - describes type of blade                                                                                                                                                                                 \t|\n",
    "| Travel_Controls          \t| machine configuration - describes operator control configuration                                                                                                                                                                \t|\n",
    "| Differential_Type        \t| machine configuration - differential type, typically locking or standard                                                                                                                                                        \t|\n",
    "| Steering_Controls        \t| machine configuration - describes operator control configuration                                                                                                                                                                \t|\n",
    "\n",
    "### Import tools and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import training and validations sets\n",
    "df = pd.read_csv('data/bluebook-for-bulldozers/TrainAndValid.csv',\n",
    "                 low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   saledate                  412698 non-null  object \n",
      " 10  fiModelDesc               412698 non-null  object \n",
      " 11  fiBaseModel               412698 non-null  object \n",
      " 12  fiSecondaryDesc           271971 non-null  object \n",
      " 13  fiModelSeries             58667 non-null   object \n",
      " 14  fiModelDescriptor         74816 non-null   object \n",
      " 15  ProductSize               196093 non-null  object \n",
      " 16  fiProductClassDesc        412698 non-null  object \n",
      " 17  state                     412698 non-null  object \n",
      " 18  ProductGroup              412698 non-null  object \n",
      " 19  ProductGroupDesc          412698 non-null  object \n",
      " 20  Drive_System              107087 non-null  object \n",
      " 21  Enclosure                 412364 non-null  object \n",
      " 22  Forks                     197715 non-null  object \n",
      " 23  Pad_Type                  81096 non-null   object \n",
      " 24  Ride_Control              152728 non-null  object \n",
      " 25  Stick                     81096 non-null   object \n",
      " 26  Transmission              188007 non-null  object \n",
      " 27  Turbocharged              81096 non-null   object \n",
      " 28  Blade_Extension           25983 non-null   object \n",
      " 29  Blade_Width               25983 non-null   object \n",
      " 30  Enclosure_Type            25983 non-null   object \n",
      " 31  Engine_Horsepower         25983 non-null   object \n",
      " 32  Hydraulics                330133 non-null  object \n",
      " 33  Pushblock                 25983 non-null   object \n",
      " 34  Ripper                    106945 non-null  object \n",
      " 35  Scarifier                 25994 non-null   object \n",
      " 36  Tip_Control               25983 non-null   object \n",
      " 37  Tire_Size                 97638 non-null   object \n",
      " 38  Coupler                   220679 non-null  object \n",
      " 39  Coupler_System            44974 non-null   object \n",
      " 40  Grouser_Tracks            44875 non-null   object \n",
      " 41  Hydraulics_Flow           44875 non-null   object \n",
      " 42  Track_Type                102193 non-null  object \n",
      " 43  Undercarriage_Pad_Width   102916 non-null  object \n",
      " 44  Stick_Length              102261 non-null  object \n",
      " 45  Thumb                     102332 non-null  object \n",
      " 46  Pattern_Changer           102261 non-null  object \n",
      " 47  Grouser_Type              102193 non-null  object \n",
      " 48  Backhoe_Mounting          80712 non-null   object \n",
      " 49  Blade_Type                81875 non-null   object \n",
      " 50  Travel_Controls           81877 non-null   object \n",
      " 51  Differential_Type         71564 non-null   object \n",
      " 52  Steering_Controls         71522 non-null   object \n",
      "dtypes: float64(3), int64(5), object(45)\n",
      "memory usage: 166.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                          0\n",
       "SalePrice                        0\n",
       "MachineID                        0\n",
       "ModelID                          0\n",
       "datasource                       0\n",
       "auctioneerID                 20136\n",
       "YearMade                         0\n",
       "MachineHoursCurrentMeter    265194\n",
       "UsageBand                   339028\n",
       "saledate                         0\n",
       "fiModelDesc                      0\n",
       "fiBaseModel                      0\n",
       "fiSecondaryDesc             140727\n",
       "fiModelSeries               354031\n",
       "fiModelDescriptor           337882\n",
       "ProductSize                 216605\n",
       "fiProductClassDesc               0\n",
       "state                            0\n",
       "ProductGroup                     0\n",
       "ProductGroupDesc                 0\n",
       "Drive_System                305611\n",
       "Enclosure                      334\n",
       "Forks                       214983\n",
       "Pad_Type                    331602\n",
       "Ride_Control                259970\n",
       "Stick                       331602\n",
       "Transmission                224691\n",
       "Turbocharged                331602\n",
       "Blade_Extension             386715\n",
       "Blade_Width                 386715\n",
       "Enclosure_Type              386715\n",
       "Engine_Horsepower           386715\n",
       "Hydraulics                   82565\n",
       "Pushblock                   386715\n",
       "Ripper                      305753\n",
       "Scarifier                   386704\n",
       "Tip_Control                 386715\n",
       "Tire_Size                   315060\n",
       "Coupler                     192019\n",
       "Coupler_System              367724\n",
       "Grouser_Tracks              367823\n",
       "Hydraulics_Flow             367823\n",
       "Track_Type                  310505\n",
       "Undercarriage_Pad_Width     309782\n",
       "Stick_Length                310437\n",
       "Thumb                       310366\n",
       "Pattern_Changer             310437\n",
       "Grouser_Type                310505\n",
       "Backhoe_Mounting            331986\n",
       "Blade_Type                  330823\n",
       "Travel_Controls             330821\n",
       "Differential_Type           341134\n",
       "Steering_Controls           341176\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000]);\n",
    "ax.set(title=\"Sale Price vs Sale Date\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD4CAYAAADRuPC7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaoklEQVR4nO3dcZSV9X3n8fcnTIKaBAUZDZmhHYycpOhJVpkQ0mS7NjRC1Irp0e54mpVNadlatpu025NA7InZ5HAONG1M2K4mNFKBGIXQJLJxXUOwabbnGHCMGkClTIXoBCrThUWaRgzmu388v1ueud4ZLsP87r0zfl7nPOc+z/d5fs/zvaPMd37P73efq4jAzMxstL2m2QmYmdn45AJjZmZZuMCYmVkWLjBmZpaFC4yZmWXR1uwEWsXUqVOjq6ur2WmYmY0pjz766D9FRHutfS4wSVdXF729vc1Ow8xsTJH0o6H2+RaZmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeFP8o+SrmX3N+W6+1de3ZTrmpmdinswZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWWQrMJLWSjokaVeNfX8sKSRNLcWWS+qTtEfS/FJ8tqSdad9qSUrxiZI2pvh2SV2lNosk7U3Lolzv0czMhpazB3MXsKA6KGk68H7g2VJsFtADXJLa3C5pQtp9B7AEmJmWyjkXA0ci4mLgNmBVOtcU4FbgXcAc4FZJk0f5vZmZ2SlkKzAR8T3gcI1dtwEfA6IUWwjcGxHHI2If0AfMkTQNmBQRD0dEAOuB60pt1qX1zcC81LuZD2yNiMMRcQTYSo1CZ2ZmeTV0DEbStcCPI+KJql0dwHOl7f4U60jr1fFBbSLiBHAUOH+Yc9XKZ4mkXkm9AwMDI3pPZmZWW8MKjKRzgFuAT9baXSMWw8RH2mZwMGJNRHRHRHd7e3utQ8zMbIQa2YN5CzADeELSfqAT+IGkN1H0MqaXju0EDqR4Z4045TaS2oBzKW7JDXUuMzNroIYVmIjYGREXRERXRHRRFILLI+IfgS1AT5oZNoNiMH9HRBwEjkmam8ZXbgLuS6fcAlRmiF0PPJTGaR4ErpQ0OQ3uX5liZmbWQNm+MlnSPcAVwFRJ/cCtEXFnrWMjYrekTcCTwAlgaUS8nHbfTDEj7WzggbQA3AlskNRH0XPpSec6LOkzwCPpuE9HRK3JBmZmllG2AhMRN55if1fV9gpgRY3jeoFLa8RfBG4Y4txrgbWnka6ZmY0yf5LfzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyyyFRhJayUdkrSrFPuspKcl/VDSNySdV9q3XFKfpD2S5pfisyXtTPtWS1KKT5S0McW3S+oqtVkkaW9aFuV6j2ZmNrScPZi7gAVVsa3ApRHxduDvgeUAkmYBPcAlqc3tkiakNncAS4CZaamcczFwJCIuBm4DVqVzTQFuBd4FzAFulTQ5w/szM7NhZCswEfE94HBV7NsRcSJtfh/oTOsLgXsj4nhE7AP6gDmSpgGTIuLhiAhgPXBdqc26tL4ZmJd6N/OBrRFxOCKOUBS16kJnZmaZNXMM5reBB9J6B/BcaV9/inWk9er4oDapaB0Fzh/mXK8gaYmkXkm9AwMDZ/RmzMxssKYUGEm3ACeAuyuhGofFMPGRthkcjFgTEd0R0d3e3j580mZmdlraGn3BNOh+DTAv3faCopcxvXRYJ3AgxTtrxMtt+iW1AedS3JLrB66oavPdUX0TLaRr2f1Nu/b+lVc37dpm1voa2oORtAD4OHBtRPxLadcWoCfNDJtBMZi/IyIOAsckzU3jKzcB95XaVGaIXQ88lArWg8CVkianwf0rU8zMzBooWw9G0j0UPYmpkvopZnYtByYCW9Ns4+9HxO9FxG5Jm4AnKW6dLY2Il9OpbqaYkXY2xZhNZdzmTmCDpD6KnksPQEQclvQZ4JF03KcjYtBkAzMzyy9bgYmIG2uE7xzm+BXAihrxXuDSGvEXgRuGONdaYG3dyZqZ2ajzJ/nNzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsshWYCStlXRI0q5SbIqkrZL2ptfJpX3LJfVJ2iNpfik+W9LOtG+1JKX4REkbU3y7pK5Sm0XpGnslLcr1Hs3MbGg5ezB3AQuqYsuAbRExE9iWtpE0C+gBLkltbpc0IbW5A1gCzExL5ZyLgSMRcTFwG7AqnWsKcCvwLmAOcGu5kJmZWWNkKzAR8T3gcFV4IbAura8DrivF742I4xGxD+gD5kiaBkyKiIcjIoD1VW0q59oMzEu9m/nA1og4HBFHgK28stCZmVlmjR6DuTAiDgKk1wtSvAN4rnRcf4p1pPXq+KA2EXECOAqcP8y5XkHSEkm9knoHBgbO4G2ZmVm1VhnkV41YDBMfaZvBwYg1EdEdEd3t7e11JWpmZvVpdIF5Pt32Ir0eSvF+YHrpuE7gQIp31ogPaiOpDTiX4pbcUOcyM7MGanSB2QJUZnUtAu4rxXvSzLAZFIP5O9JttGOS5qbxlZuq2lTOdT3wUBqneRC4UtLkNLh/ZYqZmVkDteU6saR7gCuAqZL6KWZ2rQQ2SVoMPAvcABARuyVtAp4ETgBLI+LldKqbKWaknQ08kBaAO4ENkvooei496VyHJX0GeCQd9+mIqJ5sYGZmmdVVYCRdGhG7Tn3kSRFx4xC75g1x/ApgRY14L3BpjfiLpAJVY99aYG3dyZqZ2air9xbZFyXtkPT7ks7LmZCZmY0PdRWYiHgv8FsUg+e9kr4q6f1ZMzMzszGt7kH+iNgL/AnwceDfAaslPS3pN3IlZ2ZmY1ddBUbS2yXdBjwFvA/49Yj4pbR+W8b8zMxsjKp3FtlfAH8JfCIifloJRsQBSX+SJTMzMxvT6i0wVwE/rUwdlvQa4KyI+JeI2JAtOzMzG7PqHYP5DsXnUCrOSTEzM7Oa6i0wZ0XEP1c20vo5eVIyM7PxoN4C8xNJl1c2JM0GfjrM8WZm9ipX7xjMR4GvSao8NHIa8O+zZGRmZuNCXQUmIh6R9DbgrRSPw386In6WNTMzMxvTTudhl+8EulKbyyQREeuzZGVmZmNevQ+73AC8BXgcqDzluPIVxmZmZq9Qbw+mG5iVvm/FzMzslOqdRbYLeFPORMzMbHyptwczFXhS0g7geCUYEddmycrMzMa8egvMp3ImYWZm40+905T/VtIvAjMj4juSzgEm5E3NzMzGsnof1/+7wGbgSynUAXxzpBeV9IeSdkvaJekeSWdJmiJpq6S96XVy6fjlkvok7ZE0vxSfLWln2rdaklJ8oqSNKb5dUtdIczUzs5Gpd5B/KfAe4AX41y8fu2AkF5TUAfwXoDsiLqXoCfUAy4BtETET2Ja2kTQr7b8EWADcLqnSe7oDWALMTMuCFF8MHImIiym+r2bVSHI1M7ORq7fAHI+IlyobktooPgczUm3A2ek85wAHgIXAurR/HXBdWl8I3BsRxyNiH9AHzJE0DZgUEQ+n6dPrq9pUzrUZmFfp3ZiZWWPUW2D+VtInKIrC+4GvAf9zJBeMiB8DfwY8CxwEjkbEt4ELI+JgOuYgJ3tIHcBzpVP0p1hHWq+OD2oTESeAo8D51blIWiKpV1LvwMDASN6OmZkNod4CswwYAHYC/wn4X8CIvskyja0sBGYAbwZeL+lDwzWpEYth4sO1GRyIWBMR3RHR3d7ePnziZmZ2WuqdRfZziq9M/stRuOavAfsiYgBA0teBXwaelzQtIg6m21+H0vH9wPRS+06KW2r9ab06Xm7Tn27DnQscHoXczcysTvXOItsn6ZnqZYTXfBaYK+mcNC4yD3gK2AIsSscsAu5L61uAnjQzbAbFYP6OdBvtmKS56Tw3VbWpnOt64CE/5sbMrLFO51lkFWcBNwBTRnLBiNguaTPwA+AE8BiwBngDsEnSYooidEM6frekTcCT6filEVF54ObNwF0UX+f8QFoA7gQ2SOqj6Ln0jCRXMzMbOY30D3tJfxcR7x3lfJqmu7s7ent7R9y+a9n9o5jN2LB/5dXNTsHMmkzSoxHRXWtfvY/rv7y0+RqKHs0bRyE3MzMbp+q9RfbnpfUTwH7gN0c9GzMzGzfqnUX2q7kTMTOz8aXeW2R/NNz+iPjc6KRjZmbjxenMInsnxfRfgF8HvsfgT9ibmZn9q9P5wrHLI+IYgKRPAV+LiN/JlZiZmY1t9T4q5heAl0rbLwFdo56NmZmNG/X2YDYAOyR9g+KZXh+keHqxmZlZTfXOIlsh6QHg36bQhyPisXxpmZnZWFfvLTIovrflhYj4AsVDJGdkysnMzMaBeh92eSvwcWB5Cr0W+EqupMzMbOyrtwfzQeBa4CcAEXEAPyrGzMyGUW+BeSk97j4AJL0+X0pmZjYe1FtgNkn6EnCepN8FvsPofPmYmZmNU6ecRZa+zGsj8DbgBeCtwCcjYmvm3MzMbAw7ZYGJiJD0zYiYDbiomJlZXeq9RfZ9Se/MmomZmY0r9X6S/1eB35O0n2ImmSg6N2/PlZiZmY1tw/ZgJP1CWv0AcBHwPoonKV+TXkdE0nmSNkt6WtJTkt4taYqkrZL2ptfJpeOXS+qTtEfS/FJ8tqSdad/qNF6EpImSNqb4dkldI83VzMxG5lS3yL4JEBE/Aj4XET8qL2dw3S8A/zsi3ga8A3gKWAZsi4iZwLa0jaRZQA9wCbAAuF3ShHSeO4AlwMy0LEjxxcCRiLgYuA1YdQa5mpnZCJyqwKi0ftFoXFDSJOBXgDsBIuKliPh/wEJgXTpsHXBdWl8I3BsRxyNiH9AHzJE0DZgUEQ+nz+isr2pTOddmYF6ld2NmZo1xqgITQ6yfiYuAAeCvJD0m6cvpg5sXRsRBgPR6QTq+g8FfbNafYh1pvTo+qE1EnACOAuePUv5mZlaHUxWYd0h6QdIx4O1p/QVJxyS9MMJrtgGXA3dExGUUkwaWDXN8rZ5HDBMfrs3gE0tLJPVK6h0YGBg+azMzOy3DFpiImBARkyLijRHRltYr25NGeM1+oD8itqftzRQF5/l024v0eqh0/PRS+07gQIp31ogPaiOpDTgXOFzj/a2JiO6I6G5vbx/h2zEzs1rqnaY8aiLiHyU9J+mtEbEHmAc8mZZFwMr0el9qsgX4qqTPAW+mGMzfEREvp57UXGA7cBPw30ttFgEPA9cDD6VxGhtFXcvub8p196+8uinXNbPT0/ACk/wBcLek1wHPAB+m6E1tkrQYeBa4ASAidkvaRFGATgBLI+LldJ6bgbuAs4EH0gLFBIINkvooei49jXhTZmZ2UlMKTEQ8DnTX2DVviONXACtqxHuBS2vEXyQVKDMza47T+UZLMzOzurnAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpZF0wqMpAmSHpP0rbQ9RdJWSXvT6+TSscsl9UnaI2l+KT5b0s60b7UkpfhESRtTfLukroa/QTOzV7lm9mA+AjxV2l4GbIuImcC2tI2kWUAPcAmwALhd0oTU5g5gCTAzLQtSfDFwJCIuBm4DVuV9K2ZmVq0pBUZSJ3A18OVSeCGwLq2vA64rxe+NiOMRsQ/oA+ZImgZMioiHIyKA9VVtKufaDMyr9G7MzKwxmtWD+TzwMeDnpdiFEXEQIL1ekOIdwHOl4/pTrCOtV8cHtYmIE8BR4PzqJCQtkdQrqXdgYOAM35KZmZU1vMBIugY4FBGP1tukRiyGiQ/XZnAgYk1EdEdEd3t7e53pmJlZPdqacM33ANdKugo4C5gk6SvA85KmRcTBdPvrUDq+H5heat8JHEjxzhrxcpt+SW3AucDhXG/IzMxeqeE9mIhYHhGdEdFFMXj/UER8CNgCLEqHLQLuS+tbgJ40M2wGxWD+jnQb7ZikuWl85aaqNpVzXZ+u8YoejJmZ5dOMHsxQVgKbJC0GngVuAIiI3ZI2AU8CJ4ClEfFyanMzcBdwNvBAWgDuBDZI6qPoufQ06k2YmVmhqQUmIr4LfDet/19g3hDHrQBW1Ij3ApfWiL9IKlBmZtYc/iS/mZll4QJjZmZZuMCYmVkWLjBmZpZFK80iM6tL17L7m3Ld/Suvbsp1zcYq92DMzCwLFxgzM8vCBcbMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7Ms/LBLszo16yGb4Adt2tjU8B6MpOmS/kbSU5J2S/pIik+RtFXS3vQ6udRmuaQ+SXskzS/FZ0vamfatlqQUnyhpY4pvl9TV6PdpZvZq14xbZCeA/xoRvwTMBZZKmgUsA7ZFxExgW9om7esBLgEWALdLmpDOdQewBJiZlgUpvhg4EhEXA7cBqxrxxszM7KSGF5iIOBgRP0jrx4CngA5gIbAuHbYOuC6tLwTujYjjEbEP6APmSJoGTIqIhyMigPVVbSrn2gzMq/RuzMysMZo6yJ9uXV0GbAcujIiDUBQh4IJ0WAfwXKlZf4p1pPXq+KA2EXECOAqcX+P6SyT1SuodGBgYpXdlZmbQxAIj6Q3AXwMfjYgXhju0RiyGiQ/XZnAgYk1EdEdEd3t7+6lSNjOz09CUAiPptRTF5e6I+HoKP59ue5FeD6V4PzC91LwTOJDinTXig9pIagPOBQ6P/jsxM7OhNGMWmYA7gaci4nOlXVuARWl9EXBfKd6TZobNoBjM35Fuox2TNDed86aqNpVzXQ88lMZpzMysQZrxOZj3AP8B2Cnp8RT7BLAS2CRpMfAscANAROyWtAl4kmIG2tKIeDm1uxm4CzgbeCAtUBSwDZL6KHouPZnfk5mZVWl4gYmIv6P2GAnAvCHarABW1Ij3ApfWiL9IKlBmZtYcflSMmZll4QJjZmZZuMCYmVkWftil2RjQrAdt+iGbdibcgzEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLDyLzMyG5K+JtjPhHoyZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWXiaspm1JD/gc+xzD8bMzLJwgTEzsyzG9S0ySQuALwATgC9HxMomp2RmLc5PLxg947YHI2kC8D+ADwCzgBslzWpuVmZmrx7juQczB+iLiGcAJN0LLASebGpWZmZDGG8TG8ZzgekAnitt9wPvKh8gaQmwJG3+s6Q9VeeYCvxTtgxH31jLF8ZezmMtXxh7OTvf/AblrFVndK5fHGrHeC4wqhGLQRsRa4A1Q55A6o2I7tFOLJexli+MvZzHWr4w9nJ2vvk1KudxOwZD0WOZXtruBA40KRczs1ed8VxgHgFmSpoh6XVAD7ClyTmZmb1qjNtbZBFxQtJ/Bh6kmKa8NiJ2n+Zphrx91qLGWr4w9nIea/nC2MvZ+ebXkJwVEac+yszM7DSN51tkZmbWRC4wZmaWhQvMECQtkLRHUp+kZQ287nRJfyPpKUm7JX0kxadI2ippb3qdXGqzPOW5R9L8Uny2pJ1p32pJSvGJkjam+HZJXaOQ9wRJj0n61hjJ9zxJmyU9nX7W727lnCX9Yfr/YZekeySd1Wr5Slor6ZCkXaVYQ3KUtChdY6+kRWeQ72fT/xM/lPQNSee1Sr5D5Vza98eSQtLUlsk5IrxULRSTAv4BuAh4HfAEMKtB154GXJ7W3wj8PcWjbv4UWJbiy4BVaX1Wym8iMCPlPSHt2wG8m+IzQQ8AH0jx3we+mNZ7gI2jkPcfAV8FvpW2Wz3fdcDvpPXXAee1as4UHxreB5ydtjcB/7HV8gV+Bbgc2FWKZc8RmAI8k14np/XJI8z3SqAtra9qpXyHyjnFp1NMaPoRMLVVcs7+C3MsLukH/2BpezmwvEm53Ae8H9gDTEuxacCeWrml/8nenY55uhS/EfhS+Zi03kbxiV6dQY6dwDbgfZwsMK2c7ySKX9iqirdkzpx8KsWUdK5vUfwibLl8gS4G/8LOnmP5mLTvS8CNI8m3at8HgbtbKd+hcgY2A+8A9nOywDQ9Z98iq63WY2Y6Gp1E6p5eBmwHLoyIgwDp9YJ02FC5dqT16vigNhFxAjgKnH8GqX4e+Bjw81KslfO9CBgA/krFbb0vS3p9q+YcET8G/gx4FjgIHI2Ib7dqvlUakWOuf6+/TfHXfUvnK+la4McR8UTVrqbn7AJT2ykfM5M9AekNwF8DH42IF4Y7tEYshokP1+a0SboGOBQRj9bbZIhrNyTfpI3iNsMdEXEZ8BOK2zdDafbPeDLFg1pnAG8GXi/pQ8M1GeLajfwZn8po5jjquUu6BTgB3H0G186er6RzgFuAT9baPYLrj2rOLjC1NfUxM5JeS1Fc7o6Ir6fw85Kmpf3TgEOnyLU/rVfHB7WR1AacCxweYbrvAa6VtB+4F3ifpK+0cL6V8/VHxPa0vZmi4LRqzr8G7IuIgYj4GfB14JdbON+yRuQ4qv9e0wD2NcBvRbof1ML5voXiD48n0r/BTuAHkt7UEjmP5J7weF8o/sJ9Jv2HqwzyX9KgawtYD3y+Kv5ZBg+W/mlav4TBA3nPcHIg7xFgLicH8q5K8aUMHsjbNEq5X8HJMZiWzhf4P8Bb0/qnUr4tmTPFU8B3A+ek66wD/qAV8+WVYzDZc6QYm9pHMfg8Oa1PGWG+Cyi+0qO96riWyLdWzlX79nNyDKbpOWf/hTlWF+Aqihlc/wDc0sDrvpei6/lD4PG0XEVxH3QbsDe9Tim1uSXluYc0GyTFu4Fdad9fcPLJDWcBXwP6KGaTXDRKuV/ByQLT0vkC/wboTT/nb6Z/NC2bM/DfgKfTtTakXxotlS9wD8UY0c8o/uJd3KgcKcZL+tLy4TPIt49irOHxtHyxVfIdKueq/ftJBaYVcvajYszMLAuPwZiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll8f8Bis5vTEC2MfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SalePrice'].plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      11/16/2006 0:00\n",
       "1       3/26/2004 0:00\n",
       "2       2/26/2004 0:00\n",
       "3       5/19/2011 0:00\n",
       "4       7/23/2009 0:00\n",
       "            ...       \n",
       "995     7/16/2009 0:00\n",
       "996     6/14/2007 0:00\n",
       "997     9/22/2005 0:00\n",
       "998     7/28/2005 0:00\n",
       "999     6/16/2011 0:00\n",
       "Name: saledate, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['saledate'][:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing dates\n",
    "\n",
    "For time series data, we need to convert the dates from type Object to Date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2006-11-16\n",
       "1     2004-03-26\n",
       "2     2004-02-26\n",
       "3     2011-05-19\n",
       "4     2009-07-23\n",
       "         ...    \n",
       "995   2009-07-16\n",
       "996   2007-06-14\n",
       "997   2005-09-22\n",
       "998   2005-07-28\n",
       "999   2011-06-16\n",
       "Name: saledate, Length: 1000, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data again, but parsign dates\n",
    "df = pd.read_csv('data/bluebook-for-bulldozers/TrainAndValid.csv',\n",
    "                 low_memory=False,\n",
    "                 parse_dates=['saledate'])\n",
    "df.saledate[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If we plot again, data will be clearer\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000]);\n",
    "ax.set(title=\"Sale Price vs Sale Date\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1139246</td>\n",
       "      <td>1139248</td>\n",
       "      <td>1139249</td>\n",
       "      <td>1139251</td>\n",
       "      <td>1139253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>66000</td>\n",
       "      <td>57000</td>\n",
       "      <td>10000</td>\n",
       "      <td>38500</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>999089</td>\n",
       "      <td>117657</td>\n",
       "      <td>434808</td>\n",
       "      <td>1026470</td>\n",
       "      <td>1057373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>3157</td>\n",
       "      <td>77</td>\n",
       "      <td>7009</td>\n",
       "      <td>332</td>\n",
       "      <td>17311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>2004</td>\n",
       "      <td>1996</td>\n",
       "      <td>2001</td>\n",
       "      <td>2001</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <td>68</td>\n",
       "      <td>4640</td>\n",
       "      <td>2838</td>\n",
       "      <td>3486</td>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UsageBand</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saledate</th>\n",
       "      <td>2006-11-16 00:00:00</td>\n",
       "      <td>2004-03-26 00:00:00</td>\n",
       "      <td>2004-02-26 00:00:00</td>\n",
       "      <td>2011-05-19 00:00:00</td>\n",
       "      <td>2009-07-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDesc</th>\n",
       "      <td>521D</td>\n",
       "      <td>950FII</td>\n",
       "      <td>226</td>\n",
       "      <td>PC120-6E</td>\n",
       "      <td>S175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>521</td>\n",
       "      <td>950</td>\n",
       "      <td>226</td>\n",
       "      <td>PC120</td>\n",
       "      <td>S175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>D</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelSeries</th>\n",
       "      <td>NaN</td>\n",
       "      <td>II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6E</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDescriptor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductSize</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Wheel Loader - 110.0 to 120.0 Horsepower</td>\n",
       "      <td>Wheel Loader - 150.0 to 175.0 Horsepower</td>\n",
       "      <td>Skid Steer Loader - 1351.0 to 1601.0 Lb Operat...</td>\n",
       "      <td>Hydraulic Excavator, Track - 12.0 to 14.0 Metr...</td>\n",
       "      <td>Skid Steer Loader - 1601.0 to 1751.0 Lb Operat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>New York</td>\n",
       "      <td>Texas</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>WL</td>\n",
       "      <td>WL</td>\n",
       "      <td>SSL</td>\n",
       "      <td>TEX</td>\n",
       "      <td>SSL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Skid Steer Loaders</td>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>Skid Steer Loaders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drive_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>EROPS w AC</td>\n",
       "      <td>EROPS w AC</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS w AC</td>\n",
       "      <td>EROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pad_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ride_Control</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turbocharged</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Extension</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine_Horsepower</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics</th>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>Auxiliary</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>Auxiliary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pushblock</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ripper</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarifier</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tip_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tire_Size</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>23.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Tracks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics_Flow</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Track_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick_Length</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thumb</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>Standard</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls</th>\n",
       "      <td>Conventional</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 0  \\\n",
       "SalesID                                                    1139246   \n",
       "SalePrice                                                    66000   \n",
       "MachineID                                                   999089   \n",
       "ModelID                                                       3157   \n",
       "datasource                                                     121   \n",
       "auctioneerID                                                     3   \n",
       "YearMade                                                      2004   \n",
       "MachineHoursCurrentMeter                                        68   \n",
       "UsageBand                                                      Low   \n",
       "saledate                                       2006-11-16 00:00:00   \n",
       "fiModelDesc                                                   521D   \n",
       "fiBaseModel                                                    521   \n",
       "fiSecondaryDesc                                                  D   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                    NaN   \n",
       "fiProductClassDesc        Wheel Loader - 110.0 to 120.0 Horsepower   \n",
       "state                                                      Alabama   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                               EROPS w AC   \n",
       "Forks                                          None or Unspecified   \n",
       "Pad_Type                                                       NaN   \n",
       "Ride_Control                                   None or Unspecified   \n",
       "Stick                                                          NaN   \n",
       "Transmission                                                   NaN   \n",
       "Turbocharged                                                   NaN   \n",
       "Blade_Extension                                                NaN   \n",
       "Blade_Width                                                    NaN   \n",
       "Enclosure_Type                                                 NaN   \n",
       "Engine_Horsepower                                              NaN   \n",
       "Hydraulics                                                 2 Valve   \n",
       "Pushblock                                                      NaN   \n",
       "Ripper                                                         NaN   \n",
       "Scarifier                                                      NaN   \n",
       "Tip_Control                                                    NaN   \n",
       "Tire_Size                                      None or Unspecified   \n",
       "Coupler                                        None or Unspecified   \n",
       "Coupler_System                                                 NaN   \n",
       "Grouser_Tracks                                                 NaN   \n",
       "Hydraulics_Flow                                                NaN   \n",
       "Track_Type                                                     NaN   \n",
       "Undercarriage_Pad_Width                                        NaN   \n",
       "Stick_Length                                                   NaN   \n",
       "Thumb                                                          NaN   \n",
       "Pattern_Changer                                                NaN   \n",
       "Grouser_Type                                                   NaN   \n",
       "Backhoe_Mounting                                               NaN   \n",
       "Blade_Type                                                     NaN   \n",
       "Travel_Controls                                                NaN   \n",
       "Differential_Type                                         Standard   \n",
       "Steering_Controls                                     Conventional   \n",
       "\n",
       "                                                                 1  \\\n",
       "SalesID                                                    1139248   \n",
       "SalePrice                                                    57000   \n",
       "MachineID                                                   117657   \n",
       "ModelID                                                         77   \n",
       "datasource                                                     121   \n",
       "auctioneerID                                                     3   \n",
       "YearMade                                                      1996   \n",
       "MachineHoursCurrentMeter                                      4640   \n",
       "UsageBand                                                      Low   \n",
       "saledate                                       2004-03-26 00:00:00   \n",
       "fiModelDesc                                                 950FII   \n",
       "fiBaseModel                                                    950   \n",
       "fiSecondaryDesc                                                  F   \n",
       "fiModelSeries                                                   II   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                 Medium   \n",
       "fiProductClassDesc        Wheel Loader - 150.0 to 175.0 Horsepower   \n",
       "state                                               North Carolina   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                               EROPS w AC   \n",
       "Forks                                          None or Unspecified   \n",
       "Pad_Type                                                       NaN   \n",
       "Ride_Control                                   None or Unspecified   \n",
       "Stick                                                          NaN   \n",
       "Transmission                                                   NaN   \n",
       "Turbocharged                                                   NaN   \n",
       "Blade_Extension                                                NaN   \n",
       "Blade_Width                                                    NaN   \n",
       "Enclosure_Type                                                 NaN   \n",
       "Engine_Horsepower                                              NaN   \n",
       "Hydraulics                                                 2 Valve   \n",
       "Pushblock                                                      NaN   \n",
       "Ripper                                                         NaN   \n",
       "Scarifier                                                      NaN   \n",
       "Tip_Control                                                    NaN   \n",
       "Tire_Size                                                     23.5   \n",
       "Coupler                                        None or Unspecified   \n",
       "Coupler_System                                                 NaN   \n",
       "Grouser_Tracks                                                 NaN   \n",
       "Hydraulics_Flow                                                NaN   \n",
       "Track_Type                                                     NaN   \n",
       "Undercarriage_Pad_Width                                        NaN   \n",
       "Stick_Length                                                   NaN   \n",
       "Thumb                                                          NaN   \n",
       "Pattern_Changer                                                NaN   \n",
       "Grouser_Type                                                   NaN   \n",
       "Backhoe_Mounting                                               NaN   \n",
       "Blade_Type                                                     NaN   \n",
       "Travel_Controls                                                NaN   \n",
       "Differential_Type                                         Standard   \n",
       "Steering_Controls                                     Conventional   \n",
       "\n",
       "                                                                          2  \\\n",
       "SalesID                                                             1139249   \n",
       "SalePrice                                                             10000   \n",
       "MachineID                                                            434808   \n",
       "ModelID                                                                7009   \n",
       "datasource                                                              121   \n",
       "auctioneerID                                                              3   \n",
       "YearMade                                                               2001   \n",
       "MachineHoursCurrentMeter                                               2838   \n",
       "UsageBand                                                              High   \n",
       "saledate                                                2004-02-26 00:00:00   \n",
       "fiModelDesc                                                             226   \n",
       "fiBaseModel                                                             226   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                             NaN   \n",
       "fiProductClassDesc        Skid Steer Loader - 1351.0 to 1601.0 Lb Operat...   \n",
       "state                                                              New York   \n",
       "ProductGroup                                                            SSL   \n",
       "ProductGroupDesc                                         Skid Steer Loaders   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                   None or Unspecified   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                            NaN   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                        Auxiliary   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                                  NaN   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                 None or Unspecified   \n",
       "Coupler_System                                          None or Unspecified   \n",
       "Grouser_Tracks                                          None or Unspecified   \n",
       "Hydraulics_Flow                                                    Standard   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "\n",
       "                                                                          3  \\\n",
       "SalesID                                                             1139251   \n",
       "SalePrice                                                             38500   \n",
       "MachineID                                                           1026470   \n",
       "ModelID                                                                 332   \n",
       "datasource                                                              121   \n",
       "auctioneerID                                                              3   \n",
       "YearMade                                                               2001   \n",
       "MachineHoursCurrentMeter                                               3486   \n",
       "UsageBand                                                              High   \n",
       "saledate                                                2011-05-19 00:00:00   \n",
       "fiModelDesc                                                        PC120-6E   \n",
       "fiBaseModel                                                           PC120   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           -6E   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                           Small   \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 12.0 to 14.0 Metr...   \n",
       "state                                                                 Texas   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                        EROPS w AC   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                            NaN   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                                  NaN   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                 None or Unspecified   \n",
       "Coupler_System                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "\n",
       "                                                                          4  \n",
       "SalesID                                                             1139253  \n",
       "SalePrice                                                             11000  \n",
       "MachineID                                                           1057373  \n",
       "ModelID                                                               17311  \n",
       "datasource                                                              121  \n",
       "auctioneerID                                                              3  \n",
       "YearMade                                                               2007  \n",
       "MachineHoursCurrentMeter                                                722  \n",
       "UsageBand                                                            Medium  \n",
       "saledate                                                2009-07-23 00:00:00  \n",
       "fiModelDesc                                                            S175  \n",
       "fiBaseModel                                                            S175  \n",
       "fiSecondaryDesc                                                         NaN  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Skid Steer Loader - 1601.0 to 1751.0 Lb Operat...  \n",
       "state                                                              New York  \n",
       "ProductGroup                                                            SSL  \n",
       "ProductGroupDesc                                         Skid Steer Loaders  \n",
       "Drive_System                                                            NaN  \n",
       "Enclosure                                                             EROPS  \n",
       "Forks                                                   None or Unspecified  \n",
       "Pad_Type                                                                NaN  \n",
       "Ride_Control                                                            NaN  \n",
       "Stick                                                                   NaN  \n",
       "Transmission                                                            NaN  \n",
       "Turbocharged                                                            NaN  \n",
       "Blade_Extension                                                         NaN  \n",
       "Blade_Width                                                             NaN  \n",
       "Enclosure_Type                                                          NaN  \n",
       "Engine_Horsepower                                                       NaN  \n",
       "Hydraulics                                                        Auxiliary  \n",
       "Pushblock                                                               NaN  \n",
       "Ripper                                                                  NaN  \n",
       "Scarifier                                                               NaN  \n",
       "Tip_Control                                                             NaN  \n",
       "Tire_Size                                                               NaN  \n",
       "Coupler                                                 None or Unspecified  \n",
       "Coupler_System                                          None or Unspecified  \n",
       "Grouser_Tracks                                          None or Unspecified  \n",
       "Hydraulics_Flow                                                    Standard  \n",
       "Track_Type                                                              NaN  \n",
       "Undercarriage_Pad_Width                                                 NaN  \n",
       "Stick_Length                                                            NaN  \n",
       "Thumb                                                                   NaN  \n",
       "Pattern_Changer                                                         NaN  \n",
       "Grouser_Type                                                            NaN  \n",
       "Backhoe_Mounting                                                        NaN  \n",
       "Blade_Type                                                              NaN  \n",
       "Travel_Controls                                                         NaN  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T # Transpose so we can see all features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2006-11-16\n",
       "1    2004-03-26\n",
       "2    2004-02-26\n",
       "3    2011-05-19\n",
       "4    2009-07-23\n",
       "5    2008-12-18\n",
       "6    2004-08-26\n",
       "7    2005-11-17\n",
       "8    2009-08-27\n",
       "9    2007-08-09\n",
       "10   2008-08-21\n",
       "11   2006-08-24\n",
       "12   2005-10-20\n",
       "13   2006-01-26\n",
       "14   2006-01-03\n",
       "15   2006-11-16\n",
       "16   2007-06-14\n",
       "17   2010-01-28\n",
       "18   2006-03-09\n",
       "19   2005-11-17\n",
       "Name: saledate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['saledate'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort Dataframe by saledate\n",
    "\n",
    "Since we're working with time series data, let's sort by sale date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['saledate'], inplace=True, ascending=True)\n",
    "df['saledate'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a copy of the original DataFrame\n",
    "\n",
    "Let's keep the original dataset and work over an exact copy of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add datetime parameters for `saledate` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp['saleYear'] = df_tmp['saledate'].dt.year\n",
    "df_tmp['saleMonth'] = df_tmp['saledate'].dt.month\n",
    "df_tmp['saleDay'] = df_tmp['saledate'].dt.day\n",
    "df_tmp['saleDayOfWeek'] = df_tmp['saledate'].dt.dayofweek\n",
    "df_tmp['saleDayOfYear'] = df_tmp['saledate'].dt.dayofyear\n",
    "\n",
    "# We can remove saledate\n",
    "df_tmp.drop('saledate', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>205615</th>\n",
       "      <th>274835</th>\n",
       "      <th>141296</th>\n",
       "      <th>212552</th>\n",
       "      <th>62755</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1646770</td>\n",
       "      <td>1821514</td>\n",
       "      <td>1505138</td>\n",
       "      <td>1671174</td>\n",
       "      <td>1329056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>9500</td>\n",
       "      <td>14000</td>\n",
       "      <td>50000</td>\n",
       "      <td>16000</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1126363</td>\n",
       "      <td>1194089</td>\n",
       "      <td>1473654</td>\n",
       "      <td>1327630</td>\n",
       "      <td>1336053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>8434</td>\n",
       "      <td>10150</td>\n",
       "      <td>4139</td>\n",
       "      <td>8591</td>\n",
       "      <td>4089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>18</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>1974</td>\n",
       "      <td>1980</td>\n",
       "      <td>1978</td>\n",
       "      <td>1980</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UsageBand</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDesc</th>\n",
       "      <td>TD20</td>\n",
       "      <td>A66</td>\n",
       "      <td>D7G</td>\n",
       "      <td>A62</td>\n",
       "      <td>D3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>TD20</td>\n",
       "      <td>A66</td>\n",
       "      <td>D7</td>\n",
       "      <td>A62</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelSeries</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDescriptor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductSize</th>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Track Type Tractor, Dozer - 105.0 to 130.0 Hor...</td>\n",
       "      <td>Wheel Loader - 120.0 to 135.0 Horsepower</td>\n",
       "      <td>Track Type Tractor, Dozer - 190.0 to 260.0 Hor...</td>\n",
       "      <td>Wheel Loader - Unidentified</td>\n",
       "      <td>Track Type Tractor, Dozer - 20.0 to 75.0 Horse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drive_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>OROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pad_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ride_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <td>Direct Drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turbocharged</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Extension</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine_Horsepower</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics</th>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pushblock</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ripper</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarifier</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tip_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tire_Size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Tracks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics_Flow</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Track_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick_Length</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thumb</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type</th>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleYear</th>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDay</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfYear</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     205615  \\\n",
       "SalesID                                                             1646770   \n",
       "SalePrice                                                              9500   \n",
       "MachineID                                                           1126363   \n",
       "ModelID                                                                8434   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             18   \n",
       "YearMade                                                               1974   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "fiModelDesc                                                            TD20   \n",
       "fiBaseModel                                                            TD20   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                          Medium   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 105.0 to 130.0 Hor...   \n",
       "state                                                                 Texas   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                   Direct Drive   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \n",
       "Coupler_System                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  17   \n",
       "saleDayOfWeek                                                             1   \n",
       "saleDayOfYear                                                            17   \n",
       "\n",
       "                                                            274835  \\\n",
       "SalesID                                                    1821514   \n",
       "SalePrice                                                    14000   \n",
       "MachineID                                                  1194089   \n",
       "ModelID                                                      10150   \n",
       "datasource                                                     132   \n",
       "auctioneerID                                                    99   \n",
       "YearMade                                                      1980   \n",
       "MachineHoursCurrentMeter                                       NaN   \n",
       "UsageBand                                                      NaN   \n",
       "fiModelDesc                                                    A66   \n",
       "fiBaseModel                                                    A66   \n",
       "fiSecondaryDesc                                                NaN   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                    NaN   \n",
       "fiProductClassDesc        Wheel Loader - 120.0 to 135.0 Horsepower   \n",
       "state                                                      Florida   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                                    OROPS   \n",
       "Forks                                          None or Unspecified   \n",
       "Pad_Type                                                       NaN   \n",
       "Ride_Control                                   None or Unspecified   \n",
       "Stick                                                          NaN   \n",
       "Transmission                                                   NaN   \n",
       "Turbocharged                                                   NaN   \n",
       "Blade_Extension                                                NaN   \n",
       "Blade_Width                                                    NaN   \n",
       "Enclosure_Type                                                 NaN   \n",
       "Engine_Horsepower                                              NaN   \n",
       "Hydraulics                                                 2 Valve   \n",
       "Pushblock                                                      NaN   \n",
       "Ripper                                                         NaN   \n",
       "Scarifier                                                      NaN   \n",
       "Tip_Control                                                    NaN   \n",
       "Tire_Size                                      None or Unspecified   \n",
       "Coupler                                        None or Unspecified   \n",
       "Coupler_System                                                 NaN   \n",
       "Grouser_Tracks                                                 NaN   \n",
       "Hydraulics_Flow                                                NaN   \n",
       "Track_Type                                                     NaN   \n",
       "Undercarriage_Pad_Width                                        NaN   \n",
       "Stick_Length                                                   NaN   \n",
       "Thumb                                                          NaN   \n",
       "Pattern_Changer                                                NaN   \n",
       "Grouser_Type                                                   NaN   \n",
       "Backhoe_Mounting                                               NaN   \n",
       "Blade_Type                                                     NaN   \n",
       "Travel_Controls                                                NaN   \n",
       "Differential_Type                                         Standard   \n",
       "Steering_Controls                                     Conventional   \n",
       "saleYear                                                      1989   \n",
       "saleMonth                                                        1   \n",
       "saleDay                                                         31   \n",
       "saleDayOfWeek                                                    1   \n",
       "saleDayOfYear                                                   31   \n",
       "\n",
       "                                                                     141296  \\\n",
       "SalesID                                                             1505138   \n",
       "SalePrice                                                             50000   \n",
       "MachineID                                                           1473654   \n",
       "ModelID                                                                4139   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             99   \n",
       "YearMade                                                               1978   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "fiModelDesc                                                             D7G   \n",
       "fiBaseModel                                                              D7   \n",
       "fiSecondaryDesc                                                           G   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                           Large   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 190.0 to 260.0 Hor...   \n",
       "state                                                               Florida   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                       Standard   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \n",
       "Coupler_System                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  31   \n",
       "saleDayOfWeek                                                             1   \n",
       "saleDayOfYear                                                            31   \n",
       "\n",
       "                                               212552  \\\n",
       "SalesID                                       1671174   \n",
       "SalePrice                                       16000   \n",
       "MachineID                                     1327630   \n",
       "ModelID                                          8591   \n",
       "datasource                                        132   \n",
       "auctioneerID                                       99   \n",
       "YearMade                                         1980   \n",
       "MachineHoursCurrentMeter                          NaN   \n",
       "UsageBand                                         NaN   \n",
       "fiModelDesc                                       A62   \n",
       "fiBaseModel                                       A62   \n",
       "fiSecondaryDesc                                   NaN   \n",
       "fiModelSeries                                     NaN   \n",
       "fiModelDescriptor                                 NaN   \n",
       "ProductSize                                       NaN   \n",
       "fiProductClassDesc        Wheel Loader - Unidentified   \n",
       "state                                         Florida   \n",
       "ProductGroup                                       WL   \n",
       "ProductGroupDesc                         Wheel Loader   \n",
       "Drive_System                                      NaN   \n",
       "Enclosure                                       EROPS   \n",
       "Forks                             None or Unspecified   \n",
       "Pad_Type                                          NaN   \n",
       "Ride_Control                      None or Unspecified   \n",
       "Stick                                             NaN   \n",
       "Transmission                                      NaN   \n",
       "Turbocharged                                      NaN   \n",
       "Blade_Extension                                   NaN   \n",
       "Blade_Width                                       NaN   \n",
       "Enclosure_Type                                    NaN   \n",
       "Engine_Horsepower                                 NaN   \n",
       "Hydraulics                                    2 Valve   \n",
       "Pushblock                                         NaN   \n",
       "Ripper                                            NaN   \n",
       "Scarifier                                         NaN   \n",
       "Tip_Control                                       NaN   \n",
       "Tire_Size                         None or Unspecified   \n",
       "Coupler                           None or Unspecified   \n",
       "Coupler_System                                    NaN   \n",
       "Grouser_Tracks                                    NaN   \n",
       "Hydraulics_Flow                                   NaN   \n",
       "Track_Type                                        NaN   \n",
       "Undercarriage_Pad_Width                           NaN   \n",
       "Stick_Length                                      NaN   \n",
       "Thumb                                             NaN   \n",
       "Pattern_Changer                                   NaN   \n",
       "Grouser_Type                                      NaN   \n",
       "Backhoe_Mounting                                  NaN   \n",
       "Blade_Type                                        NaN   \n",
       "Travel_Controls                                   NaN   \n",
       "Differential_Type                            Standard   \n",
       "Steering_Controls                        Conventional   \n",
       "saleYear                                         1989   \n",
       "saleMonth                                           1   \n",
       "saleDay                                            31   \n",
       "saleDayOfWeek                                       1   \n",
       "saleDayOfYear                                      31   \n",
       "\n",
       "                                                                     62755   \n",
       "SalesID                                                             1329056  \n",
       "SalePrice                                                             22000  \n",
       "MachineID                                                           1336053  \n",
       "ModelID                                                                4089  \n",
       "datasource                                                              132  \n",
       "auctioneerID                                                             99  \n",
       "YearMade                                                               1984  \n",
       "MachineHoursCurrentMeter                                                NaN  \n",
       "UsageBand                                                               NaN  \n",
       "fiModelDesc                                                             D3B  \n",
       "fiBaseModel                                                              D3  \n",
       "fiSecondaryDesc                                                           B  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 20.0 to 75.0 Horse...  \n",
       "state                                                               Florida  \n",
       "ProductGroup                                                            TTT  \n",
       "ProductGroupDesc                                        Track Type Tractors  \n",
       "Drive_System                                                            NaN  \n",
       "Enclosure                                                             OROPS  \n",
       "Forks                                                                   NaN  \n",
       "Pad_Type                                                                NaN  \n",
       "Ride_Control                                                            NaN  \n",
       "Stick                                                                   NaN  \n",
       "Transmission                                                       Standard  \n",
       "Turbocharged                                                            NaN  \n",
       "Blade_Extension                                                         NaN  \n",
       "Blade_Width                                                             NaN  \n",
       "Enclosure_Type                                                          NaN  \n",
       "Engine_Horsepower                                                       NaN  \n",
       "Hydraulics                                                          2 Valve  \n",
       "Pushblock                                                               NaN  \n",
       "Ripper                                                  None or Unspecified  \n",
       "Scarifier                                                               NaN  \n",
       "Tip_Control                                                             NaN  \n",
       "Tire_Size                                                               NaN  \n",
       "Coupler                                                                 NaN  \n",
       "Coupler_System                                                          NaN  \n",
       "Grouser_Tracks                                                          NaN  \n",
       "Hydraulics_Flow                                                         NaN  \n",
       "Track_Type                                                              NaN  \n",
       "Undercarriage_Pad_Width                                                 NaN  \n",
       "Stick_Length                                                            NaN  \n",
       "Thumb                                                                   NaN  \n",
       "Pattern_Changer                                                         NaN  \n",
       "Grouser_Type                                                            NaN  \n",
       "Backhoe_Mounting                                        None or Unspecified  \n",
       "Blade_Type                                                              PAT  \n",
       "Travel_Controls                                                       Lever  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  \n",
       "saleYear                                                               1989  \n",
       "saleMonth                                                                 1  \n",
       "saleDay                                                                  31  \n",
       "saleDayOfWeek                                                             1  \n",
       "saleDayOfYear                                                            31  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida           67320\n",
       "Texas             53110\n",
       "California        29761\n",
       "Washington        16222\n",
       "Georgia           14633\n",
       "Maryland          13322\n",
       "Mississippi       13240\n",
       "Ohio              12369\n",
       "Illinois          11540\n",
       "Colorado          11529\n",
       "New Jersey        11156\n",
       "North Carolina    10636\n",
       "Tennessee         10298\n",
       "Alabama           10292\n",
       "Pennsylvania      10234\n",
       "South Carolina     9951\n",
       "Arizona            9364\n",
       "New York           8639\n",
       "Connecticut        8276\n",
       "Minnesota          7885\n",
       "Missouri           7178\n",
       "Nevada             6932\n",
       "Louisiana          6627\n",
       "Kentucky           5351\n",
       "Maine              5096\n",
       "Indiana            4124\n",
       "Arkansas           3933\n",
       "New Mexico         3631\n",
       "Utah               3046\n",
       "Unspecified        2801\n",
       "Wisconsin          2745\n",
       "New Hampshire      2738\n",
       "Virginia           2353\n",
       "Idaho              2025\n",
       "Oregon             1911\n",
       "Michigan           1831\n",
       "Wyoming            1672\n",
       "Montana            1336\n",
       "Iowa               1336\n",
       "Oklahoma           1326\n",
       "Nebraska            866\n",
       "West Virginia       840\n",
       "Kansas              667\n",
       "Delaware            510\n",
       "North Dakota        480\n",
       "Alaska              430\n",
       "Massachusetts       347\n",
       "Vermont             300\n",
       "South Dakota        244\n",
       "Hawaii              118\n",
       "Rhode Island         83\n",
       "Puerto Rico          42\n",
       "Washington DC         2\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# States with the most sales\n",
    "df_tmp['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modelling\n",
    "\n",
    "Since we have way too many features, let's better start doing some model-driven EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're gonna need to convert all features to number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 412698 entries, 205615 to 409203\n",
      "Data columns (total 57 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   fiModelDesc               412698 non-null  object \n",
      " 10  fiBaseModel               412698 non-null  object \n",
      " 11  fiSecondaryDesc           271971 non-null  object \n",
      " 12  fiModelSeries             58667 non-null   object \n",
      " 13  fiModelDescriptor         74816 non-null   object \n",
      " 14  ProductSize               196093 non-null  object \n",
      " 15  fiProductClassDesc        412698 non-null  object \n",
      " 16  state                     412698 non-null  object \n",
      " 17  ProductGroup              412698 non-null  object \n",
      " 18  ProductGroupDesc          412698 non-null  object \n",
      " 19  Drive_System              107087 non-null  object \n",
      " 20  Enclosure                 412364 non-null  object \n",
      " 21  Forks                     197715 non-null  object \n",
      " 22  Pad_Type                  81096 non-null   object \n",
      " 23  Ride_Control              152728 non-null  object \n",
      " 24  Stick                     81096 non-null   object \n",
      " 25  Transmission              188007 non-null  object \n",
      " 26  Turbocharged              81096 non-null   object \n",
      " 27  Blade_Extension           25983 non-null   object \n",
      " 28  Blade_Width               25983 non-null   object \n",
      " 29  Enclosure_Type            25983 non-null   object \n",
      " 30  Engine_Horsepower         25983 non-null   object \n",
      " 31  Hydraulics                330133 non-null  object \n",
      " 32  Pushblock                 25983 non-null   object \n",
      " 33  Ripper                    106945 non-null  object \n",
      " 34  Scarifier                 25994 non-null   object \n",
      " 35  Tip_Control               25983 non-null   object \n",
      " 36  Tire_Size                 97638 non-null   object \n",
      " 37  Coupler                   220679 non-null  object \n",
      " 38  Coupler_System            44974 non-null   object \n",
      " 39  Grouser_Tracks            44875 non-null   object \n",
      " 40  Hydraulics_Flow           44875 non-null   object \n",
      " 41  Track_Type                102193 non-null  object \n",
      " 42  Undercarriage_Pad_Width   102916 non-null  object \n",
      " 43  Stick_Length              102261 non-null  object \n",
      " 44  Thumb                     102332 non-null  object \n",
      " 45  Pattern_Changer           102261 non-null  object \n",
      " 46  Grouser_Type              102193 non-null  object \n",
      " 47  Backhoe_Mounting          80712 non-null   object \n",
      " 48  Blade_Type                81875 non-null   object \n",
      " 49  Travel_Controls           81877 non-null   object \n",
      " 50  Differential_Type         71564 non-null   object \n",
      " 51  Steering_Controls         71522 non-null   object \n",
      " 52  saleYear                  412698 non-null  int64  \n",
      " 53  saleMonth                 412698 non-null  int64  \n",
      " 54  saleDay                   412698 non-null  int64  \n",
      " 55  saleDayOfWeek             412698 non-null  int64  \n",
      " 56  saleDayOfYear             412698 non-null  int64  \n",
      "dtypes: float64(3), int64(10), object(44)\n",
      "memory usage: 182.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also have to handle missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                          0\n",
       "SalePrice                        0\n",
       "MachineID                        0\n",
       "ModelID                          0\n",
       "datasource                       0\n",
       "auctioneerID                 20136\n",
       "YearMade                         0\n",
       "MachineHoursCurrentMeter    265194\n",
       "UsageBand                   339028\n",
       "fiModelDesc                      0\n",
       "fiBaseModel                      0\n",
       "fiSecondaryDesc             140727\n",
       "fiModelSeries               354031\n",
       "fiModelDescriptor           337882\n",
       "ProductSize                 216605\n",
       "fiProductClassDesc               0\n",
       "state                            0\n",
       "ProductGroup                     0\n",
       "ProductGroupDesc                 0\n",
       "Drive_System                305611\n",
       "Enclosure                      334\n",
       "Forks                       214983\n",
       "Pad_Type                    331602\n",
       "Ride_Control                259970\n",
       "Stick                       331602\n",
       "Transmission                224691\n",
       "Turbocharged                331602\n",
       "Blade_Extension             386715\n",
       "Blade_Width                 386715\n",
       "Enclosure_Type              386715\n",
       "Engine_Horsepower           386715\n",
       "Hydraulics                   82565\n",
       "Pushblock                   386715\n",
       "Ripper                      305753\n",
       "Scarifier                   386704\n",
       "Tip_Control                 386715\n",
       "Tire_Size                   315060\n",
       "Coupler                     192019\n",
       "Coupler_System              367724\n",
       "Grouser_Tracks              367823\n",
       "Hydraulics_Flow             367823\n",
       "Track_Type                  310505\n",
       "Undercarriage_Pad_Width     309782\n",
       "Stick_Length                310437\n",
       "Thumb                       310366\n",
       "Pattern_Changer             310437\n",
       "Grouser_Type                310505\n",
       "Backhoe_Mounting            331986\n",
       "Blade_Type                  330823\n",
       "Travel_Controls             330821\n",
       "Differential_Type           341134\n",
       "Steering_Controls           341176\n",
       "saleYear                         0\n",
       "saleMonth                        0\n",
       "saleDay                          0\n",
       "saleDayOfWeek                    0\n",
       "saleDayOfYear                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting strings to categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the columns which contain strings and convert them to ordered categories\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        df_tmp[label] = content.astype('category').cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 412698 entries, 205615 to 409203\n",
      "Data columns (total 57 columns):\n",
      " #   Column                    Non-Null Count   Dtype   \n",
      "---  ------                    --------------   -----   \n",
      " 0   SalesID                   412698 non-null  int64   \n",
      " 1   SalePrice                 412698 non-null  float64 \n",
      " 2   MachineID                 412698 non-null  int64   \n",
      " 3   ModelID                   412698 non-null  int64   \n",
      " 4   datasource                412698 non-null  int64   \n",
      " 5   auctioneerID              392562 non-null  float64 \n",
      " 6   YearMade                  412698 non-null  int64   \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64 \n",
      " 8   UsageBand                 73670 non-null   category\n",
      " 9   fiModelDesc               412698 non-null  category\n",
      " 10  fiBaseModel               412698 non-null  category\n",
      " 11  fiSecondaryDesc           271971 non-null  category\n",
      " 12  fiModelSeries             58667 non-null   category\n",
      " 13  fiModelDescriptor         74816 non-null   category\n",
      " 14  ProductSize               196093 non-null  category\n",
      " 15  fiProductClassDesc        412698 non-null  category\n",
      " 16  state                     412698 non-null  category\n",
      " 17  ProductGroup              412698 non-null  category\n",
      " 18  ProductGroupDesc          412698 non-null  category\n",
      " 19  Drive_System              107087 non-null  category\n",
      " 20  Enclosure                 412364 non-null  category\n",
      " 21  Forks                     197715 non-null  category\n",
      " 22  Pad_Type                  81096 non-null   category\n",
      " 23  Ride_Control              152728 non-null  category\n",
      " 24  Stick                     81096 non-null   category\n",
      " 25  Transmission              188007 non-null  category\n",
      " 26  Turbocharged              81096 non-null   category\n",
      " 27  Blade_Extension           25983 non-null   category\n",
      " 28  Blade_Width               25983 non-null   category\n",
      " 29  Enclosure_Type            25983 non-null   category\n",
      " 30  Engine_Horsepower         25983 non-null   category\n",
      " 31  Hydraulics                330133 non-null  category\n",
      " 32  Pushblock                 25983 non-null   category\n",
      " 33  Ripper                    106945 non-null  category\n",
      " 34  Scarifier                 25994 non-null   category\n",
      " 35  Tip_Control               25983 non-null   category\n",
      " 36  Tire_Size                 97638 non-null   category\n",
      " 37  Coupler                   220679 non-null  category\n",
      " 38  Coupler_System            44974 non-null   category\n",
      " 39  Grouser_Tracks            44875 non-null   category\n",
      " 40  Hydraulics_Flow           44875 non-null   category\n",
      " 41  Track_Type                102193 non-null  category\n",
      " 42  Undercarriage_Pad_Width   102916 non-null  category\n",
      " 43  Stick_Length              102261 non-null  category\n",
      " 44  Thumb                     102332 non-null  category\n",
      " 45  Pattern_Changer           102261 non-null  category\n",
      " 46  Grouser_Type              102193 non-null  category\n",
      " 47  Backhoe_Mounting          80712 non-null   category\n",
      " 48  Blade_Type                81875 non-null   category\n",
      " 49  Travel_Controls           81877 non-null   category\n",
      " 50  Differential_Type         71564 non-null   category\n",
      " 51  Steering_Controls         71522 non-null   category\n",
      " 52  saleYear                  412698 non-null  int64   \n",
      " 53  saleMonth                 412698 non-null  int64   \n",
      " 54  saleDay                   412698 non-null  int64   \n",
      " 55  saleDayOfWeek             412698 non-null  int64   \n",
      " 56  saleDayOfYear             412698 non-null  int64   \n",
      "dtypes: category(44), float64(3), int64(10)\n",
      "memory usage: 63.3 MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp['state'].cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp['state'].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now have a way to access all the features in the form of numbers. \n",
    "\n",
    "The next step is to take care of the missing data. Let's see the percentage of record with missing data for each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                      0.000000\n",
       "SalePrice                    0.000000\n",
       "MachineID                    0.000000\n",
       "ModelID                      0.000000\n",
       "datasource                   0.000000\n",
       "auctioneerID                 4.879113\n",
       "YearMade                     0.000000\n",
       "MachineHoursCurrentMeter    64.258610\n",
       "UsageBand                   82.149174\n",
       "fiModelDesc                  0.000000\n",
       "fiBaseModel                  0.000000\n",
       "fiSecondaryDesc             34.099269\n",
       "fiModelSeries               85.784520\n",
       "fiModelDescriptor           81.871490\n",
       "ProductSize                 52.485110\n",
       "fiProductClassDesc           0.000000\n",
       "state                        0.000000\n",
       "ProductGroup                 0.000000\n",
       "ProductGroupDesc             0.000000\n",
       "Drive_System                74.051970\n",
       "Enclosure                    0.080931\n",
       "Forks                       52.092087\n",
       "Pad_Type                    80.349796\n",
       "Ride_Control                62.992794\n",
       "Stick                       80.349796\n",
       "Transmission                54.444412\n",
       "Turbocharged                80.349796\n",
       "Blade_Extension             93.704113\n",
       "Blade_Width                 93.704113\n",
       "Enclosure_Type              93.704113\n",
       "Engine_Horsepower           93.704113\n",
       "Hydraulics                  20.006155\n",
       "Pushblock                   93.704113\n",
       "Ripper                      74.086378\n",
       "Scarifier                   93.701448\n",
       "Tip_Control                 93.704113\n",
       "Tire_Size                   76.341538\n",
       "Coupler                     46.527727\n",
       "Coupler_System              89.102443\n",
       "Grouser_Tracks              89.126431\n",
       "Hydraulics_Flow             89.126431\n",
       "Track_Type                  75.237825\n",
       "Undercarriage_Pad_Width     75.062637\n",
       "Stick_Length                75.221348\n",
       "Thumb                       75.204144\n",
       "Pattern_Changer             75.221348\n",
       "Grouser_Type                75.237825\n",
       "Backhoe_Mounting            80.442842\n",
       "Blade_Type                  80.161038\n",
       "Travel_Controls             80.160553\n",
       "Differential_Type           82.659475\n",
       "Steering_Controls           82.669652\n",
       "saleYear                     0.000000\n",
       "saleMonth                    0.000000\n",
       "saleDay                      0.000000\n",
       "saleDayOfWeek                0.000000\n",
       "saleDayOfYear                0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isnull().sum()/len(df_tmp)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the preprocessed data to a new file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export to csv\n",
    "df_tmp.to_csv('data/bluebook-for-bulldozers/processed-data/train_tmp.csv',\n",
    "              index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data into train and validation\n",
    "\n",
    "Before filling missing values, we need to split the data intro train and validation subsets, to prevent future data (from validation) leaking into the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    1989\n",
       "274835    1989\n",
       "141296    1989\n",
       "212552    1989\n",
       "62755     1989\n",
       "          ... \n",
       "410879    2012\n",
       "412476    2012\n",
       "411927    2012\n",
       "407124    2012\n",
       "409203    2012\n",
       "Name: saleYear, Length: 412698, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp['saleYear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009    43849\n",
       "2008    39767\n",
       "2011    35197\n",
       "2010    33390\n",
       "2007    32208\n",
       "2006    21685\n",
       "2005    20463\n",
       "2004    19879\n",
       "2001    17594\n",
       "2000    17415\n",
       "2002    17246\n",
       "2003    15254\n",
       "1998    13046\n",
       "1999    12793\n",
       "2012    11573\n",
       "1997     9785\n",
       "1996     8829\n",
       "1995     8530\n",
       "1994     7929\n",
       "1993     6303\n",
       "1992     5519\n",
       "1991     5109\n",
       "1989     4806\n",
       "1990     4529\n",
       "Name: saleYear, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp['saleYear'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401125, 11573)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into train and validation\n",
    "df_train = df_tmp[df_tmp.saleYear != 2012].copy()\n",
    "df_val = df_tmp[df_tmp.saleYear == 2012].copy()\n",
    "\n",
    "len(df_train), len(df_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing values\n",
    "\n",
    "First, let's create a function that takes a dataframe and a list of factor names and print how many and which ones has missing values. It also returns an array with the found factors if there are at least one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_missing_values(df, list_of_factors):\n",
    "    \"\"\"\n",
    "    Searches for factors with missing values in a given subset\n",
    "    of a dataframe. If it finds at least one, it returns a list with \n",
    "    the name of the factors.\n",
    "    \n",
    "    Parameters:\n",
    "     df: Dataframe to be searched.\n",
    "     list_of_factors: List with the subset of factors.\n",
    "    \"\"\"\n",
    "    missing = []\n",
    "    \n",
    "    for label, content in df[list_of_factors].items():\n",
    "        if pd.isnull(content).sum():\n",
    "            missing.append(label)\n",
    "    \n",
    "    print(f\"There are {len(missing)} columns with missing values{':'+ chr(10) if len(missing) else '.'}\")\n",
    "    for el in missing:\n",
    "        print(el)\n",
    "        \n",
    "    return missing if len(missing) > 0 else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numeric missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalesID\n",
      "SalePrice\n",
      "MachineID\n",
      "ModelID\n",
      "datasource\n",
      "auctioneerID\n",
      "YearMade\n",
      "MachineHoursCurrentMeter\n",
      "saleYear\n",
      "saleMonth\n",
      "saleDay\n",
      "saleDayOfWeek\n",
      "saleDayOfYear\n"
     ]
    }
   ],
   "source": [
    "# Print numerical factors\n",
    "numeric = []\n",
    "for label, content in df_train.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)\n",
    "        numeric.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 columns with missing values:\n",
      "\n",
      "auctioneerID\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "# Now let's find out which of them have missing values\n",
    "missing = find_missing_values(df_train, numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fill with the median, since mean is too sensitive to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For train subset\n",
    "for label, content in df_train[missing].items():\n",
    "    \n",
    "    # Add a binary column that indicates if data was missing\n",
    "    df_train[f'{label}_is_missing'] = pd.isnull(content)\n",
    "\n",
    "    # Fill missing values with the median\n",
    "    df_train[label] = content.fillna(content.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 columns with missing values.\n"
     ]
    }
   ],
   "source": [
    "# Let's check if there's no null numeric values anymore\n",
    "find_missing_values(df_train, numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    380989\n",
       "True      20136\n",
       "Name: auctioneerID_is_missing, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many examples were missing\n",
    "df_train['auctioneerID_is_missing'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 columns with missing values:\n",
      "\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "# Let's do the same for the the validation data\n",
    "missing_val = find_missing_values(df_val, numeric)\n",
    "\n",
    "for label, content in df_val[missing_val].items():\n",
    "    df_val[f'{label}_is_missing'] = pd.isnull(content)\n",
    "    df_val[label] = content.fillna(content.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're gonna need to also add an `auctioneerID_is_missing` column to the validation data, so we have the exact same columns for the training and validation dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_val['auctioneerID_is_missing'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand\n",
      "fiModelDesc\n",
      "fiBaseModel\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "fiProductClassDesc\n",
      "state\n",
      "ProductGroup\n",
      "ProductGroupDesc\n",
      "Drive_System\n",
      "Enclosure\n",
      "Forks\n",
      "Pad_Type\n",
      "Ride_Control\n",
      "Stick\n",
      "Transmission\n",
      "Turbocharged\n",
      "Blade_Extension\n",
      "Blade_Width\n",
      "Enclosure_Type\n",
      "Engine_Horsepower\n",
      "Hydraulics\n",
      "Pushblock\n",
      "Ripper\n",
      "Scarifier\n",
      "Tip_Control\n",
      "Tire_Size\n",
      "Coupler\n",
      "Coupler_System\n",
      "Grouser_Tracks\n",
      "Hydraulics_Flow\n",
      "Track_Type\n",
      "Undercarriage_Pad_Width\n",
      "Stick_Length\n",
      "Thumb\n",
      "Pattern_Changer\n",
      "Grouser_Type\n",
      "Backhoe_Mounting\n",
      "Blade_Type\n",
      "Travel_Controls\n",
      "Differential_Type\n",
      "Steering_Controls\n"
     ]
    }
   ],
   "source": [
    "# Check for columns which are not numeric for train data\n",
    "categorical = []\n",
    "\n",
    "for label, content in df_train.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        categorical.append(label)\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',\n",
       "                  'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia',\n",
       "                  'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas',\n",
       "                  'Kentucky', 'Louisiana', 'Maine', 'Maryland',\n",
       "                  'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi',\n",
       "                  'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire',\n",
       "                  'New Jersey', 'New Mexico', 'New York', 'North Carolina',\n",
       "                  'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania',\n",
       "                  'Puerto Rico', 'Rhode Island', 'South Carolina',\n",
       "                  'South Dakota', 'Tennessee', 'Texas', 'Unspecified', 'Utah',\n",
       "                  'Vermont', 'Virginia', 'Washington', 'Washington DC',\n",
       "                  'West Virginia', 'Wisconsin', 'Wyoming'],\n",
       "                 ordered=True)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_train['state']).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a numerical representation of these in the codes properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43,  8,  8, ..., 31, 31, 31], dtype=int8)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_train['state']).codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the categorical factors with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 38 columns with missing values:\n",
      "\n",
      "UsageBand\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "Drive_System\n",
      "Enclosure\n",
      "Forks\n",
      "Pad_Type\n",
      "Ride_Control\n",
      "Stick\n",
      "Transmission\n",
      "Turbocharged\n",
      "Blade_Extension\n",
      "Blade_Width\n",
      "Enclosure_Type\n",
      "Engine_Horsepower\n",
      "Hydraulics\n",
      "Pushblock\n",
      "Ripper\n",
      "Scarifier\n",
      "Tip_Control\n",
      "Tire_Size\n",
      "Coupler\n",
      "Coupler_System\n",
      "Grouser_Tracks\n",
      "Hydraulics_Flow\n",
      "Track_Type\n",
      "Undercarriage_Pad_Width\n",
      "Stick_Length\n",
      "Thumb\n",
      "Pattern_Changer\n",
      "Grouser_Type\n",
      "Backhoe_Mounting\n",
      "Blade_Type\n",
      "Travel_Controls\n",
      "Differential_Type\n",
      "Steering_Controls\n"
     ]
    }
   ],
   "source": [
    "missing_cat = find_missing_values(df_train, categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn all categorical variables into numbers and fill the missing values.\n",
    "for label, content in df_train.items():\n",
    "    \n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        # Add a binary column that indicates if data was missing\n",
    "        df_train[f'{label}_is_missing'] = pd.isnull(content)\n",
    "\n",
    "        # Turn categories into numbers and add 1 (to turn missing into 0 insted of -1)\n",
    "        df_train[label] = pd.Categorical(content).codes + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                         0\n",
       "SalePrice                       0\n",
       "MachineID                       0\n",
       "ModelID                         0\n",
       "datasource                      0\n",
       "                               ..\n",
       "Backhoe_Mounting_is_missing     0\n",
       "Blade_Type_is_missing           0\n",
       "Travel_Controls_is_missing      0\n",
       "Differential_Type_is_missing    0\n",
       "Steering_Controls_is_missing    0\n",
       "Length: 103, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 38 columns with missing values:\n",
      "\n",
      "UsageBand\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "Drive_System\n",
      "Enclosure\n",
      "Forks\n",
      "Pad_Type\n",
      "Ride_Control\n",
      "Stick\n",
      "Transmission\n",
      "Turbocharged\n",
      "Blade_Extension\n",
      "Blade_Width\n",
      "Enclosure_Type\n",
      "Engine_Horsepower\n",
      "Hydraulics\n",
      "Pushblock\n",
      "Ripper\n",
      "Scarifier\n",
      "Tip_Control\n",
      "Tire_Size\n",
      "Coupler\n",
      "Coupler_System\n",
      "Grouser_Tracks\n",
      "Hydraulics_Flow\n",
      "Track_Type\n",
      "Undercarriage_Pad_Width\n",
      "Stick_Length\n",
      "Thumb\n",
      "Pattern_Changer\n",
      "Grouser_Type\n",
      "Backhoe_Mounting\n",
      "Blade_Type\n",
      "Travel_Controls\n",
      "Differential_Type\n",
      "Steering_Controls\n"
     ]
    }
   ],
   "source": [
    "# The same for validation data\n",
    "cat_missing_val = find_missing_values(df_val, categorical)\n",
    "\n",
    "for label, content in df_val.items():\n",
    "    \n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        df_val[f'{label}_is_missing'] = pd.isnull(content)\n",
    "        df_val[label] = pd.Categorical(content).codes + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                         0\n",
       "SalePrice                       0\n",
       "MachineID                       0\n",
       "ModelID                         0\n",
       "datasource                      0\n",
       "                               ..\n",
       "Backhoe_Mounting_is_missing     0\n",
       "Blade_Type_is_missing           0\n",
       "Travel_Controls_is_missing      0\n",
       "Differential_Type_is_missing    0\n",
       "Steering_Controls_is_missing    0\n",
       "Length: 103, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that we now have all data as numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 401125 entries, 205615 to 393123\n",
      "Columns: 103 entries, SalesID to Steering_Controls_is_missing\n",
      "dtypes: bool(46), float64(3), int16(4), int64(10), int8(40)\n",
      "memory usage: 78.8 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have taken care of the missing data, we can start experimenting with machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a custom evaluation function\n",
    "\n",
    "The desired evaluation metric is RMSLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
    "    Calculates root mean squared log error between predictions and true labels.\n",
    "    \"\"\"\n",
    "    return np.sqrt(mean_squared_log_error(y_test, y_preds))\n",
    "\n",
    "# Create a function to evaluate on different levels\n",
    "def model_scores(model):\n",
    "    train_preds = model.predict(X_train) # Lookout for overfitting\n",
    "    val_preds = model.predict(X_val)\n",
    "    scores = {\"Training MAE\": mean_absolute_error(y_train, train_preds),\n",
    "              \"Validation MAE\": mean_absolute_error(y_val, val_preds),\n",
    "              \"Training RMSLE\": rmsle(y_train, train_preds),\n",
    "              \"Valid RMSLE\": rmsle(y_val, val_preds),\n",
    "              \"Training R^2\": r2_score(y_train, train_preds),\n",
    "              \"Valid R^2\": r2_score(y_val, val_preds)}\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 102), (401125,), (11573, 102), (11573,))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split datasets into X and y\n",
    "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']\n",
    "\n",
    "X_train.shape, y_train.shape, X_val.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new Random Forest Regressor\n",
    "\n",
    "Since we have too many rows, we will give each estimator of the model only 10000 samples until we find the best hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor(n_jobs=1,\n",
    "                              random_state=919,\n",
    "                              max_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27.2 s, sys: 437 ms, total: 27.6 s\n",
      "Wall time: 27.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_samples=10000, n_jobs=1, random_state=919)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5558.674899395451,\n",
       " 'Validation MAE': 7174.361975287306,\n",
       " 'Training RMSLE': 0.25775046677650415,\n",
       " 'Valid RMSLE': 0.2951636972470515,\n",
       " 'Training R^2': 0.8609861837634141,\n",
       " 'Valid R^2': 0.8333419984956177}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning\n",
    "\n",
    "We'll use RandomizedSearchCV to try different hyperparameters and find the optimal ones to be used to train the whole train subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 400 candidates, totalling 2000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 2000 out of 2000 | elapsed: 144.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestRegressor(max_samples=10000, n_jobs=-1,\n",
       "                                                   random_state=919),\n",
       "                   n_iter=400,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'max_features': [0.5, 1, 'sqrt',\n",
       "                                                         'auto'],\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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Hyperparamters\n",
    "rf_grid = {'n_estimators': np.arange(10, 100, 10),\n",
    "           'max_depth': [None, 3, 5, 10],\n",
    "           'min_samples_split': np.arange(2, 20, 2),\n",
    "           'min_samples_leaf': np.arange(1, 20, 2),\n",
    "           'max_features': [0.5, 1, 'sqrt', 'auto']}\n",
    "\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1,\n",
    "                                                    random_state=919,\n",
    "                                                    max_samples=10000),\n",
    "                              param_distributions=rf_grid,\n",
    "                              n_iter=400,\n",
    "                              cv=5,\n",
    "                              verbose=True)\n",
    "\n",
    "# Fit the RandomizedSearchCV model\n",
    "rs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 90,\n",
       " 'min_samples_split': 8,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best hyperparameters\n",
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5642.928735272909,\n",
       " 'Validation MAE': 7230.236319500355,\n",
       " 'Training RMSLE': 0.2608373524694065,\n",
       " 'Valid RMSLE': 0.2956047504945353,\n",
       " 'Training R^2': 0.8565085074766974,\n",
       " 'Valid R^2': 0.8301131076262743}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the RandomizedSearch model\n",
    "model_scores(rs_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model with the best hyperparameters\n",
    "\n",
    "We will now train a new Random Forest Regressor with the hyperparameters found in `rs_model.best_params_`. We will now leave the `max_samples` parameter to its default value of `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=0.5, n_estimators=90, n_jobs=-1,\n",
       "                      random_state=919)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = RandomForestRegressor(n_jobs=-1,\n",
    "                                   n_estimators=90,\n",
    "                                   min_samples_split=2,\n",
    "                                   min_samples_leaf=1,\n",
    "                                   max_features=0.5,\n",
    "                                   max_depth=None,\n",
    "                                   random_state=919)\n",
    "\n",
    "best_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 1563.3769983310826,\n",
       " 'Validation MAE': 5932.545063701911,\n",
       " 'Training RMSLE': 0.08337331853259292,\n",
       " 'Valid RMSLE': 0.24594554277656414,\n",
       " 'Training R^2': 0.9880042134456916,\n",
       " 'Valid R^2': 0.8828852673160688}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions with `best_model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-03</td>\n",
       "      <td>580G</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>936</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>EC210BLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>9' 6\"</td>\n",
       "      <td>Manual</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>330CL</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Triple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>650K</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
       "0                    3688.0       Low 2012-05-03        580G  ...   \n",
       "1                   28555.0      High 2012-05-10         936  ...   \n",
       "2                    6038.0    Medium 2012-05-10    EC210BLC  ...   \n",
       "3                    8940.0      High 2012-05-10       330CL  ...   \n",
       "4                    2286.0       Low 2012-05-10        650K  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width         Stick_Length   Thumb      Pattern_Changer  \\\n",
       "0                     NaN                  NaN     NaN                  NaN   \n",
       "1                     NaN                  NaN     NaN                  NaN   \n",
       "2     None or Unspecified                9' 6\"  Manual  None or Unspecified   \n",
       "3     None or Unspecified  None or Unspecified  Manual                  Yes   \n",
       "4                     NaN                  NaN     NaN                  NaN   \n",
       "\n",
       "  Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "0          NaN                  NaN        NaN                  NaN   \n",
       "1          NaN                  NaN        NaN                  NaN   \n",
       "2       Double                  NaN        NaN                  NaN   \n",
       "3       Triple                  NaN        NaN                  NaN   \n",
       "4          NaN  None or Unspecified        PAT  None or Unspecified   \n",
       "\n",
       "  Differential_Type Steering_Controls  \n",
       "0               NaN               NaN  \n",
       "1          Standard      Conventional  \n",
       "2               NaN               NaN  \n",
       "3               NaN               NaN  \n",
       "4               NaN               NaN  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import test data\n",
    "df_test = pd.read_csv('./data/bluebook-for-bulldozers/Test.csv',\n",
    "                      low_memory=False,\n",
    "                      parse_dates=['saledate'])\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12457 entries, 0 to 12456\n",
      "Data columns (total 52 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   SalesID                   12457 non-null  int64         \n",
      " 1   MachineID                 12457 non-null  int64         \n",
      " 2   ModelID                   12457 non-null  int64         \n",
      " 3   datasource                12457 non-null  int64         \n",
      " 4   auctioneerID              12457 non-null  int64         \n",
      " 5   YearMade                  12457 non-null  int64         \n",
      " 6   MachineHoursCurrentMeter  2129 non-null   float64       \n",
      " 7   UsageBand                 1834 non-null   object        \n",
      " 8   saledate                  12457 non-null  datetime64[ns]\n",
      " 9   fiModelDesc               12457 non-null  object        \n",
      " 10  fiBaseModel               12457 non-null  object        \n",
      " 11  fiSecondaryDesc           8482 non-null   object        \n",
      " 12  fiModelSeries             2006 non-null   object        \n",
      " 13  fiModelDescriptor         3024 non-null   object        \n",
      " 14  ProductSize               6048 non-null   object        \n",
      " 15  fiProductClassDesc        12457 non-null  object        \n",
      " 16  state                     12457 non-null  object        \n",
      " 17  ProductGroup              12457 non-null  object        \n",
      " 18  ProductGroupDesc          12457 non-null  object        \n",
      " 19  Drive_System              2759 non-null   object        \n",
      " 20  Enclosure                 12455 non-null  object        \n",
      " 21  Forks                     6308 non-null   object        \n",
      " 22  Pad_Type                  2108 non-null   object        \n",
      " 23  Ride_Control              4241 non-null   object        \n",
      " 24  Stick                     2108 non-null   object        \n",
      " 25  Transmission              4818 non-null   object        \n",
      " 26  Turbocharged              2108 non-null   object        \n",
      " 27  Blade_Extension           651 non-null    object        \n",
      " 28  Blade_Width               651 non-null    object        \n",
      " 29  Enclosure_Type            651 non-null    object        \n",
      " 30  Engine_Horsepower         651 non-null    object        \n",
      " 31  Hydraulics                10315 non-null  object        \n",
      " 32  Pushblock                 651 non-null    object        \n",
      " 33  Ripper                    2704 non-null   object        \n",
      " 34  Scarifier                 651 non-null    object        \n",
      " 35  Tip_Control               651 non-null    object        \n",
      " 36  Tire_Size                 2778 non-null   object        \n",
      " 37  Coupler                   7601 non-null   object        \n",
      " 38  Coupler_System            2066 non-null   object        \n",
      " 39  Grouser_Tracks            2066 non-null   object        \n",
      " 40  Hydraulics_Flow           2066 non-null   object        \n",
      " 41  Track_Type                3394 non-null   object        \n",
      " 42  Undercarriage_Pad_Width   3398 non-null   object        \n",
      " 43  Stick_Length              3394 non-null   object        \n",
      " 44  Thumb                     3395 non-null   object        \n",
      " 45  Pattern_Changer           3394 non-null   object        \n",
      " 46  Grouser_Type              3394 non-null   object        \n",
      " 47  Backhoe_Mounting          2051 non-null   object        \n",
      " 48  Blade_Type                2058 non-null   object        \n",
      " 49  Travel_Controls           2058 non-null   object        \n",
      " 50  Differential_Type         2129 non-null   object        \n",
      " 51  Steering_Controls         2129 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(6), object(44)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                         0\n",
       "MachineID                       0\n",
       "ModelID                         0\n",
       "datasource                      0\n",
       "auctioneerID                    0\n",
       "YearMade                        0\n",
       "MachineHoursCurrentMeter    10328\n",
       "UsageBand                   10623\n",
       "saledate                        0\n",
       "fiModelDesc                     0\n",
       "fiBaseModel                     0\n",
       "fiSecondaryDesc              3975\n",
       "fiModelSeries               10451\n",
       "fiModelDescriptor            9433\n",
       "ProductSize                  6409\n",
       "fiProductClassDesc              0\n",
       "state                           0\n",
       "ProductGroup                    0\n",
       "ProductGroupDesc                0\n",
       "Drive_System                 9698\n",
       "Enclosure                       2\n",
       "Forks                        6149\n",
       "Pad_Type                    10349\n",
       "Ride_Control                 8216\n",
       "Stick                       10349\n",
       "Transmission                 7639\n",
       "Turbocharged                10349\n",
       "Blade_Extension             11806\n",
       "Blade_Width                 11806\n",
       "Enclosure_Type              11806\n",
       "Engine_Horsepower           11806\n",
       "Hydraulics                   2142\n",
       "Pushblock                   11806\n",
       "Ripper                       9753\n",
       "Scarifier                   11806\n",
       "Tip_Control                 11806\n",
       "Tire_Size                    9679\n",
       "Coupler                      4856\n",
       "Coupler_System              10391\n",
       "Grouser_Tracks              10391\n",
       "Hydraulics_Flow             10391\n",
       "Track_Type                   9063\n",
       "Undercarriage_Pad_Width      9059\n",
       "Stick_Length                 9063\n",
       "Thumb                        9062\n",
       "Pattern_Changer              9063\n",
       "Grouser_Type                 9063\n",
       "Backhoe_Mounting            10406\n",
       "Blade_Type                  10399\n",
       "Travel_Controls             10399\n",
       "Differential_Type           10328\n",
       "Steering_Controls           10328\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need to match the factor count and make them all numeric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(df):\n",
    "    \"\"\"\n",
    "    Process the data as needed and return the transormed dataframe\n",
    "    \"\"\"\n",
    "    df['saleYear'] = df['saledate'].dt.year\n",
    "    df['saleMonth'] = df['saledate'].dt.month\n",
    "    df['saleDay'] = df['saledate'].dt.day\n",
    "    df['saleDayOfWeek'] = df['saledate'].dt.dayofweek\n",
    "    df['saleDayOfYear'] = df['saledate'].dt.dayofyear\n",
    "    \n",
    "    df.drop('saledate', axis=1, inplace=True)\n",
    "    \n",
    "    # Fill numeric rows with median\n",
    "    for label, content in df.items():\n",
    "        if pd.api.types.is_numeric_dtype(content):\n",
    "            if pd.isnull(content).sum():\n",
    "                # Add binary column that indicates if there was missing data\n",
    "                df[label+\"_is_missing\"] = pd.isnull(content)\n",
    "                # Fill missing numeric values with median\n",
    "                df[label] = content.fillna(content.median())\n",
    "    \n",
    "        # Turn categorical into numbers and fill missings\n",
    "        else:\n",
    "            df[label+'_is_missing'] = pd.isnull(content)\n",
    "            # We add 1 to the category code\n",
    "            df[label] = pd.Categorical(content).codes + 1\n",
    "                \n",
    "        \n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>1</td>\n",
       "      <td>831</td>\n",
       "      <td>292</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1177</td>\n",
       "      <td>404</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>1</td>\n",
       "      <td>287</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                               False                    False   \n",
       "3                               False                    False   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2             False                       False                    False   \n",
       "3             False                       False                    False   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                         True                   True   \n",
       "1                         True                   True   \n",
       "2                         True                   True   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                        True                          True   \n",
       "1                        True                         False   \n",
       "2                        True                          True   \n",
       "3                        True                          True   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                          True  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocessing test data\n",
    "df_test = preprocess_data(df_test)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
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       "      <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",
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       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4593</td>\n",
       "      <td>1744</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>559</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>141296</th>\n",
       "      <td>1505138</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1819</td>\n",
       "      <td>558</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>62755</th>\n",
       "      <td>1329056</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2119</td>\n",
       "      <td>683</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "205615  1646770    1126363     8434         132          18.0      1974   \n",
       "274835  1821514    1194089    10150         132          99.0      1980   \n",
       "141296  1505138    1473654     4139         132          99.0      1978   \n",
       "212552  1671174    1327630     8591         132          99.0      1980   \n",
       "62755   1329056    1336053     4089         132          99.0      1984   \n",
       "\n",
       "        MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "205615                       0.0          0         4593         1744  ...   \n",
       "274835                       0.0          0         1820          559  ...   \n",
       "141296                       0.0          0         2348          713  ...   \n",
       "212552                       0.0          0         1819          558  ...   \n",
       "62755                        0.0          0         2119          683  ...   \n",
       "\n",
       "        Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "205615                                True                     True   \n",
       "274835                                True                     True   \n",
       "141296                                True                     True   \n",
       "212552                                True                     True   \n",
       "62755                                 True                     True   \n",
       "\n",
       "        Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "205615              True                        True                     True   \n",
       "274835              True                        True                     True   \n",
       "141296              True                        True                     True   \n",
       "212552              True                        True                     True   \n",
       "62755               True                        True                     True   \n",
       "\n",
       "        Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "205615                        False                  False   \n",
       "274835                         True                   True   \n",
       "141296                        False                  False   \n",
       "212552                         True                   True   \n",
       "62755                         False                  False   \n",
       "\n",
       "        Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "205615                       False                          True   \n",
       "274835                        True                         False   \n",
       "141296                       False                          True   \n",
       "212552                        True                         False   \n",
       "62755                        False                          True   \n",
       "\n",
       "        Steering_Controls_is_missing  \n",
       "205615                          True  \n",
       "274835                         False  \n",
       "141296                          True  \n",
       "212552                         False  \n",
       "62755                           True  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we have a different number of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'auctioneerID_is_missing'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(X_train.columns) - set(df_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could edit our `preprocess_data` function to iterate over the array of the factors with missing values that we previously have. However, since we have just one missing column, we can simply add a new column with all `False` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SalesID</th>\n",
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       "<p>5 rows × 102 columns</p>\n",
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      ],
      "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",
       "   Stick_Length_is_missing  Thumb_is_missing  Pattern_Changer_is_missing  \\\n",
       "0                     True              True                        True   \n",
       "1                     True              True                        True   \n",
       "2                    False             False                       False   \n",
       "3                    False             False                       False   \n",
       "4                     True              True                        True   \n",
       "\n",
       "   Grouser_Type_is_missing  Backhoe_Mounting_is_missing  \\\n",
       "0                     True                         True   \n",
       "1                     True                         True   \n",
       "2                    False                         True   \n",
       "3                    False                         True   \n",
       "4                     True                        False   \n",
       "\n",
       "   Blade_Type_is_missing  Travel_Controls_is_missing  \\\n",
       "0                   True                        True   \n",
       "1                   True                        True   \n",
       "2                   True                        True   \n",
       "3                   True                        True   \n",
       "4                  False                       False   \n",
       "\n",
       "   Differential_Type_is_missing  Steering_Controls_is_missing  \\\n",
       "0                          True                          True   \n",
       "1                         False                         False   \n",
       "2                          True                          True   \n",
       "3                          True                          True   \n",
       "4                          True                          True   \n",
       "\n",
       "   auctioneerID_is_missing  \n",
       "0                    False  \n",
       "1                    False  \n",
       "2                    False  \n",
       "3                    False  \n",
       "4                    False  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['auctioneerID_is_missing'] = False\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we're ready to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test data\n",
    "test_preds = best_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's format the predictions in the required format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       SalesID    SalesPrice\n",
       "0      1227829  21300.000000\n",
       "1      1227844  19225.555556\n",
       "2      1227847  51566.666667\n",
       "3      1227848  67011.111111\n",
       "4      1227863  38359.444444\n",
       "...        ...           ...\n",
       "12452  6643171  45575.000000\n",
       "12453  6643173  14050.000000\n",
       "12454  6643184  14861.111111\n",
       "12455  6643186  17444.444444\n",
       "12456  6643196  27572.222222\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_preds = pd.DataFrame()\n",
    "final_preds['SalesID'] = df_test['SalesID']\n",
    "final_preds['SalesPrice'] = test_preds\n",
    "final_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export prediction data\n",
    "final_preds.to_csv('./data/predictions/bulldozers-price-predictions.csv',\n",
    "                    index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Let's figure out which features were the most important to predict the sale price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.64599375e-02, 2.30926668e-02, 3.99502617e-02, 2.06361068e-03,\n",
       "       4.96486559e-03, 1.96331348e-01, 4.00512328e-03, 1.43118329e-03,\n",
       "       4.25108115e-02, 4.29289537e-02, 6.00462133e-02, 4.88061703e-03,\n",
       "       1.54961478e-02, 1.43984089e-01, 4.38049879e-02, 9.09381442e-03,\n",
       "       2.47686454e-03, 2.78068774e-03, 3.42772654e-03, 7.18436854e-02,\n",
       "       1.15255570e-03, 7.48086296e-05, 1.60333607e-03, 1.84763620e-04,\n",
       "       1.60303677e-03, 4.65702474e-05, 1.98435447e-03, 6.56701290e-03,\n",
       "       1.21438166e-03, 1.03902641e-03, 3.10051778e-03, 3.31984515e-03,\n",
       "       5.37252504e-03, 1.77784576e-03, 2.12644941e-03, 6.40578549e-03,\n",
       "       1.31539499e-03, 1.01273318e-02, 1.53255451e-03, 1.63631386e-03,\n",
       "       1.21235930e-03, 1.45538096e-03, 2.43905563e-03, 8.25802389e-04,\n",
       "       6.38042716e-04, 5.56724304e-04, 4.19619243e-04, 2.51095109e-03,\n",
       "       9.36369070e-04, 2.59911295e-04, 2.88863167e-04, 7.27614154e-02,\n",
       "       6.10375255e-03, 9.48334156e-03, 5.03844075e-03, 1.35473727e-02,\n",
       "       3.73347883e-04, 1.59658883e-03, 5.53906056e-04, 0.00000000e+00,\n",
       "       0.00000000e+00, 2.93995137e-03, 1.45211097e-03, 5.71727243e-03,\n",
       "       2.89309259e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 3.03493288e-05, 7.55178157e-06, 3.91487118e-04,\n",
       "       2.78440395e-05, 1.58974647e-04, 4.99698049e-06, 2.66443369e-04,\n",
       "       8.96636799e-06, 2.63019963e-03, 3.69926531e-03, 1.02185219e-03,\n",
       "       1.22387878e-03, 2.07898547e-03, 1.10104434e-03, 2.12224235e-03,\n",
       "       8.81066384e-04, 3.30114735e-05, 1.91651369e-03, 1.90848543e-04,\n",
       "       1.37337254e-02, 2.54902851e-03, 3.11926409e-04, 8.70433592e-05,\n",
       "       8.24789593e-05, 6.92599644e-05, 8.26481329e-05, 6.07384937e-05,\n",
       "       6.28572154e-05, 6.70696605e-04, 2.28491816e-04, 1.47897304e-04,\n",
       "       2.00998415e-04, 1.17175365e-04])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting function\n",
    "def plot_features(columns, importances, n=20):\n",
    "    df = (pd.DataFrame({'features': columns,\n",
    "                        'feature_importances': importances})\n",
    "          .sort_values('feature_importances', ascending=False)\n",
    "          .reset_index(drop=True))\n",
    "    \n",
    "    # Plot\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(df['features'][:n], df['feature_importances'][:n])\n",
    "    ax.set_ylabel(\"Features\")\n",
    "    ax.set_xlabel(\"Feature importance\")\n",
    "    ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features(X_train.columns, best_model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium            64342\n",
       "Large / Medium    51297\n",
       "Small             27057\n",
       "Mini              25721\n",
       "Large             21396\n",
       "Compact            6280\n",
       "Name: ProductSize, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ProductSize'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OROPS                  177971\n",
       "EROPS                  141769\n",
       "EROPS w AC              92601\n",
       "EROPS AC                   18\n",
       "NO ROPS                     3\n",
       "None or Unspecified         2\n",
       "Name: Enclosure, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Enclosure'].value_counts()\n",
    "# Does a machine has an enclosed cab or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We managed to build a Random Forest Regressor with a **Root Mean Squared Log Error (RMSLE) score of ~0.24**, which is an indicator of how far the predicted value are from the actual value, without penalizing big differences, as we are trying to predict huge numbers.\n",
    "\n",
    "We algo found out that the most important feature is the **Year in which the bulldozer was made**, which is expected, since most assets devaluate with time. Product size is also important, followed by some other bulldozers configurations, such as Enclosure.\n",
    "\n",
    "As a recommendation for further exploration, some other models, such as **Support Vector Regressor or SGD Regressor** may help in decreasing the RMSLE and, generally, to get better results."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
 "nbformat": 4,
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}