end-to-end-bulldozer-price-regression-project 2.ipynb

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   "source": [
    "# Predicting the Sale Price of Bulldozers using Maching Learning\n",
    "\n",
    "In the notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers\n",
    "\n",
    "# 1. Problem defination\n",
    "\n",
    "> How well can we predict the future sale price of a bulldozer, given its characteristics and previous examples of how much similar bulldozer hae been sold for?\n",
    "\n",
    "# 2. Data\n",
    "\n",
    "The data is downloaded from the Kaggle Bluebook for Bulldozers competition:https://www.kaggle.com/c/bluebook-for-bulldozers/data\n",
    "\n",
    "There are 3 main datasets:\n",
    "\n",
    "* Train.csv is the training set, which contains data through the end of 2011.\n",
    "* Valid.csv is the validation set, which contains data from January 1, 2012 - April 30, 2012 You make predictions on this set throughout the majority of the competition. Your score on this set is used to create the public leaderboard.\n",
    "* Test.csv is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition.\n",
    "\n",
    "\n",
    "# 3. Evaluation\n",
    "\n",
    "The evaluation metric for this competition is the RMSLE (root mean squared log error) between the actual and predicted auction prices.\n",
    "\n",
    "For more Evaluation of this project check: https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation\n",
    "\n",
    "NOTE: The goal for most regression evaluation metrics is to minimize error. For example, our goal for this project wil be to build a machine learning model which minimises RMSLE.\n",
    "\n",
    "\n",
    "# 4. Features\n",
    "\n",
    "Kaggle provides a data dictionary detailing all the features of the datasets. you an view this data dictionary on Google sheet:\n",
    "https://docs.google.com/spreadsheets/d/18ly-bLR8sbDJLITkWG7ozKm8l3RyieQ2Fpgix-beSYI/edit#gid=1021421956"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>SalePrice</th>\n",
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       "      <th>Thumb</th>\n",
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       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
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       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
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       "<p>412698 rows × 53 columns</p>\n",
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      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "0       1139246    66000.0     999089     3157         121           3.0   \n",
       "1       1139248    57000.0     117657       77         121           3.0   \n",
       "2       1139249    10000.0     434808     7009         121           3.0   \n",
       "3       1139251    38500.0    1026470      332         121           3.0   \n",
       "4       1139253    11000.0    1057373    17311         121           3.0   \n",
       "...         ...        ...        ...      ...         ...           ...   \n",
       "412693  6333344    10000.0    1919201    21435         149           2.0   \n",
       "412694  6333345    10500.0    1882122    21436         149           2.0   \n",
       "412695  6333347    12500.0    1944213    21435         149           2.0   \n",
       "412696  6333348    10000.0    1794518    21435         149           2.0   \n",
       "412697  6333349    13000.0    1944743    21436         149           2.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter UsageBand         saledate  ...  \\\n",
       "0           2004                      68.0       Low  11/16/2006 0:00  ...   \n",
       "1           1996                    4640.0       Low   3/26/2004 0:00  ...   \n",
       "2           2001                    2838.0      High   2/26/2004 0:00  ...   \n",
       "3           2001                    3486.0      High   5/19/2011 0:00  ...   \n",
       "4           2007                     722.0    Medium   7/23/2009 0:00  ...   \n",
       "...          ...                       ...       ...              ...  ...   \n",
       "412693      2005                       NaN       NaN    3/7/2012 0:00  ...   \n",
       "412694      2005                       NaN       NaN   1/28/2012 0:00  ...   \n",
       "412695      2005                       NaN       NaN   1/28/2012 0:00  ...   \n",
       "412696      2006                       NaN       NaN    3/7/2012 0:00  ...   \n",
       "412697      2006                       NaN       NaN   1/28/2012 0:00  ...   \n",
       "\n",
       "       Undercarriage_Pad_Width         Stick_Length                Thumb  \\\n",
       "0                          NaN                  NaN                  NaN   \n",
       "1                          NaN                  NaN                  NaN   \n",
       "2                          NaN                  NaN                  NaN   \n",
       "3                          NaN                  NaN                  NaN   \n",
       "4                          NaN                  NaN                  NaN   \n",
       "...                        ...                  ...                  ...   \n",
       "412693     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "412694     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "412695     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "412696     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "412697     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "\n",
       "            Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type  \\\n",
       "0                       NaN          NaN              NaN        NaN   \n",
       "1                       NaN          NaN              NaN        NaN   \n",
       "2                       NaN          NaN              NaN        NaN   \n",
       "3                       NaN          NaN              NaN        NaN   \n",
       "4                       NaN          NaN              NaN        NaN   \n",
       "...                     ...          ...              ...        ...   \n",
       "412693  None or Unspecified       Double              NaN        NaN   \n",
       "412694  None or Unspecified       Double              NaN        NaN   \n",
       "412695  None or Unspecified       Double              NaN        NaN   \n",
       "412696  None or Unspecified       Double              NaN        NaN   \n",
       "412697  None or Unspecified       Double              NaN        NaN   \n",
       "\n",
       "       Travel_Controls Differential_Type Steering_Controls  \n",
       "0                  NaN          Standard      Conventional  \n",
       "1                  NaN          Standard      Conventional  \n",
       "2                  NaN               NaN               NaN  \n",
       "3                  NaN               NaN               NaN  \n",
       "4                  NaN               NaN               NaN  \n",
       "...                ...               ...               ...  \n",
       "412693             NaN               NaN               NaN  \n",
       "412694             NaN               NaN               NaN  \n",
       "412695             NaN               NaN               NaN  \n",
       "412696             NaN               NaN               NaN  \n",
       "412697             NaN               NaN               NaN  \n",
       "\n",
       "[412698 rows x 53 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import training and validation sets\n",
    "df = pd.read_csv('TrainAndValid.csv', low_memory=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   saledate                  412698 non-null  object \n",
      " 10  fiModelDesc               412698 non-null  object \n",
      " 11  fiBaseModel               412698 non-null  object \n",
      " 12  fiSecondaryDesc           271971 non-null  object \n",
      " 13  fiModelSeries             58667 non-null   object \n",
      " 14  fiModelDescriptor         74816 non-null   object \n",
      " 15  ProductSize               196093 non-null  object \n",
      " 16  fiProductClassDesc        412698 non-null  object \n",
      " 17  state                     412698 non-null  object \n",
      " 18  ProductGroup              412698 non-null  object \n",
      " 19  ProductGroupDesc          412698 non-null  object \n",
      " 20  Drive_System              107087 non-null  object \n",
      " 21  Enclosure                 412364 non-null  object \n",
      " 22  Forks                     197715 non-null  object \n",
      " 23  Pad_Type                  81096 non-null   object \n",
      " 24  Ride_Control              152728 non-null  object \n",
      " 25  Stick                     81096 non-null   object \n",
      " 26  Transmission              188007 non-null  object \n",
      " 27  Turbocharged              81096 non-null   object \n",
      " 28  Blade_Extension           25983 non-null   object \n",
      " 29  Blade_Width               25983 non-null   object \n",
      " 30  Enclosure_Type            25983 non-null   object \n",
      " 31  Engine_Horsepower         25983 non-null   object \n",
      " 32  Hydraulics                330133 non-null  object \n",
      " 33  Pushblock                 25983 non-null   object \n",
      " 34  Ripper                    106945 non-null  object \n",
      " 35  Scarifier                 25994 non-null   object \n",
      " 36  Tip_Control               25983 non-null   object \n",
      " 37  Tire_Size                 97638 non-null   object \n",
      " 38  Coupler                   220679 non-null  object \n",
      " 39  Coupler_System            44974 non-null   object \n",
      " 40  Grouser_Tracks            44875 non-null   object \n",
      " 41  Hydraulics_Flow           44875 non-null   object \n",
      " 42  Track_Type                102193 non-null  object \n",
      " 43  Undercarriage_Pad_Width   102916 non-null  object \n",
      " 44  Stick_Length              102261 non-null  object \n",
      " 45  Thumb                     102332 non-null  object \n",
      " 46  Pattern_Changer           102261 non-null  object \n",
      " 47  Grouser_Type              102193 non-null  object \n",
      " 48  Backhoe_Mounting          80712 non-null   object \n",
      " 49  Blade_Type                81875 non-null   object \n",
      " 50  Travel_Controls           81877 non-null   object \n",
      " 51  Differential_Type         71564 non-null   object \n",
      " 52  Steering_Controls         71522 non-null   object \n",
      "dtypes: float64(3), int64(5), object(45)\n",
      "memory usage: 166.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                          0\n",
       "SalePrice                        0\n",
       "MachineID                        0\n",
       "ModelID                          0\n",
       "datasource                       0\n",
       "auctioneerID                 20136\n",
       "YearMade                         0\n",
       "MachineHoursCurrentMeter    265194\n",
       "UsageBand                   339028\n",
       "saledate                         0\n",
       "fiModelDesc                      0\n",
       "fiBaseModel                      0\n",
       "fiSecondaryDesc             140727\n",
       "fiModelSeries               354031\n",
       "fiModelDescriptor           337882\n",
       "ProductSize                 216605\n",
       "fiProductClassDesc               0\n",
       "state                            0\n",
       "ProductGroup                     0\n",
       "ProductGroupDesc                 0\n",
       "Drive_System                305611\n",
       "Enclosure                      334\n",
       "Forks                       214983\n",
       "Pad_Type                    331602\n",
       "Ride_Control                259970\n",
       "Stick                       331602\n",
       "Transmission                224691\n",
       "Turbocharged                331602\n",
       "Blade_Extension             386715\n",
       "Blade_Width                 386715\n",
       "Enclosure_Type              386715\n",
       "Engine_Horsepower           386715\n",
       "Hydraulics                   82565\n",
       "Pushblock                   386715\n",
       "Ripper                      305753\n",
       "Scarifier                   386704\n",
       "Tip_Control                 386715\n",
       "Tire_Size                   315060\n",
       "Coupler                     192019\n",
       "Coupler_System              367724\n",
       "Grouser_Tracks              367823\n",
       "Hydraulics_Flow             367823\n",
       "Track_Type                  310505\n",
       "Undercarriage_Pad_Width     309782\n",
       "Stick_Length                310437\n",
       "Thumb                       310366\n",
       "Pattern_Changer             310437\n",
       "Grouser_Type                310505\n",
       "Backhoe_Mounting            331986\n",
       "Blade_Type                  330823\n",
       "Travel_Controls             330821\n",
       "Differential_Type           341134\n",
       "Steering_Controls           341176\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SalesID', 'SalePrice', 'MachineID', 'ModelID', 'datasource',\n",
       "       'auctioneerID', 'YearMade', 'MachineHoursCurrentMeter', 'UsageBand',\n",
       "       'saledate', 'fiModelDesc', 'fiBaseModel', 'fiSecondaryDesc',\n",
       "       'fiModelSeries', 'fiModelDescriptor', 'ProductSize',\n",
       "       'fiProductClassDesc', 'state', 'ProductGroup', 'ProductGroupDesc',\n",
       "       'Drive_System', 'Enclosure', 'Forks', 'Pad_Type', 'Ride_Control',\n",
       "       'Stick', 'Transmission', 'Turbocharged', 'Blade_Extension',\n",
       "       'Blade_Width', 'Enclosure_Type', 'Engine_Horsepower', 'Hydraulics',\n",
       "       'Pushblock', 'Ripper', 'Scarifier', 'Tip_Control', 'Tire_Size',\n",
       "       'Coupler', 'Coupler_System', 'Grouser_Tracks', 'Hydraulics_Flow',\n",
       "       'Track_Type', 'Undercarriage_Pad_Width', 'Stick_Length', 'Thumb',\n",
       "       'Pattern_Changer', 'Grouser_Type', 'Backhoe_Mounting', 'Blade_Type',\n",
       "       'Travel_Controls', 'Differential_Type', 'Steering_Controls'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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]);"
   ]
  },
  {
   "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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "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": [
    "df.SalePrice.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Parsing dates on (Time series) issues\n",
    "\n",
    "When we work with time series data, we want to enrich the time & date component as much as possible.\n",
    "\n",
    "We can do that by telling pandas which of our columns has dates in it usinf the `parse_data` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data again but this time parse dates\n",
    "df = pd.read_csv('TrainAndValid.csv',\n",
    "                low_memory=False,\n",
    "               parse_dates=['saledate']) #this will make d dates to appear in a good format \"11/16/2006 0:00\" to \"2006-11-16\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9a9fdc1e88>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1139246</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>999089</td>\n",
       "      <td>3157</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2006-11-16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1139248</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>117657</td>\n",
       "      <td>77</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2004-03-26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1139249</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>434808</td>\n",
       "      <td>7009</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2004-02-26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1139251</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>1026470</td>\n",
       "      <td>332</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2011-05-19</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1139253</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1057373</td>\n",
       "      <td>17311</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>722.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2009-07-23</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1139246    66000.0     999089     3157         121           3.0      2004   \n",
       "1  1139248    57000.0     117657       77         121           3.0      1996   \n",
       "2  1139249    10000.0     434808     7009         121           3.0      2001   \n",
       "3  1139251    38500.0    1026470      332         121           3.0      2001   \n",
       "4  1139253    11000.0    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate  ... Undercarriage_Pad_Width  \\\n",
       "0                      68.0       Low 2006-11-16  ...                     NaN   \n",
       "1                    4640.0       Low 2004-03-26  ...                     NaN   \n",
       "2                    2838.0      High 2004-02-26  ...                     NaN   \n",
       "3                    3486.0      High 2011-05-19  ...                     NaN   \n",
       "4                     722.0    Medium 2009-07-23  ...                     NaN   \n",
       "\n",
       "  Stick_Length Thumb Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type  \\\n",
       "0          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "1          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "2          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "3          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "4          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "\n",
       "  Travel_Controls Differential_Type Steering_Controls  \n",
       "0             NaN          Standard      Conventional  \n",
       "1             NaN          Standard      Conventional  \n",
       "2             NaN               NaN               NaN  \n",
       "3             NaN               NaN               NaN  \n",
       "4             NaN               NaN               NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['saledate'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sort DataFrame by saledate\n",
    "\n",
    "When working with time series data, it's a good idea to sort it by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort DataFrame in date order\n",
    "df.sort_values(by=['saledate'], inplace=True, ascending=True)\n",
    "df['saledate'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205615</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-17</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274835</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141296</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212552</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>62755</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>...</td>\n",
       "      <td>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>Lever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "205615  1646770     9500.0    1126363     8434         132          18.0   \n",
       "274835  1821514    14000.0    1194089    10150         132          99.0   \n",
       "141296  1505138    50000.0    1473654     4139         132          99.0   \n",
       "212552  1671174    16000.0    1327630     8591         132          99.0   \n",
       "62755   1329056    22000.0    1336053     4089         132          99.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter UsageBand   saledate  ...  \\\n",
       "205615      1974                       NaN       NaN 1989-01-17  ...   \n",
       "274835      1980                       NaN       NaN 1989-01-31  ...   \n",
       "141296      1978                       NaN       NaN 1989-01-31  ...   \n",
       "212552      1980                       NaN       NaN 1989-01-31  ...   \n",
       "62755       1984                       NaN       NaN 1989-01-31  ...   \n",
       "\n",
       "       Undercarriage_Pad_Width Stick_Length Thumb Pattern_Changer  \\\n",
       "205615                     NaN          NaN   NaN             NaN   \n",
       "274835                     NaN          NaN   NaN             NaN   \n",
       "141296                     NaN          NaN   NaN             NaN   \n",
       "212552                     NaN          NaN   NaN             NaN   \n",
       "62755                      NaN          NaN   NaN             NaN   \n",
       "\n",
       "       Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "205615          NaN  None or Unspecified   Straight  None or Unspecified   \n",
       "274835          NaN                  NaN        NaN                  NaN   \n",
       "141296          NaN  None or Unspecified   Straight  None or Unspecified   \n",
       "212552          NaN                  NaN        NaN                  NaN   \n",
       "62755           NaN  None or Unspecified        PAT                Lever   \n",
       "\n",
       "       Differential_Type Steering_Controls  \n",
       "205615               NaN               NaN  \n",
       "274835          Standard      Conventional  \n",
       "141296               NaN               NaN  \n",
       "212552          Standard      Conventional  \n",
       "62755                NaN               NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make copy of the original DataFrame\n",
    "\n",
    "We make a copy of the original dataframe so when we manipulate the copy, we'll still got our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the original DataFrame to perform edits on\n",
    "df_temp = df.copy() #it should take df we've been working on including d changes in order of saledate and make a copy of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "Name: saledate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp['saledate'].head()\n",
    "#Note: df_temp will be our temporary data we working on now!, while the \"df\" is our original data...lolz how interesting!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add datetime parameters for `saledate` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    1989\n",
       "Name: saledate, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp[:1].saledate.dt.year # dt.year is the datetimeindex year, which is the year of the date '1989'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205615   1989-01-17\n",
       " Name: saledate, dtype: datetime64[ns],\n",
       " 205615    17\n",
       " Name: saledate, dtype: int64,\n",
       " 205615    1\n",
       " Name: saledate, dtype: int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp[:1].saledate,   df_temp[:1].saledate.dt.day,  df_temp[:1].saledate.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp['saleYear'] = df_temp['saledate'].dt.year\n",
    "df_temp['saleMonth'] = df_temp['saledate'].dt.month\n",
    "df_temp['saleDay'] = df_temp['saledate'].dt.day\n",
    "df_temp['saleDayOfWeek'] = df_temp['saledate'].dt.dayofweek\n",
    "df_temp['saleDayOfYear'] = df_temp['saledate'].dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>saledate</th>\n",
       "      <td>1989-01-17 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDesc</th>\n",
       "      <td>TD20</td>\n",
       "      <td>A66</td>\n",
       "      <td>D7G</td>\n",
       "      <td>A62</td>\n",
       "      <td>D3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>TD20</td>\n",
       "      <td>A66</td>\n",
       "      <td>D7</td>\n",
       "      <td>A62</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelSeries</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDescriptor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductSize</th>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Track Type Tractor, Dozer - 105.0 to 130.0 Hor...</td>\n",
       "      <td>Wheel Loader - 120.0 to 135.0 Horsepower</td>\n",
       "      <td>Track Type Tractor, Dozer - 190.0 to 260.0 Hor...</td>\n",
       "      <td>Wheel Loader - Unidentified</td>\n",
       "      <td>Track Type Tractor, Dozer - 20.0 to 75.0 Horse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drive_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>OROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pad_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ride_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <td>Direct Drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turbocharged</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Extension</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine_Horsepower</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics</th>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pushblock</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ripper</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarifier</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tip_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tire_Size</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Tracks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics_Flow</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Track_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick_Length</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thumb</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type</th>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleYear</th>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDay</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfYear</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     205615  \\\n",
       "SalesID                                                             1646770   \n",
       "SalePrice                                                              9500   \n",
       "MachineID                                                           1126363   \n",
       "ModelID                                                                8434   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             18   \n",
       "YearMade                                                               1974   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                1989-01-17 00:00:00   \n",
       "fiModelDesc                                                            TD20   \n",
       "fiBaseModel                                                            TD20   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                          Medium   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 105.0 to 130.0 Hor...   \n",
       "state                                                                 Texas   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                   Direct Drive   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \n",
       "Coupler_System                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  17   \n",
       "saleDayOfWeek                                                             1   \n",
       "saleDayOfYear                                                            17   \n",
       "\n",
       "                                                            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",
       "saledate                                       1989-01-31 00:00:00   \n",
       "fiModelDesc                                                    A66   \n",
       "fiBaseModel                                                    A66   \n",
       "fiSecondaryDesc                                                NaN   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                    NaN   \n",
       "fiProductClassDesc        Wheel Loader - 120.0 to 135.0 Horsepower   \n",
       "state                                                      Florida   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                                    OROPS   \n",
       "Forks                                          None or Unspecified   \n",
       "Pad_Type                                                       NaN   \n",
       "Ride_Control                                   None or Unspecified   \n",
       "Stick                                                          NaN   \n",
       "Transmission                                                   NaN   \n",
       "Turbocharged                                                   NaN   \n",
       "Blade_Extension                                                NaN   \n",
       "Blade_Width                                                    NaN   \n",
       "Enclosure_Type                                                 NaN   \n",
       "Engine_Horsepower                                              NaN   \n",
       "Hydraulics                                                 2 Valve   \n",
       "Pushblock                                                      NaN   \n",
       "Ripper                                                         NaN   \n",
       "Scarifier                                                      NaN   \n",
       "Tip_Control                                                    NaN   \n",
       "Tire_Size                                      None or Unspecified   \n",
       "Coupler                                        None or Unspecified   \n",
       "Coupler_System                                                 NaN   \n",
       "Grouser_Tracks                                                 NaN   \n",
       "Hydraulics_Flow                                                NaN   \n",
       "Track_Type                                                     NaN   \n",
       "Undercarriage_Pad_Width                                        NaN   \n",
       "Stick_Length                                                   NaN   \n",
       "Thumb                                                          NaN   \n",
       "Pattern_Changer                                                NaN   \n",
       "Grouser_Type                                                   NaN   \n",
       "Backhoe_Mounting                                               NaN   \n",
       "Blade_Type                                                     NaN   \n",
       "Travel_Controls                                                NaN   \n",
       "Differential_Type                                         Standard   \n",
       "Steering_Controls                                     Conventional   \n",
       "saleYear                                                      1989   \n",
       "saleMonth                                                        1   \n",
       "saleDay                                                         31   \n",
       "saleDayOfWeek                                                    1   \n",
       "saleDayOfYear                                                   31   \n",
       "\n",
       "                                                                     141296  \\\n",
       "SalesID                                                             1505138   \n",
       "SalePrice                                                             50000   \n",
       "MachineID                                                           1473654   \n",
       "ModelID                                                                4139   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             99   \n",
       "YearMade                                                               1978   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                1989-01-31 00:00:00   \n",
       "fiModelDesc                                                             D7G   \n",
       "fiBaseModel                                                              D7   \n",
       "fiSecondaryDesc                                                           G   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                           Large   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 190.0 to 260.0 Hor...   \n",
       "state                                                               Florida   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                       Standard   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \n",
       "Coupler_System                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  31   \n",
       "saleDayOfWeek                                                             1   \n",
       "saleDayOfYear                                                            31   \n",
       "\n",
       "                                               212552  \\\n",
       "SalesID                                       1671174   \n",
       "SalePrice                                       16000   \n",
       "MachineID                                     1327630   \n",
       "ModelID                                          8591   \n",
       "datasource                                        132   \n",
       "auctioneerID                                       99   \n",
       "YearMade                                         1980   \n",
       "MachineHoursCurrentMeter                          NaN   \n",
       "UsageBand                                         NaN   \n",
       "saledate                          1989-01-31 00:00:00   \n",
       "fiModelDesc                                       A62   \n",
       "fiBaseModel                                       A62   \n",
       "fiSecondaryDesc                                   NaN   \n",
       "fiModelSeries                                     NaN   \n",
       "fiModelDescriptor                                 NaN   \n",
       "ProductSize                                       NaN   \n",
       "fiProductClassDesc        Wheel Loader - Unidentified   \n",
       "state                                         Florida   \n",
       "ProductGroup                                       WL   \n",
       "ProductGroupDesc                         Wheel Loader   \n",
       "Drive_System                                      NaN   \n",
       "Enclosure                                       EROPS   \n",
       "Forks                             None or Unspecified   \n",
       "Pad_Type                                          NaN   \n",
       "Ride_Control                      None or Unspecified   \n",
       "Stick                                             NaN   \n",
       "Transmission                                      NaN   \n",
       "Turbocharged                                      NaN   \n",
       "Blade_Extension                                   NaN   \n",
       "Blade_Width                                       NaN   \n",
       "Enclosure_Type                                    NaN   \n",
       "Engine_Horsepower                                 NaN   \n",
       "Hydraulics                                    2 Valve   \n",
       "Pushblock                                         NaN   \n",
       "Ripper                                            NaN   \n",
       "Scarifier                                         NaN   \n",
       "Tip_Control                                       NaN   \n",
       "Tire_Size                         None or Unspecified   \n",
       "Coupler                           None or Unspecified   \n",
       "Coupler_System                                    NaN   \n",
       "Grouser_Tracks                                    NaN   \n",
       "Hydraulics_Flow                                   NaN   \n",
       "Track_Type                                        NaN   \n",
       "Undercarriage_Pad_Width                           NaN   \n",
       "Stick_Length                                      NaN   \n",
       "Thumb                                             NaN   \n",
       "Pattern_Changer                                   NaN   \n",
       "Grouser_Type                                      NaN   \n",
       "Backhoe_Mounting                                  NaN   \n",
       "Blade_Type                                        NaN   \n",
       "Travel_Controls                                   NaN   \n",
       "Differential_Type                            Standard   \n",
       "Steering_Controls                        Conventional   \n",
       "saleYear                                         1989   \n",
       "saleMonth                                           1   \n",
       "saleDay                                            31   \n",
       "saleDayOfWeek                                       1   \n",
       "saleDayOfYear                                      31   \n",
       "\n",
       "                                                                     62755   \n",
       "SalesID                                                             1329056  \n",
       "SalePrice                                                             22000  \n",
       "MachineID                                                           1336053  \n",
       "ModelID                                                                4089  \n",
       "datasource                                                              132  \n",
       "auctioneerID                                                             99  \n",
       "YearMade                                                               1984  \n",
       "MachineHoursCurrentMeter                                                NaN  \n",
       "UsageBand                                                               NaN  \n",
       "saledate                                                1989-01-31 00:00:00  \n",
       "fiModelDesc                                                             D3B  \n",
       "fiBaseModel                                                              D3  \n",
       "fiSecondaryDesc                                                           B  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 20.0 to 75.0 Horse...  \n",
       "state                                                               Florida  \n",
       "ProductGroup                                                            TTT  \n",
       "ProductGroupDesc                                        Track Type Tractors  \n",
       "Drive_System                                                            NaN  \n",
       "Enclosure                                                             OROPS  \n",
       "Forks                                                                   NaN  \n",
       "Pad_Type                                                                NaN  \n",
       "Ride_Control                                                            NaN  \n",
       "Stick                                                                   NaN  \n",
       "Transmission                                                       Standard  \n",
       "Turbocharged                                                            NaN  \n",
       "Blade_Extension                                                         NaN  \n",
       "Blade_Width                                                             NaN  \n",
       "Enclosure_Type                                                          NaN  \n",
       "Engine_Horsepower                                                       NaN  \n",
       "Hydraulics                                                          2 Valve  \n",
       "Pushblock                                                               NaN  \n",
       "Ripper                                                  None or Unspecified  \n",
       "Scarifier                                                               NaN  \n",
       "Tip_Control                                                             NaN  \n",
       "Tire_Size                                                               NaN  \n",
       "Coupler                                                                 NaN  \n",
       "Coupler_System                                                          NaN  \n",
       "Grouser_Tracks                                                          NaN  \n",
       "Hydraulics_Flow                                                         NaN  \n",
       "Track_Type                                                              NaN  \n",
       "Undercarriage_Pad_Width                                                 NaN  \n",
       "Stick_Length                                                            NaN  \n",
       "Thumb                                                                   NaN  \n",
       "Pattern_Changer                                                         NaN  \n",
       "Grouser_Type                                                            NaN  \n",
       "Backhoe_Mounting                                        None or Unspecified  \n",
       "Blade_Type                                                              PAT  \n",
       "Travel_Controls                                                       Lever  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  \n",
       "saleYear                                                               1989  \n",
       "saleMonth                                                                 1  \n",
       "saleDay                                                                  31  \n",
       "saleDayOfWeek                                                             1  \n",
       "saleDayOfYear                                                            31  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we've enriched our DataFrame with date time features, we can remove 'saledate'\n",
    "df_temp.drop('saledate', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the values of different columns\n",
    "df_temp.state.value_counts() # Florida has the highest sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205615</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD20</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274835</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A66</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141296</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D7G</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212552</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A62</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62755</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D3B</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>Lever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "205615  1646770     9500.0    1126363     8434         132          18.0   \n",
       "274835  1821514    14000.0    1194089    10150         132          99.0   \n",
       "141296  1505138    50000.0    1473654     4139         132          99.0   \n",
       "212552  1671174    16000.0    1327630     8591         132          99.0   \n",
       "62755   1329056    22000.0    1336053     4089         132          99.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter UsageBand fiModelDesc  ...  \\\n",
       "205615      1974                       NaN       NaN        TD20  ...   \n",
       "274835      1980                       NaN       NaN         A66  ...   \n",
       "141296      1978                       NaN       NaN         D7G  ...   \n",
       "212552      1980                       NaN       NaN         A62  ...   \n",
       "62755       1984                       NaN       NaN         D3B  ...   \n",
       "\n",
       "           Backhoe_Mounting Blade_Type      Travel_Controls Differential_Type  \\\n",
       "205615  None or Unspecified   Straight  None or Unspecified               NaN   \n",
       "274835                  NaN        NaN                  NaN          Standard   \n",
       "141296  None or Unspecified   Straight  None or Unspecified               NaN   \n",
       "212552                  NaN        NaN                  NaN          Standard   \n",
       "62755   None or Unspecified        PAT                Lever               NaN   \n",
       "\n",
       "       Steering_Controls saleYear saleMonth saleDay saleDayOfWeek  \\\n",
       "205615               NaN     1989         1      17             1   \n",
       "274835      Conventional     1989         1      31             1   \n",
       "141296               NaN     1989         1      31             1   \n",
       "212552      Conventional     1989         1      31             1   \n",
       "62755                NaN     1989         1      31             1   \n",
       "\n",
       "       saleDayOfYear  \n",
       "205615            17  \n",
       "274835            31  \n",
       "141296            31  \n",
       "212552            31  \n",
       "62755             31  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Modelling\n",
    "\n",
    "we've done enough EDA (we could always do more) but lets start to do some model-driven EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Low'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-97d56a2c2bd1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m                              random_state=42) # random_state=42 can act as np.random.seed(42) they kinda similar\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_temp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_temp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    293\u001b[0m         \"\"\"\n\u001b[0;32m    294\u001b[0m         \u001b[1;31m# Validate or convert input data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 295\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    296\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    529\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# Let's build a machine learning model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-1, #our computr procssor needs 2 perform alot with speed cos of much rows we have\n",
    "                             random_state=42) # random_state=42 can act as np.random.seed(42) they kinda similar\n",
    "\n",
    "model.fit(df_temp.drop('SalePrice', axis=1), df_temp['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting String to categories\n",
    "\n",
    "One way we can turn all of our into numbers is by converting them into pandas categories.\n",
    "\n",
    "We can check the different datatypes compatible with pandas here: https://pandas.pydata.org/pandas-docs/stable/reference/general_utility_functions.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.api.types.is_string_dtype(df_temp['UsageBand']) # yes \"UsageBand\" is a column of  string type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the columns which contains strings\n",
    "for label, content in df_temp.items():\n",
    "    if pd.api.types.is_string_dtype(content): # we are finding all the columns with strings\n",
    "        print(label)\n",
    "        \n",
    "# below are the columns of strings data type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will turn all of the string value into category values\n",
    "for label, content in df_temp.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        df_temp[label] = content.astype('category').cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# accessing a category to see if its a numeric\n",
    "df_temp['state'].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to pandas Category we now have a way to access all of our data in the form of Numbers\n",
    "\n",
    "But we still have a bunch of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check missing data\n",
    "\n",
    "df_temp.isnull().sum()/len(df_temp) # to check there % of missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Prepocessed data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export current temp dataframe, let me save it in my desktop\n",
    "df_temp.to_csv('desktop/train_temp.csv',\n",
    "              index=False) # index should be false so we won't come out with funny index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD20</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A66</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D7G</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A62</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D3B</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>Lever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand fiModelDesc  ...     Backhoe_Mounting  \\\n",
       "0                       NaN       NaN        TD20  ...  None or Unspecified   \n",
       "1                       NaN       NaN         A66  ...                  NaN   \n",
       "2                       NaN       NaN         D7G  ...  None or Unspecified   \n",
       "3                       NaN       NaN         A62  ...                  NaN   \n",
       "4                       NaN       NaN         D3B  ...  None or Unspecified   \n",
       "\n",
       "  Blade_Type      Travel_Controls Differential_Type Steering_Controls  \\\n",
       "0   Straight  None or Unspecified               NaN               NaN   \n",
       "1        NaN                  NaN          Standard      Conventional   \n",
       "2   Straight  None or Unspecified               NaN               NaN   \n",
       "3        NaN                  NaN          Standard      Conventional   \n",
       "4        PAT                Lever               NaN               NaN   \n",
       "\n",
       "  saleYear saleMonth saleDay saleDayOfWeek saleDayOfYear  \n",
       "0     1989         1      17             1            17  \n",
       "1     1989         1      31             1            31  \n",
       "2     1989         1      31             1            31  \n",
       "3     1989         1      31             1            31  \n",
       "4     1989         1      31             1            31  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import prepocessed data\n",
    "df_temp = pd.read_csv('desktop/train_temp.csv',\n",
    "                     low_memory=False)\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill Missing Values\n",
    "\n",
    "### Fill Numeric missing value first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "# let's see all numeric columns\n",
    "for label, content in df_temp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auctioneerID\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "# Check for which numeric columns have null values\n",
    "for label, content in df_temp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum(): # the numeric columns with null values\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the numeric row wit their median\n",
    "for label, content in df_temp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            # Add a binary which tells us if the data was missing or not\n",
    "            df_temp[label+'_is_missing'] = pd.isnull(content) # also creating a new column\n",
    "            # Fill missing numeric values with median\n",
    "            df_temp[label] = content.fillna(content.median()) # we use median cos median is more robust than mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100.0, 999100.8991008991, 100.0, 100.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's demostrate how median is more robust than mean\n",
    "hundreds = np.full((1000), 100)\n",
    "hundreds_billion = np.append(hundreds, 1000000000)\n",
    "np.mean(hundreds), np.mean(hundreds_billion), np.median(hundreds), np.median(hundreds_billion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets check if there's any null numeric values\n",
    "for label, content, in df_temp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    392562\n",
       "True      20136\n",
       "Name: auctioneerID_is_missing, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see how many examples were missing\n",
    "df_temp.auctioneerID_is_missing.value_counts()\n",
    "\n",
    "#Note: its means that we just filled \"20136\" value in the \"auctioneerID\" that was missing column and not\n",
    "# in the\"auctioneerID_is_missing\" cos we just created it, but got \"auctioneerID\" filled with his median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                                     0\n",
       "SalePrice                                   0\n",
       "MachineID                                   0\n",
       "ModelID                                     0\n",
       "datasource                                  0\n",
       "auctioneerID                                0\n",
       "YearMade                                    0\n",
       "MachineHoursCurrentMeter                    0\n",
       "UsageBand                              339028\n",
       "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",
       "auctioneerID_is_missing                     0\n",
       "MachineHoursCurrentMeter_is_missing         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling and turning categorical variables into numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp['Steering_Controls'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 59 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                         412698 non-null  float64\n",
      " 6   YearMade                             412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter             412698 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",
      " 57  auctioneerID_is_missing              412698 non-null  bool   \n",
      " 58  MachineHoursCurrentMeter_is_missing  412698 non-null  bool   \n",
      "dtypes: bool(2), float64(3), int64(10), object(44)\n",
      "memory usage: 180.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_temp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling and turning categorical variables into number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 aren't numeric, e.g they might be other data types but not numeric\n",
    "for label, content in df_temp.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43,  8,  8, ...,  4,  4,  4], dtype=int8)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we trying to show an example of what 'state' category is which is in 'int' and then use the code to convert all to category\n",
    "pd.Categorical(df_temp['state']).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1, -1, -1, ..., -1, -1, -1], dtype=int8),\n",
       " array([0, 0, 0, ..., 0, 0, 0], dtype=int8))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_temp['UsageBand']).codes,  pd.Categorical(df_temp['UsageBand']).codes+1 #u see the difference cos of +1 ????"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn categorical variables into numbers and fill missing\n",
    "for label, content in df_temp.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        # Add binary column to indicate whether sample had missing value\n",
    "        df_temp[label+'is_missing'] = pd.isnull(content)\n",
    "        # Turn Categories into numbers and add +1\n",
    "        df_temp[label] = pd.Categorical(content).codes + 1 # we adding +1 incase we have -1 in the catorgories\n",
    "        \n",
    "#NOTE: since we turing them to category if they have a missing value that row will -1 but we ading +1 , so all the missing\n",
    "#value will be 0, and all the numbers in the catergories are goiing to be sum number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Columns: 103 entries, SalesID to Steering_Controlsis_missing\n",
      "dtypes: bool(46), float64(3), int16(4), int64(10), int8(40)\n",
      "memory usage: 77.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1646770</td>\n",
       "      <td>1821514</td>\n",
       "      <td>1505138</td>\n",
       "      <td>1671174</td>\n",
       "      <td>1329056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>9500</td>\n",
       "      <td>14000</td>\n",
       "      <td>50000</td>\n",
       "      <td>16000</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1126363</td>\n",
       "      <td>1194089</td>\n",
       "      <td>1473654</td>\n",
       "      <td>1327630</td>\n",
       "      <td>1336053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>8434</td>\n",
       "      <td>10150</td>\n",
       "      <td>4139</td>\n",
       "      <td>8591</td>\n",
       "      <td>4089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mountingis_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Typeis_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controlsis_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Typeis_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controlsis_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0        1        2        3        4\n",
       "SalesID                      1646770  1821514  1505138  1671174  1329056\n",
       "SalePrice                       9500    14000    50000    16000    22000\n",
       "MachineID                    1126363  1194089  1473654  1327630  1336053\n",
       "ModelID                         8434    10150     4139     8591     4089\n",
       "datasource                       132      132      132      132      132\n",
       "...                              ...      ...      ...      ...      ...\n",
       "Backhoe_Mountingis_missing     False     True    False     True    False\n",
       "Blade_Typeis_missing           False     True    False     True    False\n",
       "Travel_Controlsis_missing      False     True    False     True    False\n",
       "Differential_Typeis_missing     True    False     True    False     True\n",
       "Steering_Controlsis_missing     True    False     True    False     True\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                        0\n",
       "SalePrice                      0\n",
       "MachineID                      0\n",
       "ModelID                        0\n",
       "datasource                     0\n",
       "                              ..\n",
       "Backhoe_Mountingis_missing     0\n",
       "Blade_Typeis_missing           0\n",
       "Travel_Controlsis_missing      0\n",
       "Differential_Typeis_missing    0\n",
       "Steering_Controlsis_missing    0\n",
       "Length: 103, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that all of our data is numeric as well our dataframe has no missing values, we should be build a machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Widthis_missing</th>\n",
       "      <th>Stick_Lengthis_missing</th>\n",
       "      <th>Thumbis_missing</th>\n",
       "      <th>Pattern_Changeris_missing</th>\n",
       "      <th>Grouser_Typeis_missing</th>\n",
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       "      <th>Travel_Controlsis_missing</th>\n",
       "      <th>Differential_Typeis_missing</th>\n",
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       "      <td>True</td>\n",
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       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
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       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
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       "      <td>99.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  ...  \\\n",
       "0                       0.0          0         4593  ...   \n",
       "1                       0.0          0         1820  ...   \n",
       "2                       0.0          0         2348  ...   \n",
       "3                       0.0          0         1819  ...   \n",
       "4                       0.0          0         2119  ...   \n",
       "\n",
       "   Undercarriage_Pad_Widthis_missing  Stick_Lengthis_missing  Thumbis_missing  \\\n",
       "0                               True                    True             True   \n",
       "1                               True                    True             True   \n",
       "2                               True                    True             True   \n",
       "3                               True                    True             True   \n",
       "4                               True                    True             True   \n",
       "\n",
       "   Pattern_Changeris_missing  Grouser_Typeis_missing  \\\n",
       "0                       True                    True   \n",
       "1                       True                    True   \n",
       "2                       True                    True   \n",
       "3                       True                    True   \n",
       "4                       True                    True   \n",
       "\n",
       "   Backhoe_Mountingis_missing  Blade_Typeis_missing  \\\n",
       "0                       False                 False   \n",
       "1                        True                  True   \n",
       "2                       False                 False   \n",
       "3                        True                  True   \n",
       "4                       False                 False   \n",
       "\n",
       "   Travel_Controlsis_missing  Differential_Typeis_missing  \\\n",
       "0                      False                         True   \n",
       "1                       True                        False   \n",
       "2                      False                         True   \n",
       "3                       True                        False   \n",
       "4                      False                         True   \n",
       "\n",
       "   Steering_Controlsis_missing  \n",
       "0                         True  \n",
       "1                        False  \n",
       "2                         True  \n",
       "3                        False  \n",
       "4                         True  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412698"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 14min 47s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=-1, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "# the above \"%time\" will calculate how much time this perticular cell takes time to run\n",
    "# Instantiate model\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-1,\n",
    "                             random_state=42)\n",
    "\n",
    "#Fit the model\n",
    "model.fit(df_temp.drop('SalePrice', axis=1), df_temp['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.score(df_temp.drop('SalePrice', axis=1), df_temp['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: why doesnt the above metrics hold water? (why isn't the metric reliable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data into train/validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1989\n",
       "1         1989\n",
       "2         1989\n",
       "3         1989\n",
       "4         1989\n",
       "          ... \n",
       "412693    2012\n",
       "412694    2012\n",
       "412695    2012\n",
       "412696    2012\n",
       "412697    2012\n",
       "Name: saleYear, Length: 412698, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.saleYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.saleYear.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401125, 11573)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note: train_set should end 2011, while validation_set should start from jan 1 2012 - april 30 2012 on your dataframe\n",
    "# what we did we just divide the year into two split but using same dataframe\n",
    "\n",
    "# Split data into training and validation\n",
    "df_train = df_temp[df_temp.saleYear != 2012]\n",
    "df_val = df_temp[df_temp.saleYear == 2012]\n",
    "\n",
    "len(df_train), len(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 102), (401125,), (11573, 102), (11573,))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into X and y\n",
    "X_train, y_train = df_train.drop('SalePrice', axis=1), df_train['SalePrice']\n",
    "X_valid, y_valid = df_val.drop('SalePrice', axis=1), df_val['SalePrice']\n",
    "\n",
    "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          9500.0\n",
       "1         14000.0\n",
       "2         50000.0\n",
       "3         16000.0\n",
       "4         22000.0\n",
       "           ...   \n",
       "401120    29000.0\n",
       "401121    11000.0\n",
       "401122    11000.0\n",
       "401123    18000.0\n",
       "401124    13500.0\n",
       "Name: SalePrice, Length: 401125, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Evaluation funtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Evaluation Funtion (The Competition uses RMSLE)\n",
    "\n",
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, r2_score\n",
    "\n",
    "def rmsle(y_test, y_preds):\n",
    "    '''\n",
    "    calculate root mean squared log error predictions and true labels.\n",
    "    '''\n",
    "    return np.sqrt(mean_squared_log_error(y_test, y_preds))\n",
    "\n",
    "# Create function to evaluate model on a few different levels\n",
    "def show_scores(model):\n",
    "    train_preds = model.predict(X_train)\n",
    "    val_preds = model.predict(X_valid)\n",
    "    scores = {'Training MAE': mean_absolute_error(y_train, train_preds),\n",
    "             'Valid MAE': mean_absolute_error(y_valid, val_preds),\n",
    "             'Training RMSLE': rmsle(y_train, train_preds), # we have passed the two parameter into it\n",
    "             'Valid RMSLE': rmsle(y_valid, val_preds),\n",
    "             'Training R^2': r2_score(y_train, train_preds),\n",
    "             'Valid R^2': r2_score(y_valid, val_preds)}\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing our model on a subset (to tune the hyperparameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This takes far too long.. for experimenting\n",
    "\n",
    "# %%time\n",
    "# # We wanna decrease the amount of time it takes for us to perform the experiment, unlike the above that took 16mint+\n",
    "# model = RandomForestRegressor(n_jobs=-1,\n",
    "#                              random_state=42)\n",
    "\n",
    "# model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401125"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ## By training your model a bit fasters in other of subset them\n",
    "\n",
    "we can use `max_sample` to kinda reduce our data, max_samples default is NONE which is X_train.shape[0] and `n_estimate` default is 100, \n",
    "*   so X_train.shape[0] * 100 = 40112500 will run\n",
    "    but if we want max_samples to try only 10,000 examples, instead of 400k+,\n",
    "    cos len(X_train) is 401125 cos we are trying to \n",
    "    reduce the example of what it will run thats why we are using max_samples\n",
    "    \n",
    "    set > max_sample = 10000 \n",
    "    dont 4Get dat the default n_estimator is 100\n",
    "    \n",
    "    10000 * 100 = 1000000 examples will be runned!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=10000, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=-1, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change max_samples value\n",
    "model = RandomForestRegressor(n_jobs=-1,\n",
    "                              random_state=42,\n",
    "                             max_samples=10000)\n",
    "\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 37.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=10000, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=-1, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Cutting down the max number of samples each estimator can see improves training time\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5561.2988092240585,\n",
       " 'Valid MAE': 7177.26365505919,\n",
       " 'Training RMSLE': 0.257745378256977,\n",
       " 'Valid RMSLE': 0.29362638671089003,\n",
       " 'Training R^2': 0.8606658995199189,\n",
       " 'Valid R^2': 0.8320374995090507}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_scores(model) # dont forget we are training on a subet of 100k+ whch we used max_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tunning with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  2.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 35s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100, n_jobs=-1,\n",
       "                                                   oob_score=False,...\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'max_features': [0.5, 1, 'sqrt',\n",
       "                                                         'auto'],\n",
       "                                        'max_samples': [10000],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Different RandomForestRegressor hyperparameter\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",
    "          'max_samples': [10000]} # only on 10K samples\n",
    "\n",
    "# Instantiates RandomizedSearchCv model\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1,\n",
    "                                                    random_state=42),\n",
    "                              param_distributions=rf_grid,\n",
    "                              n_iter=2, # we reduce the iteration to 2, cos the higher the number d time it takes to load\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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 20,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 11,\n",
       " 'max_samples': 10000,\n",
       " 'max_features': 0.5,\n",
       " 'max_depth': 10}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best model Hyperparameters\n",
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5561.2988092240585,\n",
       " 'Valid MAE': 7177.26365505919,\n",
       " 'Training RMSLE': 0.257745378256977,\n",
       " 'Valid RMSLE': 0.29362638671089003,\n",
       " 'Training R^2': 0.8606658995199189,\n",
       " 'Valid R^2': 0.8320374995090507}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluates the RandomizedSearchCV model\n",
    "show_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a model with the best hyperparameters\n",
    "\n",
    "**Note:** These were found after 100 iterations of `RandomizedSearchCV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 44s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features=0.5, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=14, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=40, n_jobs=-1, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Most ideal Hyperparameters\n",
    "ideal_model = RandomForestRegressor(n_estimators=40,\n",
    "                                    min_samples_leaf=1,\n",
    "                                    min_samples_split=14,\n",
    "                                    max_features=0.5,\n",
    "                                    n_jobs=-1,\n",
    "                                    random_state=42,\n",
    "                                    max_samples=None) # we wana train all the data\n",
    "\n",
    "# Fit the idea model\n",
    "ideal_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 2953.8161137163484,\n",
       " 'Valid MAE': 5951.247761444453,\n",
       " 'Training RMSLE': 0.14469006962371858,\n",
       " 'Valid RMSLE': 0.24524163989538328,\n",
       " 'Training R^2': 0.9588145522577225,\n",
       " 'Valid R^2': 0.8818019502450094}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score for ideal model (trained on all the data)\n",
    "show_scores(ideal_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5561.2988092240585,\n",
       " 'Valid MAE': 7177.26365505919,\n",
       " 'Training RMSLE': 0.257745378256977,\n",
       " 'Valid RMSLE': 0.29362638671089003,\n",
       " 'Training R^2': 0.8606658995199189,\n",
       " 'Valid R^2': 0.8320374995090507}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score on rs_model (only trained on ~10,000 examples)\n",
    "show_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make predictions on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the test data\n",
    "\n",
    "df_test = pd.read_csv('Test.csv',\n",
    "                     low_memory=False,\n",
    "                     parse_dates=['saledate'])\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Low'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-203-a447d060caa5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Make predicions on the test data set\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest_preds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mideal_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#we predicting df_test against ideal model cos df_test doesnt have sale price\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    764\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    765\u001b[0m         \u001b[1;31m# Check data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 766\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    767\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    768\u001b[0m         \u001b[1;31m# Assign chunk of trees to jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    410\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 412\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    378\u001b[0m         \u001b[1;34m\"\"\"Validate X whenever one tries to predict, apply, predict_proba\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    379\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 380\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    381\u001b[0m             if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[0;32m    382\u001b[0m                                 X.indptr.dtype != np.intc):\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    529\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# Make predicions on the test data set\n",
    "test_preds = ideal_model.predict(df_test) #we predicting df_test against ideal model cos df_test doesnt have sale price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the data (getting the test dataset in the same format as our training dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(df):\n",
    "    '''\n",
    "    performes transformer on df and returns transformed df.\n",
    "    '''\n",
    "    df['saleYear'] = df['saledate'].dt.year\n",
    "    df['saleMonth'] = df['saledate'].dt.month\n",
    "    df['saleDay'] = df['saledate'].dt.day\n",
    "    df['saleDayOfWeek'] = df['saledate'].dt.dayofweek\n",
    "    df['saleDayOfYear'] = df['saledate'].dt.dayofyear\n",
    "    \n",
    "    df.drop('saledate', axis=1, inplace=True)\n",
    "    \n",
    "    # Fill numeric rows with median\n",
    "    for label, content in df.items():\n",
    "        if pd.api.types.is_numeric_dtype(content):\n",
    "            if pd.isnull(content).sum():\n",
    "                # Add a binary which tells us if the data was missing or not\n",
    "                df[label+'_is_missing'] = pd.isnull(content)\n",
    "                # Fill missing numeric values with median\n",
    "                df[label] = content.fillna(content.median())\n",
    "                                          \n",
    "            # Filled categorical missing data and turned into numbers\n",
    "        if not pd.api.types.is_numeric_dtype(content):\n",
    "            df[label+'_is_missing'] = pd.isnull(content)\n",
    "            # Add + 1 to the category code bcos pandas encodes missing categories as -1\n",
    "            df[label] = pd.Categorical(content).codes+1\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# process the test data\n",
    "df_test = preprocess_data(df_test)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Number of features of the model must match the input. Model n_features is 102 and input n_features is 101 ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-207-fed5b0dd1474>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Make predictions on updated data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest_preds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mideal_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    764\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    765\u001b[0m         \u001b[1;31m# Check data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 766\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    767\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    768\u001b[0m         \u001b[1;31m# Assign chunk of trees to jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    410\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 412\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    389\u001b[0m                              \u001b[1;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m                              \u001b[1;34m\"input n_features is %s \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m                              % (self.n_features_, n_features))\n\u001b[0m\u001b[0;32m    392\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 102 and input n_features is 101 "
     ]
    }
   ],
   "source": [
    "# Make predictions on updated data\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>2348</td>\n",
       "      <td>713</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "0  1646770    1126363     8434         132          18.0      1974   \n",
       "1  1821514    1194089    10150         132          99.0      1980   \n",
       "2  1505138    1473654     4139         132          99.0      1978   \n",
       "3  1671174    1327630     8591         132          99.0      1980   \n",
       "4  1329056    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                       0.0          0         4593         1744  ...   \n",
       "1                       0.0          0         1820          559  ...   \n",
       "2                       0.0          0         2348          713  ...   \n",
       "3                       0.0          0         1819          558  ...   \n",
       "4                       0.0          0         2119          683  ...   \n",
       "\n",
       "   Undercarriage_Pad_Widthis_missing  Stick_Lengthis_missing  Thumbis_missing  \\\n",
       "0                               True                    True             True   \n",
       "1                               True                    True             True   \n",
       "2                               True                    True             True   \n",
       "3                               True                    True             True   \n",
       "4                               True                    True             True   \n",
       "\n",
       "   Pattern_Changeris_missing  Grouser_Typeis_missing  \\\n",
       "0                       True                    True   \n",
       "1                       True                    True   \n",
       "2                       True                    True   \n",
       "3                       True                    True   \n",
       "4                       True                    True   \n",
       "\n",
       "   Backhoe_Mountingis_missing  Blade_Typeis_missing  \\\n",
       "0                       False                 False   \n",
       "1                        True                  True   \n",
       "2                       False                 False   \n",
       "3                        True                  True   \n",
       "4                       False                 False   \n",
       "\n",
       "   Travel_Controlsis_missing  Differential_Typeis_missing  \\\n",
       "0                      False                         True   \n",
       "1                       True                        False   \n",
       "2                      False                         True   \n",
       "3                       True                        False   \n",
       "4                      False                         True   \n",
       "\n",
       "   Steering_Controlsis_missing  \n",
       "0                         True  \n",
       "1                        False  \n",
       "2                         True  \n",
       "3                        False  \n",
       "4                         True  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Backhoe_Mountingis_missing',\n",
       " 'Blade_Extensionis_missing',\n",
       " 'Blade_Typeis_missing',\n",
       " 'Blade_Widthis_missing',\n",
       " 'Coupler_Systemis_missing',\n",
       " 'Coupleris_missing',\n",
       " 'Differential_Typeis_missing',\n",
       " 'Drive_Systemis_missing',\n",
       " 'Enclosure_Typeis_missing',\n",
       " 'Enclosureis_missing',\n",
       " 'Engine_Horsepoweris_missing',\n",
       " 'Forksis_missing',\n",
       " 'Grouser_Tracksis_missing',\n",
       " 'Grouser_Typeis_missing',\n",
       " 'Hydraulics_Flowis_missing',\n",
       " 'Hydraulicsis_missing',\n",
       " 'Pad_Typeis_missing',\n",
       " 'Pattern_Changeris_missing',\n",
       " 'ProductGroupDescis_missing',\n",
       " 'ProductGroupis_missing',\n",
       " 'ProductSizeis_missing',\n",
       " 'Pushblockis_missing',\n",
       " 'Ride_Controlis_missing',\n",
       " 'Ripperis_missing',\n",
       " 'Scarifieris_missing',\n",
       " 'Steering_Controlsis_missing',\n",
       " 'Stick_Lengthis_missing',\n",
       " 'Stickis_missing',\n",
       " 'Thumbis_missing',\n",
       " 'Tip_Controlis_missing',\n",
       " 'Tire_Sizeis_missing',\n",
       " 'Track_Typeis_missing',\n",
       " 'Transmissionis_missing',\n",
       " 'Travel_Controlsis_missing',\n",
       " 'Turbochargedis_missing',\n",
       " 'Undercarriage_Pad_Widthis_missing',\n",
       " 'UsageBandis_missing',\n",
       " 'auctioneerID_is_missing',\n",
       " 'fiBaseModelis_missing',\n",
       " 'fiModelDescis_missing',\n",
       " 'fiModelDescriptoris_missing',\n",
       " 'fiModelSeriesis_missing',\n",
       " 'fiProductClassDescis_missing',\n",
       " 'fiSecondaryDescis_missing',\n",
       " 'stateis_missing'}"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can find how the columns differ using python sets\n",
    "set(X_train.columns)-set(df_test.columns) # is going to tell us which columns are incorrect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <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>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",
       "      <th>auctioneerID_is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <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",
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       "      <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>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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",
       "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": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manually adjust df_test to have auctioneerID_is_missing columns to df_test\n",
    "df_test['auctioneerID_is_missing'] = False\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finally now our test dataframe has same features as our training dataframe. we can make predictions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20614.36780887, 19897.80170658, 44852.21959446, ...,\n",
       "       14296.98620472, 22164.85757662, 31683.80063427])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we've made some predictions but they are nit yet in same format Kagke is asking for:\n",
    "https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>20614.367809</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>19897.801707</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>44852.219594</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>68346.325323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>39487.349708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12452</th>\n",
       "      <td>6643171</td>\n",
       "      <td>46466.092910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>6643173</td>\n",
       "      <td>17500.493352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>6643184</td>\n",
       "      <td>14296.986205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>6643186</td>\n",
       "      <td>22164.857577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>6643196</td>\n",
       "      <td>31683.800634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID     SalePrice\n",
       "0      1227829  20614.367809\n",
       "1      1227844  19897.801707\n",
       "2      1227847  44852.219594\n",
       "3      1227848  68346.325323\n",
       "4      1227863  39487.349708\n",
       "...        ...           ...\n",
       "12452  6643171  46466.092910\n",
       "12453  6643173  17500.493352\n",
       "12454  6643184  14296.986205\n",
       "12455  6643186  22164.857577\n",
       "12456  6643196  31683.800634\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format predictions into same formate kaggle is after\n",
    "df_preds = pd.DataFrame()\n",
    "df_preds['SalesID'] = df_test['SalesID']\n",
    "df_preds['SalePrice'] = test_preds\n",
    "df_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export prediction data to csv\n",
    "df_preds.to_csv('desktop/test_predictions.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance \n",
    "\n",
    "Features importance seeks to figure out which different attributes of the data were most importance when it comes to predicting\n",
    "the **target variable** (SalePrice)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.39445533e-02, 1.81148281e-02, 4.09167072e-02, 1.70752171e-03,\n",
       "       3.40797459e-03, 2.08200698e-01, 2.95067052e-03, 1.10113725e-03,\n",
       "       4.16122668e-02, 4.71911805e-02, 6.23815431e-02, 4.67433955e-03,\n",
       "       1.52524442e-02, 1.52517337e-01, 4.72224713e-02, 5.96817956e-03,\n",
       "       1.29351899e-03, 2.78088439e-03, 2.37248769e-03, 6.17114453e-02,\n",
       "       8.13525488e-04, 3.61873268e-05, 9.19098115e-04, 2.23170993e-04,\n",
       "       1.28102678e-03, 2.06519636e-05, 2.01477316e-03, 6.63364759e-03,\n",
       "       2.15274492e-03, 2.50178165e-03, 4.63902393e-03, 3.85873985e-03,\n",
       "       2.76062667e-03, 1.00782454e-03, 2.47969268e-04, 6.04239818e-03,\n",
       "       7.64997072e-04, 1.57100537e-02, 2.29716203e-03, 2.58372272e-03,\n",
       "       8.07637426e-04, 9.18548690e-04, 1.35656446e-03, 5.81458569e-04,\n",
       "       4.96716928e-04, 3.79552257e-04, 5.31712788e-04, 2.71823509e-03,\n",
       "       8.34294376e-04, 3.12136841e-04, 2.14075157e-04, 7.42422919e-02,\n",
       "       3.80158492e-03, 5.67641024e-03, 2.87154703e-03, 9.83349904e-03,\n",
       "       2.65470837e-04, 1.57946459e-03, 3.10058108e-04, 0.00000000e+00,\n",
       "       0.00000000e+00, 2.27421721e-03, 1.05632062e-03, 5.42819222e-03,\n",
       "       3.48484864e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.90858845e-05, 9.09490682e-06, 1.31265147e-04,\n",
       "       5.29163902e-06, 1.11952381e-04, 4.78452431e-06, 3.43582863e-04,\n",
       "       5.57068428e-06, 1.07167376e-03, 3.99179008e-03, 4.07753410e-03,\n",
       "       1.05749617e-04, 2.76528927e-03, 2.59244312e-05, 3.51888176e-04,\n",
       "       2.31519337e-03, 1.99211177e-03, 4.02034629e-03, 2.03778082e-04,\n",
       "       1.13483313e-02, 9.02551628e-04, 1.58182497e-03, 4.63243398e-05,\n",
       "       2.92071004e-04, 3.11923094e-05, 1.56873538e-04, 2.87205987e-05,\n",
       "       3.80543083e-05, 2.55045807e-04, 1.66878572e-04, 2.10341792e-04,\n",
       "       1.26024842e-04, 9.40663015e-05])"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the Feature Importance of our best model\n",
    "ideal_model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Helper function for plotting feature importance\n",
    "def plot_features(columns, importance, n=20): #n=20 means the top 20 value\n",
    "    df = (pd.DataFrame({'features': columns, \n",
    "                        'feature_importances': importance}) \n",
    "          .sort_values('feature_importances', ascending=False) # ascending from highest to lowest \n",
    "          .reset_index(drop=True))\n",
    "    # Plot the dataframe we've created\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "    ax.barh(df['features'][:n], df['feature_importances'][:20]) # [:n] is the 1st 20 \n",
    "    ax.set_ylabel('Features')\n",
    "    ax.set_xlabel('Feature importances')\n",
    "    ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features(X_train.columns, ideal_model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "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>fiModelDesc</th>\n",
       "      <th>fiBaseModel</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Widthis_missing</th>\n",
       "      <th>Stick_Lengthis_missing</th>\n",
       "      <th>Thumbis_missing</th>\n",
       "      <th>Pattern_Changeris_missing</th>\n",
       "      <th>Grouser_Typeis_missing</th>\n",
       "      <th>Backhoe_Mountingis_missing</th>\n",
       "      <th>Blade_Typeis_missing</th>\n",
       "      <th>Travel_Controlsis_missing</th>\n",
       "      <th>Differential_Typeis_missing</th>\n",
       "      <th>Steering_Controlsis_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646770</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4593</td>\n",
       "      <td>1744</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1821514</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>559</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>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",
       "      <td>2348</td>\n",
       "      <td>713</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "0  1646770    1126363     8434         132          18.0      1974   \n",
       "1  1821514    1194089    10150         132          99.0      1980   \n",
       "2  1505138    1473654     4139         132          99.0      1978   \n",
       "3  1671174    1327630     8591         132          99.0      1980   \n",
       "4  1329056    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                       0.0          0         4593         1744  ...   \n",
       "1                       0.0          0         1820          559  ...   \n",
       "2                       0.0          0         2348          713  ...   \n",
       "3                       0.0          0         1819          558  ...   \n",
       "4                       0.0          0         2119          683  ...   \n",
       "\n",
       "   Undercarriage_Pad_Widthis_missing  Stick_Lengthis_missing  Thumbis_missing  \\\n",
       "0                               True                    True             True   \n",
       "1                               True                    True             True   \n",
       "2                               True                    True             True   \n",
       "3                               True                    True             True   \n",
       "4                               True                    True             True   \n",
       "\n",
       "   Pattern_Changeris_missing  Grouser_Typeis_missing  \\\n",
       "0                       True                    True   \n",
       "1                       True                    True   \n",
       "2                       True                    True   \n",
       "3                       True                    True   \n",
       "4                       True                    True   \n",
       "\n",
       "   Backhoe_Mountingis_missing  Blade_Typeis_missing  \\\n",
       "0                       False                 False   \n",
       "1                        True                  True   \n",
       "2                       False                 False   \n",
       "3                        True                  True   \n",
       "4                       False                 False   \n",
       "\n",
       "   Travel_Controlsis_missing  Differential_Typeis_missing  \\\n",
       "0                      False                         True   \n",
       "1                       True                        False   \n",
       "2                      False                         True   \n",
       "3                       True                        False   \n",
       "4                      False                         True   \n",
       "\n",
       "   Steering_Controlsis_missing  \n",
       "0                         True  \n",
       "1                        False  \n",
       "2                         True  \n",
       "3                        False  \n",
       "4                         True  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "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": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Enclosure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "**Question to finish:** why might knowing  the feature importance  of a trained machine learning is helpfull?\n",
    "    \n",
    "* Final Challenge: ** what other machine learning models could you try on our dataset? \n",
    "Hint: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html\n",
    "checkout the regression sections of this map, aor try to look at something lik CatBoost.ai or XGBoost.ai"
   ]
  }
 ],
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