price_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "26042027",
   "metadata": {},
   "source": [
    "# 1. Problem Definition\n",
    "For this dataset, the problem we're trying to solve, or better, the question we're trying to answer is,\n",
    "\n",
    "How well can we predict the future sale price of a bulldozer, given its characteristics previous examples of how much similar bulldozers have been sold for?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248763e2",
   "metadata": {},
   "source": [
    "# 2. Data\n",
    "Looking at the dataset from Kaggle, you can you it's a time series problem. This means there's a time attribute to dataset.\n",
    "\n",
    "In this case, it's historical sales data of bulldozers. Including things like, model type, size, sale date and more.\n",
    "\n",
    "There are 3 datasets:\n",
    "\n",
    "Train.csv - Historical bulldozer sales examples up to 2011 (close to 400,000 examples with 50+ different attributes, including SalePrice which is the target variable).\n",
    "Valid.csv - Historical bulldozer sales examples from January 1 2012 to April 30 2012 (close to 12,000 examples with the same attributes as Train.csv).\n",
    "Test.csv - Historical bulldozer sales examples from May 1 2012 to November 2012 (close to 12,000 examples but missing the SalePrice attribute, as this is what we'll be trying to predict)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dd994b",
   "metadata": {},
   "source": [
    "# 3.  Evaluation\n",
    "For this problem, Kaggle has set the evaluation metric to being root mean squared log error (RMSLE). As with many regression evaluations, the goal will be to get this value as low as possible.\n",
    "\n",
    "To see how well our model is doing, we'll calculate the RMSLE and then compare our results to others on the Kaggle leaderboard."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45361d62",
   "metadata": {},
   "source": [
    "# 4. Features\n",
    "Features are different parts of the data. During this step, you'll want to start finding out what you can about the data.\n",
    "\n",
    "One of the most common ways to do this, is to create a data dictionary.\n",
    "\n",
    "For this dataset, Kaggle provides a data dictionary which contains information about what each attribute of the dataset means. You can download this file directly from the Kaggle competition page or view it on Google Sheets.\n",
    "\n",
    "With all of this being known, let's get started!\n",
    "\n",
    "First, we'll import the dataset and start exploring. Since we know the evaluation metric we're trying to minimise, our first goal will be building a baseline model and seeing how it stacks up against the competition."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e91b6f",
   "metadata": {},
   "source": [
    "## EDA (explotary data analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9943321e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4e671e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALI\\AppData\\Local\\Temp\\ipykernel_6304\\744850505.py:2: DtypeWarning: Columns (13,39,40,41) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"data/data/TrainAndValid.csv\")\n"
     ]
    },
    {
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       "  <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",
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       "      <td>4640.0</td>\n",
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       "   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",
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       "4  1139253    11000.0    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand         saledate  ...  \\\n",
       "0                      68.0       Low  11/16/2006 0:00  ...   \n",
       "1                    4640.0       Low   3/26/2004 0:00  ...   \n",
       "2                    2838.0      High   2/26/2004 0:00  ...   \n",
       "3                    3486.0      High   5/19/2011 0:00  ...   \n",
       "4                     722.0    Medium   7/23/2009 0:00  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width Stick_Length Thumb Pattern_Changer Grouser_Type  \\\n",
       "0                     NaN          NaN   NaN             NaN          NaN   \n",
       "1                     NaN          NaN   NaN             NaN          NaN   \n",
       "2                     NaN          NaN   NaN             NaN          NaN   \n",
       "3                     NaN          NaN   NaN             NaN          NaN   \n",
       "4                     NaN          NaN   NaN             NaN          NaN   \n",
       "\n",
       "  Backhoe_Mounting Blade_Type Travel_Controls Differential_Type  \\\n",
       "0              NaN        NaN             NaN          Standard   \n",
       "1              NaN        NaN             NaN          Standard   \n",
       "2              NaN        NaN             NaN               NaN   \n",
       "3              NaN        NaN             NaN               NaN   \n",
       "4              NaN        NaN             NaN               NaN   \n",
       "\n",
       "  Steering_Controls  \n",
       "0      Conventional  \n",
       "1      Conventional  \n",
       "2               NaN  \n",
       "3               NaN  \n",
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       "\n",
       "[5 rows x 53 columns]"
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     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import our data\n",
    "df = pd.read_csv(\"data/data/TrainAndValid.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbb107ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   saledate                  412698 non-null  object \n",
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      " 21  Enclosure                 412364 non-null  object \n",
      " 22  Forks                     197715 non-null  object \n",
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      " 25  Stick                     81096 non-null   object \n",
      " 26  Transmission              188007 non-null  object \n",
      " 27  Turbocharged              81096 non-null   object \n",
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      " 31  Engine_Horsepower         25983 non-null   object \n",
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      " 33  Pushblock                 25983 non-null   object \n",
      " 34  Ripper                    106945 non-null  object \n",
      " 35  Scarifier                 25994 non-null   object \n",
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      " 37  Tire_Size                 97638 non-null   object \n",
      " 38  Coupler                   220679 non-null  object \n",
      " 39  Coupler_System            44974 non-null   object \n",
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      " 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": 82,
   "id": "a6093874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype         \n",
      "---  ------                    --------------   -----         \n",
      " 0   SalesID                   412698 non-null  int64         \n",
      " 1   SalePrice                 412698 non-null  float64       \n",
      " 2   MachineID                 412698 non-null  int64         \n",
      " 3   ModelID                   412698 non-null  int64         \n",
      " 4   datasource                412698 non-null  int64         \n",
      " 5   auctioneerID              392562 non-null  float64       \n",
      " 6   YearMade                  412698 non-null  int64         \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64       \n",
      " 8   UsageBand                 73670 non-null   object        \n",
      " 9   saledate                  412698 non-null  datetime64[ns]\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: datetime64[ns](1), float64(3), int64(5), object(44)\n",
      "memory usage: 166.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#import dataset parsing the date column\n",
    "df_t = pd.read_csv(\"data/data/TrainAndValid.csv\", parse_dates=['saledate'],low_memory=False )\n",
    "df_t.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fa5b3972",
   "metadata": {},
   "outputs": [
    {
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       "      <td>66000.0</td>\n",
       "      <td>57000.0</td>\n",
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       "      <td>38500.0</td>\n",
       "      <td>11000.0</td>\n",
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       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>2004</td>\n",
       "      <td>1996</td>\n",
       "      <td>2001</td>\n",
       "      <td>2001</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <td>68.0</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UsageBand</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saledate</th>\n",
       "      <td>2006-11-16 00:00:00</td>\n",
       "      <td>2004-03-26 00:00:00</td>\n",
       "      <td>2004-02-26 00:00:00</td>\n",
       "      <td>2011-05-19 00:00:00</td>\n",
       "      <td>2009-07-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDesc</th>\n",
       "      <td>521D</td>\n",
       "      <td>950FII</td>\n",
       "      <td>226</td>\n",
       "      <td>PC120-6E</td>\n",
       "      <td>S175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>521</td>\n",
       "      <td>950</td>\n",
       "      <td>226</td>\n",
       "      <td>PC120</td>\n",
       "      <td>S175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>D</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelSeries</th>\n",
       "      <td>NaN</td>\n",
       "      <td>II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6E</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDescriptor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductSize</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Wheel Loader - 110.0 to 120.0 Horsepower</td>\n",
       "      <td>Wheel Loader - 150.0 to 175.0 Horsepower</td>\n",
       "      <td>Skid Steer Loader - 1351.0 to 1601.0 Lb Operat...</td>\n",
       "      <td>Hydraulic Excavator, Track - 12.0 to 14.0 Metr...</td>\n",
       "      <td>Skid Steer Loader - 1601.0 to 1751.0 Lb Operat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>New York</td>\n",
       "      <td>Texas</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>WL</td>\n",
       "      <td>WL</td>\n",
       "      <td>SSL</td>\n",
       "      <td>TEX</td>\n",
       "      <td>SSL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Skid Steer Loaders</td>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>Skid Steer Loaders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drive_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>EROPS w AC</td>\n",
       "      <td>EROPS w AC</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS w AC</td>\n",
       "      <td>EROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pad_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ride_Control</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turbocharged</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Extension</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine_Horsepower</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics</th>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>Auxiliary</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>Auxiliary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pushblock</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ripper</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarifier</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tip_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tire_Size</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>23.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler_System</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Tracks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydraulics_Flow</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Track_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stick_Length</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thumb</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grouser_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>Standard</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls</th>\n",
       "      <td>Conventional</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>2006</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>2011</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 0  \\\n",
       "SalesID                                                    1139246   \n",
       "SalePrice                                                  66000.0   \n",
       "MachineID                                                   999089   \n",
       "ModelID                                                       3157   \n",
       "datasource                                                     121   \n",
       "auctioneerID                                                   3.0   \n",
       "YearMade                                                      2004   \n",
       "MachineHoursCurrentMeter                                      68.0   \n",
       "UsageBand                                                      Low   \n",
       "saledate                                       2006-11-16 00:00:00   \n",
       "fiModelDesc                                                   521D   \n",
       "fiBaseModel                                                    521   \n",
       "fiSecondaryDesc                                                  D   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                    NaN   \n",
       "fiProductClassDesc        Wheel Loader - 110.0 to 120.0 Horsepower   \n",
       "state                                                      Alabama   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                               EROPS w AC   \n",
       "Forks                                          None or Unspecified   \n",
       "Pad_Type                                                       NaN   \n",
       "Ride_Control                                   None or Unspecified   \n",
       "Stick                                                          NaN   \n",
       "Transmission                                                   NaN   \n",
       "Turbocharged                                                   NaN   \n",
       "Blade_Extension                                                NaN   \n",
       "Blade_Width                                                    NaN   \n",
       "Enclosure_Type                                                 NaN   \n",
       "Engine_Horsepower                                              NaN   \n",
       "Hydraulics                                                 2 Valve   \n",
       "Pushblock                                                      NaN   \n",
       "Ripper                                                         NaN   \n",
       "Scarifier                                                      NaN   \n",
       "Tip_Control                                                    NaN   \n",
       "Tire_Size                                      None or Unspecified   \n",
       "Coupler                                        None or Unspecified   \n",
       "Coupler_System                                                 NaN   \n",
       "Grouser_Tracks                                                 NaN   \n",
       "Hydraulics_Flow                                                NaN   \n",
       "Track_Type                                                     NaN   \n",
       "Undercarriage_Pad_Width                                        NaN   \n",
       "Stick_Length                                                   NaN   \n",
       "Thumb                                                          NaN   \n",
       "Pattern_Changer                                                NaN   \n",
       "Grouser_Type                                                   NaN   \n",
       "Backhoe_Mounting                                               NaN   \n",
       "Blade_Type                                                     NaN   \n",
       "Travel_Controls                                                NaN   \n",
       "Differential_Type                                         Standard   \n",
       "Steering_Controls                                     Conventional   \n",
       "month                                                           11   \n",
       "year                                                          2006   \n",
       "\n",
       "                                                                 1  \\\n",
       "SalesID                                                    1139248   \n",
       "SalePrice                                                  57000.0   \n",
       "MachineID                                                   117657   \n",
       "ModelID                                                         77   \n",
       "datasource                                                     121   \n",
       "auctioneerID                                                   3.0   \n",
       "YearMade                                                      1996   \n",
       "MachineHoursCurrentMeter                                    4640.0   \n",
       "UsageBand                                                      Low   \n",
       "saledate                                       2004-03-26 00:00:00   \n",
       "fiModelDesc                                                 950FII   \n",
       "fiBaseModel                                                    950   \n",
       "fiSecondaryDesc                                                  F   \n",
       "fiModelSeries                                                   II   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                 Medium   \n",
       "fiProductClassDesc        Wheel Loader - 150.0 to 175.0 Horsepower   \n",
       "state                                               North Carolina   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                               EROPS w AC   \n",
       "Forks                                          None or Unspecified   \n",
       "Pad_Type                                                       NaN   \n",
       "Ride_Control                                   None or Unspecified   \n",
       "Stick                                                          NaN   \n",
       "Transmission                                                   NaN   \n",
       "Turbocharged                                                   NaN   \n",
       "Blade_Extension                                                NaN   \n",
       "Blade_Width                                                    NaN   \n",
       "Enclosure_Type                                                 NaN   \n",
       "Engine_Horsepower                                              NaN   \n",
       "Hydraulics                                                 2 Valve   \n",
       "Pushblock                                                      NaN   \n",
       "Ripper                                                         NaN   \n",
       "Scarifier                                                      NaN   \n",
       "Tip_Control                                                    NaN   \n",
       "Tire_Size                                                     23.5   \n",
       "Coupler                                        None or Unspecified   \n",
       "Coupler_System                                                 NaN   \n",
       "Grouser_Tracks                                                 NaN   \n",
       "Hydraulics_Flow                                                NaN   \n",
       "Track_Type                                                     NaN   \n",
       "Undercarriage_Pad_Width                                        NaN   \n",
       "Stick_Length                                                   NaN   \n",
       "Thumb                                                          NaN   \n",
       "Pattern_Changer                                                NaN   \n",
       "Grouser_Type                                                   NaN   \n",
       "Backhoe_Mounting                                               NaN   \n",
       "Blade_Type                                                     NaN   \n",
       "Travel_Controls                                                NaN   \n",
       "Differential_Type                                         Standard   \n",
       "Steering_Controls                                     Conventional   \n",
       "month                                                            3   \n",
       "year                                                          2004   \n",
       "\n",
       "                                                                          2  \\\n",
       "SalesID                                                             1139249   \n",
       "SalePrice                                                           10000.0   \n",
       "MachineID                                                            434808   \n",
       "ModelID                                                                7009   \n",
       "datasource                                                              121   \n",
       "auctioneerID                                                            3.0   \n",
       "YearMade                                                               2001   \n",
       "MachineHoursCurrentMeter                                             2838.0   \n",
       "UsageBand                                                              High   \n",
       "saledate                                                2004-02-26 00:00:00   \n",
       "fiModelDesc                                                             226   \n",
       "fiBaseModel                                                             226   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                             NaN   \n",
       "fiProductClassDesc        Skid Steer Loader - 1351.0 to 1601.0 Lb Operat...   \n",
       "state                                                              New York   \n",
       "ProductGroup                                                            SSL   \n",
       "ProductGroupDesc                                         Skid Steer Loaders   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                   None or Unspecified   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                            NaN   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                        Auxiliary   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                                  NaN   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                 None or Unspecified   \n",
       "Coupler_System                                          None or Unspecified   \n",
       "Grouser_Tracks                                          None or Unspecified   \n",
       "Hydraulics_Flow                                                    Standard   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "month                                                                     2   \n",
       "year                                                                   2004   \n",
       "\n",
       "                                                                          3  \\\n",
       "SalesID                                                             1139251   \n",
       "SalePrice                                                           38500.0   \n",
       "MachineID                                                           1026470   \n",
       "ModelID                                                                 332   \n",
       "datasource                                                              121   \n",
       "auctioneerID                                                            3.0   \n",
       "YearMade                                                               2001   \n",
       "MachineHoursCurrentMeter                                             3486.0   \n",
       "UsageBand                                                              High   \n",
       "saledate                                                2011-05-19 00:00:00   \n",
       "fiModelDesc                                                        PC120-6E   \n",
       "fiBaseModel                                                           PC120   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           -6E   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                           Small   \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 12.0 to 14.0 Metr...   \n",
       "state                                                                 Texas   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                        EROPS w AC   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                            NaN   \n",
       "Turbocharged                                                            NaN   \n",
       "Blade_Extension                                                         NaN   \n",
       "Blade_Width                                                             NaN   \n",
       "Enclosure_Type                                                          NaN   \n",
       "Engine_Horsepower                                                       NaN   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                                               NaN   \n",
       "Ripper                                                                  NaN   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                 None or Unspecified   \n",
       "Coupler_System                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                              NaN   \n",
       "Undercarriage_Pad_Width                                                 NaN   \n",
       "Stick_Length                                                            NaN   \n",
       "Thumb                                                                   NaN   \n",
       "Pattern_Changer                                                         NaN   \n",
       "Grouser_Type                                                            NaN   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "month                                                                     5   \n",
       "year                                                                   2011   \n",
       "\n",
       "                                                                          4  \n",
       "SalesID                                                             1139253  \n",
       "SalePrice                                                           11000.0  \n",
       "MachineID                                                           1057373  \n",
       "ModelID                                                               17311  \n",
       "datasource                                                              121  \n",
       "auctioneerID                                                            3.0  \n",
       "YearMade                                                               2007  \n",
       "MachineHoursCurrentMeter                                              722.0  \n",
       "UsageBand                                                            Medium  \n",
       "saledate                                                2009-07-23 00:00:00  \n",
       "fiModelDesc                                                            S175  \n",
       "fiBaseModel                                                            S175  \n",
       "fiSecondaryDesc                                                         NaN  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Skid Steer Loader - 1601.0 to 1751.0 Lb Operat...  \n",
       "state                                                              New York  \n",
       "ProductGroup                                                            SSL  \n",
       "ProductGroupDesc                                         Skid Steer Loaders  \n",
       "Drive_System                                                            NaN  \n",
       "Enclosure                                                             EROPS  \n",
       "Forks                                                   None or Unspecified  \n",
       "Pad_Type                                                                NaN  \n",
       "Ride_Control                                                            NaN  \n",
       "Stick                                                                   NaN  \n",
       "Transmission                                                            NaN  \n",
       "Turbocharged                                                            NaN  \n",
       "Blade_Extension                                                         NaN  \n",
       "Blade_Width                                                             NaN  \n",
       "Enclosure_Type                                                          NaN  \n",
       "Engine_Horsepower                                                       NaN  \n",
       "Hydraulics                                                        Auxiliary  \n",
       "Pushblock                                                               NaN  \n",
       "Ripper                                                                  NaN  \n",
       "Scarifier                                                               NaN  \n",
       "Tip_Control                                                             NaN  \n",
       "Tire_Size                                                               NaN  \n",
       "Coupler                                                 None or Unspecified  \n",
       "Coupler_System                                          None or Unspecified  \n",
       "Grouser_Tracks                                          None or Unspecified  \n",
       "Hydraulics_Flow                                                    Standard  \n",
       "Track_Type                                                              NaN  \n",
       "Undercarriage_Pad_Width                                                 NaN  \n",
       "Stick_Length                                                            NaN  \n",
       "Thumb                                                                   NaN  \n",
       "Pattern_Changer                                                         NaN  \n",
       "Grouser_Type                                                            NaN  \n",
       "Backhoe_Mounting                                                        NaN  \n",
       "Blade_Type                                                              NaN  \n",
       "Travel_Controls                                                         NaN  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  \n",
       "month                                                                     7  \n",
       "year                                                                   2009  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create columns with year and months from saledate column\n",
    "df_t['month'] = df_t.saledate.dt.month\n",
    "df_t['year'] = df_t.saledate.dt.year\n",
    "df_t.head(5).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "597213eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD7CAYAAABqvuNzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVR0lEQVR4nO3dfZBd9X3f8ffHkk2EbYknoSgSeElQHoCOcVBlOnQSEjlGMWkgDczIngQ1UaopJbU7fUhF0pk0f2gqddpQkxRmNMZGEDsgK3FQ7MGBiJBOWipYDLEQgrIxVNJICMVgjO2AI/nbP+5vZ66Wq9Xd1d1dPbxfM3fuud9zfuf3O7t79bnn4R6lqpAk6R0zPQBJ0onBQJAkAQaCJKkxECRJgIEgSWoMBEkSALNnegCTdd5559XQ0NBMD0OSTipPPvnk31bV/F7zTtpAGBoaYnh4eKaHIUknlST/72jzPGQkSQIMBElS01cgJDkryZYkzyXZleQfJTknycNJXmjPZ3ctf2uSkSTPJ7mmq35Fkh1t3u1J0upnJLm/1bcnGRr4lkqSxtXvHsIngS9X1Y8C7wd2AWuBbVW1BNjWXpPkEmAlcCmwArgjyay2njuBNcCS9ljR6quB16rqYuA2YMNxbpckaYKOGQhJ5gI/AdwFUFXfrapvANcBm9pim4Dr2/R1wH1V9VZVvQiMAMuSLATmVtVj1bmj3j1j2oyuawuwfHTvQZI0PfrZQ/hB4CDwmSRPJflUkncDC6pqP0B7Pr8tvwjY09V+b6statNj60e0qapDwOvAuZPaIknSpPQTCLOBHwfurKoPAN+mHR46il6f7Guc+nhtjlxxsibJcJLhgwcPjj9qSdKE9BMIe4G9VbW9vd5CJyAOtMNAtOdXupa/oKv9YmBfqy/uUT+iTZLZwDzg1bEDqaqNVbW0qpbOn9/zexWSpEk65hfTqurlJHuS/EhVPQ8sB55tj1XA+vb8QGuyFfhckt8FfoDOyePHq+pwkjeSXAlsB24Cfq+rzSrgMeAG4JE6jv+5Z2jtlybV7qX11062S0k66fX7TeV/BXw2ybuArwG/QmfvYnOS1cBu4EaAqtqZZDOdwDgE3FJVh9t6bgbuBuYAD7YHdE5Y35tkhM6ewcrj3C5J0gT1FQhV9TSwtMes5UdZfh2wrkd9GLisR/1NWqBIkmaG31SWJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTmpP0/lU8k3ipD0qnAPQRJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRLQZyAkeSnJjiRPJxlutXOSPJzkhfZ8dtfytyYZSfJ8kmu66le09YwkuT1JWv2MJPe3+vYkQwPeTknSMUxkD+GnquryqlraXq8FtlXVEmBbe02SS4CVwKXACuCOJLNamzuBNcCS9ljR6quB16rqYuA2YMPkN0mSNBnHc8joOmBTm94EXN9Vv6+q3qqqF4ERYFmShcDcqnqsqgq4Z0yb0XVtAZaP7j1IkqZHv4FQwENJnkyyptUWVNV+gPZ8fqsvAvZ0td3baova9Nj6EW2q6hDwOnDuxDZFknQ8Zve53FVVtS/J+cDDSZ4bZ9len+xrnPp4bY5ccSeM1gBceOGF449YkjQhfe0hVNW+9vwK8AVgGXCgHQaiPb/SFt8LXNDVfDGwr9UX96gf0SbJbGAe8GqPcWysqqVVtXT+/Pn9DF2S1KdjBkKSdyd57+g08GHgGWArsKottgp4oE1vBVa2K4cuonPy+PF2WOmNJFe28wM3jWkzuq4bgEfaeQZJ0jTp55DRAuAL7RzvbOBzVfXlJE8Am5OsBnYDNwJU1c4km4FngUPALVV1uK3rZuBuYA7wYHsA3AXcm2SEzp7BygFsmyRpAo4ZCFX1NeD9PepfB5Yfpc06YF2P+jBwWY/6m7RAkSTNDL+pLEkCDARJUmMgSJIAA0GS1BgIkiSg/28q6wQytPZLk2r30vprBzwSSacS9xAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhpvbqdj8mZ60unBPQRJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJwAQCIcmsJE8l+WJ7fU6Sh5O80J7P7lr21iQjSZ5Pck1X/YokO9q825Ok1c9Icn+rb08yNMBtlCT1YSJ7CJ8AdnW9Xgtsq6olwLb2miSXACuBS4EVwB1JZrU2dwJrgCXtsaLVVwOvVdXFwG3AhkltjSRp0voKhCSLgWuBT3WVrwM2telNwPVd9fuq6q2qehEYAZYlWQjMrarHqqqAe8a0GV3XFmD56N6DJGl69LuH8N+B3wC+11VbUFX7Adrz+a2+CNjTtdzeVlvUpsfWj2hTVYeA14Fzxw4iyZokw0mGDx482OfQJUn9OOa9jJL8HPBKVT2Z5Oo+1tnrk32NUx+vzZGFqo3ARoClS5e+bb5ODd47SZoZ/dzc7irg55N8BPg+YG6SPwAOJFlYVfvb4aBX2vJ7gQu62i8G9rX64h717jZ7k8wG5gGvTnKbJEmTcMxDRlV1a1UtrqohOieLH6mqXwK2AqvaYquAB9r0VmBlu3LoIjonjx9vh5XeSHJlOz9w05g2o+u6ofXhHoAkTaPjuf31emBzktXAbuBGgKramWQz8CxwCLilqg63NjcDdwNzgAfbA+Au4N4kI3T2DFYex7gkSZMwoUCoqkeBR9v014HlR1luHbCuR30YuKxH/U1aoEiSZobfVJYkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJKa2TM9AGmmDa390qTavbT+2gGPRJpZ7iFIkgADQZLUGAiSJKCPQEjyfUkeT/LXSXYm+Z1WPyfJw0leaM9nd7W5NclIkueTXNNVvyLJjjbv9iRp9TOS3N/q25MMTcG2SpLG0c8ewlvAT1fV+4HLgRVJrgTWAtuqagmwrb0mySXASuBSYAVwR5JZbV13AmuAJe2xotVXA69V1cXAbcCG4980SdJEHPMqo6oq4Fvt5Tvbo4DrgKtbfRPwKPAfWv2+qnoLeDHJCLAsyUvA3Kp6DCDJPcD1wIOtzX9q69oC/H6StL6lU4ZXNOlE1tc5hCSzkjwNvAI8XFXbgQVVtR+gPZ/fFl8E7OlqvrfVFrXpsfUj2lTVIeB14Nwe41iTZDjJ8MGDB/vaQElSf/oKhKo6XFWXA4vpfNq/bJzF02sV49THazN2HBuramlVLZ0/f/4xRi1JmogJfTGtqr6R5FE6x/4PJFlYVfuTLKSz9wCdT/4XdDVbDOxr9cU96t1t9iaZDcwDXp3gtkgaw0NUmoh+rjKan+SsNj0H+BDwHLAVWNUWWwU80Ka3AivblUMX0Tl5/Hg7rPRGkivb1UU3jWkzuq4bgEc8fyBJ06ufPYSFwKZ2pdA7gM1V9cUkjwGbk6wGdgM3AlTVziSbgWeBQ8AtVXW4retm4G5gDp2TyQ+2+l3Ave0E9Kt0rlKSJE2jfq4y+irwgR71rwPLj9JmHbCuR30YeNv5h6p6kxYokqSZ4TeVJUmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkScAEb24nSePxZnonN/cQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqvHWFpJOWt8oYLPcQJEmAgSBJagwESRJgIEiSGgNBkgT0EQhJLkjyF0l2JdmZ5BOtfk6Sh5O80J7P7mpza5KRJM8nuaarfkWSHW3e7UnS6mckub/VtycZmoJtlSSNo589hEPAv62qHwOuBG5JcgmwFthWVUuAbe01bd5K4FJgBXBHklltXXcCa4Al7bGi1VcDr1XVxcBtwIYBbJskaQKOGQhVtb+qvtKm3wB2AYuA64BNbbFNwPVt+jrgvqp6q6peBEaAZUkWAnOr6rGqKuCeMW1G17UFWD669yBJmh4TOofQDuV8ANgOLKiq/dAJDeD8ttgiYE9Xs72ttqhNj60f0aaqDgGvA+f26H9NkuEkwwcPHpzI0CVJx9B3ICR5D/BHwL+uqm+Ot2iPWo1TH6/NkYWqjVW1tKqWzp8//1hDliRNQF+BkOSddMLgs1X1x618oB0Goj2/0up7gQu6mi8G9rX64h71I9okmQ3MA16d6MZIkiavn6uMAtwF7Kqq3+2atRVY1aZXAQ901Ve2K4cuonPy+PF2WOmNJFe2dd40ps3oum4AHmnnGSRJ06Sfm9tdBfwysCPJ0632m8B6YHOS1cBu4EaAqtqZZDPwLJ0rlG6pqsOt3c3A3cAc4MH2gE7g3JtkhM6ewcrj2yxJ0kQdMxCq6q/ofYwfYPlR2qwD1vWoDwOX9ai/SQsUSdLM8JvKkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1/dzLSJIEDK390qTavbT+2gGPZGq4hyBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNd7+WpJOUNN9u+1j7iEk+XSSV5I801U7J8nDSV5oz2d3zbs1yUiS55Nc01W/IsmONu/2JGn1M5Lc3+rbkwxNakskSceln0NGdwMrxtTWAtuqagmwrb0mySXASuDS1uaOJLNamzuBNcCS9hhd52rgtaq6GLgN2DDZjZEkTd4xA6Gq/ifw6pjydcCmNr0JuL6rfl9VvVVVLwIjwLIkC4G5VfVYVRVwz5g2o+vaAiwf3XuQJE2fyZ5UXlBV+wHa8/mtvgjY07Xc3lZb1KbH1o9oU1WHgNeBc3t1mmRNkuEkwwcPHpzk0CVJvQz6KqNen+xrnPp4bd5erNpYVUuraun8+fMnOURJUi+TDYQD7TAQ7fmVVt8LXNC13GJgX6sv7lE/ok2S2cA83n6ISpI0xSYbCFuBVW16FfBAV31lu3LoIjonjx9vh5XeSHJlOz9w05g2o+u6AXiknWeQJE2jY34PIckfAlcD5yXZC/w2sB7YnGQ1sBu4EaCqdibZDDwLHAJuqarDbVU307liaQ7wYHsA3AXcm2SEzp7ByoFsmSRpQo4ZCFX10aPMWn6U5dcB63rUh4HLetTfpAWKJGnmeOsKSRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSmhMmEJKsSPJ8kpEka2d6PJJ0ujkhAiHJLOB/AD8LXAJ8NMklMzsqSTq9nBCBACwDRqrqa1X1XeA+4LoZHpMknVZSVTM9BpLcAKyoql9rr38Z+GBV/fqY5dYAa9rLHwGen0R35wF/exzDtT/7OxX6sr/Tt7/3VdX8XjNmH994BiY9am9LqqraCGw8ro6S4apaejzrsD/7O9n7sj/76+VEOWS0F7ig6/ViYN8MjUWSTksnSiA8ASxJclGSdwErga0zPCZJOq2cEIeMqupQkl8H/gyYBXy6qnZOUXfHdcjJ/uzvFOnL/uzvbU6Ik8qSpJl3ohwykiTNMANBkgQYCJKk5pQPhCTLkvzDNn1Jkn+T5CNT2N+PJlme5D1j6iumoK8PJpnbpuck+Z0kf5pkQ5J5U9DfDyX5d0k+meS/JfkXU9HPiSDJP25/Kx+exj7vma6+TjVJ3pXkpiQfaq8/luT3k9yS5J1T0N/Hk1xw7CVPLqf0SeUkv03n/kizgYeBDwKPAh8C/qyq1g24v48DtwC7gMuBT1TVA23eV6rqxwfc307g/e0qrY3Ad4AtwPJW/6cD7OvjwD8B/hL4CPA08BrwC8C/rKpHB9XXTEjyeFUta9P/nM7v8QvAh4E/rar1A+5v7GXVAX4KeASgqn5+kP2d6pJ8ls77/EzgG8B7gD+m815IVa0acH+vA98G/gb4Q+DzVXVwkH1MYCy/UlWfGcjKquqUfQA76FzGeibwTWBuq88BvjpF/b2nTQ8Bw3RCAeCpKehvV9f0V8bMe3oqfpZt+kzg0TZ94VRsW1v3XOA/A/cCHxsz744B9/VU1/QTwPw2/W5gxxRs21eAPwCuBn6yPe9v0z85Bf3NA9YDzwFfb49drXbWVPz+xhnLg1Owzq+259nAga6/1UzRe/0pOkdYPgzcBRwEvgysAt47zT/P3YNa1wnxPYQpdKiqDgPfSfI3VfVNgKr6uyTfm4L+ZlXVt1ofLyW5GtiS5H30vj3H8Xqm69PBXydZWlXDSX4Y+Psp6G82cBg4A3gvQFXtnopd8uYzwAvAHwG/muQX6QTDW8CVA+7rHUnOpvMmT7VPe1X17SSHBtwXwFLgE8BvAf++qp5O8ndV9ZdT0BfAZjp7H1dX1csASb6fzj9gnwd+ZpCdJTna3nDo7D0P2jval1rfTecDyzzgVTp/q1Px91lV9T3gIeCh9h74WeCjwH8Fet4raLKSfPVos4AFg+rnVA+E7yY5s6q+A1wxWmzHvaciEF5OcnlVPQ1QVd9K8nPAp4F/MAX9/RrwyST/kc5Nrh5LsgfY0+YN0qeAJ5L8H+AngA0ASebTeeNNhR+qql9s03+S5LeAR5JMxeGUecCTdN5gleT7q+rldi5o4GHe/jG5Lcnn2/MBpvb9OFRVG8aM4WVgQ5JfnYL+nqBzeLHXz+6sKejvLjp7P7PohOznk3yNzgeH+6agvyO2q6r+ns7dFbYmmTMF/S0ArqFzmHbsOP73oDo51c8hnNE+TY6tnwcsrKodA+5vMZ29kpd7zLuqqv7XIPvrWvd7gR+k8w/K3qo6MEX9XAr8GPBMVT03FX2M6W8XcGn7x3O0tgr4DTqH5t43DWM4E1hQVS9OcT/XAldV1W9O0fofAv4c2DT695FkAfDPgJ+pqg8NuL9ngF+oqhd6zNtTVQM/IZvkBwCqal+Ss+icK9xdVY9PQV8/XFX/d9DrHae/u4DPVNVf9Zj3uar62ED6OZUDQSe3JP8FeKiq/nxMfQXwe1W1ZGZGdvJph8PW0vl/Rs5v5QN0PtWur6qxnzyPt78b6Jx7edst6pNcX1V/Msj+NBgGgk5KA72y4jQ33T9Lf3cnLgNBJ6Uku6vqwpkex6lgun+W/u5OXKf6SWWdxKbryorTwXT/LP3dnZwMBJ3IpuXKitPEdP8s/d2dhAwEnci+SOdqoqfHzkjy6LSP5uQ23T9Lf3cnIc8hSJKA0+DmdpKk/hgIkiTAQJAkNQaCJAkwECRJzf8HVCFYLo0qY+8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how was the sales performance per each month\n",
    "df_t[\"month\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d629ed86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#how was the sales performance per the given years\n",
    "plt.figure(figsize=(10,6))\n",
    "df_t[\"year\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44e000ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "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": [
    "#how was the sales peformance for each month in 2011 \n",
    "df_t[df_t[\"year\"] == 2011][\"month\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27be616a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "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": [
    "#how was the sales performance for each month in the validation dataset\n",
    "df_t[df_t[\"year\"] != 2011][\"month\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80c5f307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# how were the sales as per state for the different years\n",
    "plt.figure(figsize=(10,6))\n",
    "df_t[df_t[\"year\"] != 2011][\"state\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966dfe2b",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "80514e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t[\"saleDay\"] = df_t.saledate.dt.day\n",
    "df_t[\"saleDayofweek\"] = df_t.saledate.dt.dayofweek\n",
    "df_t[\"saleDayofyear\"] = df_t.saledate.dt.dayofyear\n",
    "\n",
    "# Drop original saledate\n",
    "df_t.drop(\"saledate\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e93fa929",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 57 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   fiModelDesc               412698 non-null  object \n",
      " 10  fiBaseModel               412698 non-null  object \n",
      " 11  fiSecondaryDesc           271971 non-null  object \n",
      " 12  fiModelSeries             58667 non-null   object \n",
      " 13  fiModelDescriptor         74816 non-null   object \n",
      " 14  ProductSize               196093 non-null  object \n",
      " 15  fiProductClassDesc        412698 non-null  object \n",
      " 16  state                     412698 non-null  object \n",
      " 17  ProductGroup              412698 non-null  object \n",
      " 18  ProductGroupDesc          412698 non-null  object \n",
      " 19  Drive_System              107087 non-null  object \n",
      " 20  Enclosure                 412364 non-null  object \n",
      " 21  Forks                     197715 non-null  object \n",
      " 22  Pad_Type                  81096 non-null   object \n",
      " 23  Ride_Control              152728 non-null  object \n",
      " 24  Stick                     81096 non-null   object \n",
      " 25  Transmission              188007 non-null  object \n",
      " 26  Turbocharged              81096 non-null   object \n",
      " 27  Blade_Extension           25983 non-null   object \n",
      " 28  Blade_Width               25983 non-null   object \n",
      " 29  Enclosure_Type            25983 non-null   object \n",
      " 30  Engine_Horsepower         25983 non-null   object \n",
      " 31  Hydraulics                330133 non-null  object \n",
      " 32  Pushblock                 25983 non-null   object \n",
      " 33  Ripper                    106945 non-null  object \n",
      " 34  Scarifier                 25994 non-null   object \n",
      " 35  Tip_Control               25983 non-null   object \n",
      " 36  Tire_Size                 97638 non-null   object \n",
      " 37  Coupler                   220679 non-null  object \n",
      " 38  Coupler_System            44974 non-null   object \n",
      " 39  Grouser_Tracks            44875 non-null   object \n",
      " 40  Hydraulics_Flow           44875 non-null   object \n",
      " 41  Track_Type                102193 non-null  object \n",
      " 42  Undercarriage_Pad_Width   102916 non-null  object \n",
      " 43  Stick_Length              102261 non-null  object \n",
      " 44  Thumb                     102332 non-null  object \n",
      " 45  Pattern_Changer           102261 non-null  object \n",
      " 46  Grouser_Type              102193 non-null  object \n",
      " 47  Backhoe_Mounting          80712 non-null   object \n",
      " 48  Blade_Type                81875 non-null   object \n",
      " 49  Travel_Controls           81877 non-null   object \n",
      " 50  Differential_Type         71564 non-null   object \n",
      " 51  Steering_Controls         71522 non-null   object \n",
      " 52  month                     412698 non-null  int64  \n",
      " 53  year                      412698 non-null  int64  \n",
      " 54  saleDay                   412698 non-null  int64  \n",
      " 55  saleDayofweek             412698 non-null  int64  \n",
      " 56  saleDayofyear             412698 non-null  int64  \n",
      "dtypes: float64(3), int64(10), object(44)\n",
      "memory usage: 179.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_t.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20ccfa7e",
   "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)",
      "Input \u001b[1;32mIn [13]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mensemble\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RandomForestRegressor\n\u001b[0;32m      4\u001b[0m model \u001b[38;5;241m=\u001b[39m RandomForestRegressor(n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_t\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSalePrice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdf_t\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSalePrice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:331\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(y):\n\u001b[0;32m    330\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 331\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    332\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\n\u001b[0;32m    333\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    334\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    335\u001b[0m     sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(sample_weight, X)\n",
      "File \u001b[1;32m~\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\base.py:596\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    594\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[0;32m    595\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 596\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m check_X_y(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    597\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    599\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[1;32m~\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:1074\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1069\u001b[0m         estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1070\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1071\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1072\u001b[0m     )\n\u001b[1;32m-> 1074\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1075\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1076\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1077\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1078\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1079\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1080\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1081\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1082\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1083\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1084\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1085\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1086\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1087\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1088\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1090\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[0;32m   1092\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[1;32m~\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:856\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    854\u001b[0m         array \u001b[38;5;241m=\u001b[39m array\u001b[38;5;241m.\u001b[39mastype(dtype, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m\"\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 856\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    857\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    858\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    859\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    860\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
      "File \u001b[1;32m~\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\pandas\\core\\generic.py:2064\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2063\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2064\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# lets model since we have the data, a good candidate for this would be RandomForestRegressor() from the scikit-learn machine learning map\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=1)\n",
    "model.fit(df_t.drop('SalePrice',axis=1),df_t['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "469aa1e1",
   "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",
       "month                            0\n",
       "year                             0\n",
       "saleDay                          0\n",
       "saleDayofweek                    0\n",
       "saleDayofyear                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64b6cfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert strings to numbers\n",
    "# first convert strings to category datatype\n",
    "# we shall use the pandas type api that allows us to interact and manipulate the types of data\n",
    "pd.api.types.is_string_dtype(df_t['UsageBand'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "297ce770",
   "metadata": {},
   "outputs": [],
   "source": [
    "for label,content in df_t.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        \n",
    "        df_t[label] = content.astype(\"category\").cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb393ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 57 columns):\n",
      " #   Column                    Non-Null Count   Dtype   \n",
      "---  ------                    --------------   -----   \n",
      " 0   SalesID                   412698 non-null  int64   \n",
      " 1   SalePrice                 412698 non-null  float64 \n",
      " 2   MachineID                 412698 non-null  int64   \n",
      " 3   ModelID                   412698 non-null  int64   \n",
      " 4   datasource                412698 non-null  int64   \n",
      " 5   auctioneerID              392562 non-null  float64 \n",
      " 6   YearMade                  412698 non-null  int64   \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64 \n",
      " 8   UsageBand                 73670 non-null   category\n",
      " 9   fiModelDesc               412698 non-null  category\n",
      " 10  fiBaseModel               412698 non-null  category\n",
      " 11  fiSecondaryDesc           271971 non-null  category\n",
      " 12  fiModelSeries             58667 non-null   category\n",
      " 13  fiModelDescriptor         74816 non-null   category\n",
      " 14  ProductSize               196093 non-null  category\n",
      " 15  fiProductClassDesc        412698 non-null  category\n",
      " 16  state                     412698 non-null  category\n",
      " 17  ProductGroup              412698 non-null  category\n",
      " 18  ProductGroupDesc          412698 non-null  category\n",
      " 19  Drive_System              107087 non-null  category\n",
      " 20  Enclosure                 412364 non-null  category\n",
      " 21  Forks                     197715 non-null  category\n",
      " 22  Pad_Type                  81096 non-null   category\n",
      " 23  Ride_Control              152728 non-null  category\n",
      " 24  Stick                     81096 non-null   category\n",
      " 25  Transmission              188007 non-null  category\n",
      " 26  Turbocharged              81096 non-null   category\n",
      " 27  Blade_Extension           25983 non-null   category\n",
      " 28  Blade_Width               25983 non-null   category\n",
      " 29  Enclosure_Type            25983 non-null   category\n",
      " 30  Engine_Horsepower         25983 non-null   category\n",
      " 31  Hydraulics                330133 non-null  category\n",
      " 32  Pushblock                 25983 non-null   category\n",
      " 33  Ripper                    106945 non-null  category\n",
      " 34  Scarifier                 25994 non-null   category\n",
      " 35  Tip_Control               25983 non-null   category\n",
      " 36  Tire_Size                 97638 non-null   category\n",
      " 37  Coupler                   220679 non-null  category\n",
      " 38  Coupler_System            44974 non-null   category\n",
      " 39  Grouser_Tracks            44875 non-null   category\n",
      " 40  Hydraulics_Flow           44875 non-null   category\n",
      " 41  Track_Type                102193 non-null  category\n",
      " 42  Undercarriage_Pad_Width   102916 non-null  category\n",
      " 43  Stick_Length              102261 non-null  category\n",
      " 44  Thumb                     102332 non-null  category\n",
      " 45  Pattern_Changer           102261 non-null  category\n",
      " 46  Grouser_Type              102193 non-null  category\n",
      " 47  Backhoe_Mounting          80712 non-null   category\n",
      " 48  Blade_Type                81875 non-null   category\n",
      " 49  Travel_Controls           81877 non-null   category\n",
      " 50  Differential_Type         71564 non-null   category\n",
      " 51  Steering_Controls         71522 non-null   category\n",
      " 52  month                     412698 non-null  int64   \n",
      " 53  year                      412698 non-null  int64   \n",
      " 54  saleDay                   412698 non-null  int64   \n",
      " 55  saleDayofweek             412698 non-null  int64   \n",
      " 56  saleDayofyear             412698 non-null  int64   \n",
      "dtypes: category(44), float64(3), int64(10)\n",
      "memory usage: 60.1 MB\n"
     ]
    }
   ],
   "source": [
    "df_t.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b11bf597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado',\n",
       "       'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho',\n",
       "       'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana',\n",
       "       'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',\n",
       "       'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',\n",
       "       'New Hampshire', 'New Jersey', 'New Mexico', 'New York',\n",
       "       'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon',\n",
       "       'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina',\n",
       "       'South Dakota', 'Tennessee', 'Texas', 'Unspecified', 'Utah', 'Vermont',\n",
       "       'Virginia', 'Washington', 'Washington DC', 'West Virginia', 'Wisconsin',\n",
       "       'Wyoming'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.state.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87e1bfc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1         32\n",
       "2         31\n",
       "3         43\n",
       "4         31\n",
       "          ..\n",
       "412693    43\n",
       "412694     8\n",
       "412695     8\n",
       "412696    43\n",
       "412697     8\n",
       "Length: 412698, dtype: int8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.state.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "182d8c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now lets work on missing values before we can convert categories to numbers\n",
    "# lets fill the missing on the columns that are numerical with median\n",
    "for label,content in df_t.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            \n",
    "            ## add a binary column to show if data was missing or not\n",
    "            df_t[label+\"_is_missing\"] = pd.isnull(content)\n",
    "            ## fill missing values with median\n",
    "            df_t[label] = content.fillna(content.median)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4adec536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # now lets check if there are any missing values\n",
    "for label,content in df_t.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "edd5314d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # now lets convert categorical variables to numbers\n",
    "for label,content in df_t.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        df_t[label+\"_is_missing\"] = pd.isnull(content)\n",
    "        df_t[label] = pd.Categorical(content).codes+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8f59a7de",
   "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_Controls_is_missing\n",
      "dtypes: bool(46), float64(1), int16(5), int64(10), int8(41)\n",
      "memory usage: 72.8 MB\n"
     ]
    }
   ],
   "source": [
    "df_t.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "255eed05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                         0\n",
       "SalePrice                       0\n",
       "MachineID                       0\n",
       "ModelID                         0\n",
       "datasource                      0\n",
       "                               ..\n",
       "Backhoe_Mounting_is_missing     0\n",
       "Blade_Type_is_missing           0\n",
       "Travel_Controls_is_missing      0\n",
       "Differential_Type_is_missing    0\n",
       "Steering_Controls_is_missing    0\n",
       "Length: 103, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92dd4108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1139246</td>\n",
       "      <td>1139248</td>\n",
       "      <td>1139249</td>\n",
       "      <td>1139251</td>\n",
       "      <td>1139253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>66000.0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>999089</td>\n",
       "      <td>117657</td>\n",
       "      <td>434808</td>\n",
       "      <td>1026470</td>\n",
       "      <td>1057373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>3157</td>\n",
       "      <td>77</td>\n",
       "      <td>7009</td>\n",
       "      <td>332</td>\n",
       "      <td>17311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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                       1139246  1139248  1139249  1139251  1139253\n",
       "SalePrice                     66000.0  57000.0  10000.0  38500.0  11000.0\n",
       "MachineID                      999089   117657   434808  1026470  1057373\n",
       "ModelID                          3157       77     7009      332    17311\n",
       "datasource                        121      121      121      121      121\n",
       "...                               ...      ...      ...      ...      ...\n",
       "Backhoe_Mounting_is_missing      True     True     True     True     True\n",
       "Blade_Type_is_missing            True     True     True     True     True\n",
       "Travel_Controls_is_missing       True     True     True     True     True\n",
       "Differential_Type_is_missing    False    False     True     True     True\n",
       "Steering_Controls_is_missing    False    False     True     True     True\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6a4424",
   "metadata": {},
   "source": [
    "### fitting model , tunning model and predicting using the test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "50358914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 401125)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split our data into training and validatio\n",
    "df_valid = df_t[df_t['year'] == 2012]\n",
    "df_train = df_t[df_t['year'] != 2012]\n",
    "len(df_valid), len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "8a0c485b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 102), (401125,), (11573, 102), (11573,))"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split data into x$y features and labels\n",
    "x_train, y_train = df_train.drop(\"SalePrice\",axis=1),df_train[\"SalePrice\"]\n",
    "x_valid, y_valid = df_valid.drop(\"SalePrice\", axis=1),df_valid[\"SalePrice\"]\n",
    "x_train.shape, y_train.shape, x_valid.shape,y_valid.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "c1b88325",
   "metadata": {},
   "outputs": [],
   "source": [
    "# building evaluation function\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "\n",
    "# function to  return RMSLE(squareroot of mean_squared_log_error)\n",
    "def rmsle(y_test,y_preds):\n",
    "    return np.sqrt(mean_squared_log_error(y_test,y_preds))\n",
    "\n",
    "# function to evaluate the model\n",
    "def show_score(model):\n",
    "    val_pred = model.predict(x_valid)\n",
    "    score = {\"valid RMSLE\":rmsle(y_valid,val_pred)}\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "6cceaca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now lets train our model with 10,000 samples\n",
    "model = RandomForestRegressor(n_jobs=-1,\n",
    "                             max_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "927651cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 28s\n",
      "Wall time: 36.7 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_samples=10000, n_jobs=-1)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "8ada8251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'valid RMSLE': 0.29300868591490037}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_score(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c2479fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n",
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 12min 7s\n",
      "Wall time: 12min 15s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;max_features&#x27;: [0.5, 1, &#x27;sqrt&#x27;,\n",
       "                                                         &#x27;auto&#x27;],\n",
       "                                        &#x27;max_samples&#x27;: [10000],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;max_features&#x27;: [0.5, 1, &#x27;sqrt&#x27;,\n",
       "                                                         &#x27;auto&#x27;],\n",
       "                                        &#x27;max_samples&#x27;: [10000],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\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",
       "                   verbose=True)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "## lets improve our model by hyperparameter tunning using randomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Different RandomForestClassifier hyperparameters\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]}\n",
    "\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(),\n",
    "                             param_distributions=rf_grid,\n",
    "                             n_iter=20,\n",
    "                             cv=5,\n",
    "                             verbose=True)\n",
    "rs_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "fc530071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 20,\n",
       " 'min_samples_split': 6,\n",
       " 'min_samples_leaf': 7,\n",
       " 'max_samples': 10000,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "4ea80a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'valid RMSLE': 0.3026195260183778}"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_score(rs_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "54e38a90",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 5min 49s\n",
      "Wall time: 1min 58s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, min_samples_leaf=7,\n",
       "                      min_samples_split=6, n_estimators=20, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, min_samples_leaf=7,\n",
       "                      min_samples_split=6, n_estimators=20, n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features='auto', min_samples_leaf=7,\n",
       "                      min_samples_split=6, n_estimators=20, n_jobs=-1)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "### training our model with the best parameters\n",
    "\n",
    "ideal_model = RandomForestRegressor(n_estimators=20,\n",
    "                                   min_samples_split=6,\n",
    "                                   min_samples_leaf=7,\n",
    "                                   max_features='auto',\n",
    "                                   max_depth=None,\n",
    "                                   n_jobs=-1)\n",
    "ideal_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "20aed253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'valid RMSLE': 0.25514138610276044}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_score(ideal_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "4948b969",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALI\\MLprojects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 8min 38s\n",
      "Wall time: 3min 5s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, min_samples_leaf=7,\n",
       "                      min_samples_split=6, n_estimators=30, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, min_samples_leaf=7,\n",
       "                      min_samples_split=6, n_estimators=30, n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features='auto', min_samples_leaf=7,\n",
       "                      min_samples_split=6, n_estimators=30, n_jobs=-1)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "### training our model with the best parameters\n",
    "\n",
    "fast_model = RandomForestRegressor(n_estimators=30,\n",
    "                                   min_samples_split=6,\n",
    "                                   min_samples_leaf=7,\n",
    "                                   max_features='auto',\n",
    "                                   max_depth=None,\n",
    "                                   n_jobs=-1)\n",
    "fast_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "ae514b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'valid RMSLE': 0.2525528429841453}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_score(fast_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0d103045",
   "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": [
    "## lets now make predictions on the test data since we have a trained model\n",
    "\n",
    "test_data = pd.read_csv(\"data/data/Test.csv\", parse_dates=[\"saledate\"])\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "bedcbcca",
   "metadata": {},
   "outputs": [],
   "source": [
    "## lets preprocess our data \n",
    "## Whatever you do to the training data, you have to do to the test data.\n",
    "def preprocess_data(df_t):\n",
    "    df_t['month'] = df_t.saledate.dt.month\n",
    "    df_t['year'] = df_t.saledate.dt.year\n",
    "    df_t[\"saleDay\"] = df_t.saledate.dt.day\n",
    "    df_t[\"saleDayofweek\"] = df_t.saledate.dt.dayofweek\n",
    "    df_t[\"saleDayofyear\"] = df_t.saledate.dt.dayofyear\n",
    "\n",
    "    # Drop original saledate\n",
    "    df_t.drop(\"saledate\", axis=1, inplace=True)\n",
    "    # convert object to catergory\n",
    "    for label,content in df_t.items():\n",
    "        if pd.api.types.is_string_dtype(content):\n",
    "        \n",
    "            df_t[label] = content.astype(\"category\").cat.as_ordered()\n",
    "    \n",
    "    for label,content in df_t.items():\n",
    "        if pd.api.types.is_numeric_dtype(content):\n",
    "            if pd.isnull(content).sum():\n",
    "            \n",
    "                ## add a binary column to show if data was missing or not\n",
    "                df_t[label+\"_is_missing\"] = pd.isnull(content)\n",
    "                ## fill missing values with median\n",
    "                df_t[label] = content.fillna(content.median)\n",
    "    # # now lets convert categorical variables to numbers\n",
    "    for label,content in df_t.items():\n",
    "        if not pd.api.types.is_numeric_dtype(content):\n",
    "            df_t[label+\"_is_missing\"] = pd.isnull(content)\n",
    "            df_t[label] = pd.Categorical(content).codes+1 \n",
    "            \n",
    "    return df_t        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2706186d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pro = preprocess_data(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "89aa2475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'auctioneerID_is_missing'}"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(x_train) - set(test_data_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c00ebe46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 12457 entries, 0 to 12456\n",
      "Series name: auctioneerID\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "12457 non-null  int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 97.4 KB\n"
     ]
    }
   ],
   "source": [
    "test_data_pro[\"auctioneerID\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "eca4d19f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pro['auctioneerID_is_missing'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "2c045ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 102)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data_pro.columns), len(x_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a0d5bb13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(set(), set())"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(x_train) - set(test_data_pro), set(test_data_pro)-set(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "82615db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### reoder test data the columns to match the train data\n",
    "test_data_pro = test_data_pro[x_train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "d200ce77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## now all our dataframes have same features, time to predict\n",
    "test_preds = fast_model.predict(test_data_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "e47571ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>21277.646982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>18121.253637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>49751.559221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>87895.857983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>56633.168488</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>61071.552858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>6643173</td>\n",
       "      <td>11278.464311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>6643184</td>\n",
       "      <td>11569.579139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>6643186</td>\n",
       "      <td>16888.575738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>6643196</td>\n",
       "      <td>29434.507407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID     SalePrice\n",
       "0      1227829  21277.646982\n",
       "1      1227844  18121.253637\n",
       "2      1227847  49751.559221\n",
       "3      1227848  87895.857983\n",
       "4      1227863  56633.168488\n",
       "...        ...           ...\n",
       "12452  6643171  61071.552858\n",
       "12453  6643173  11278.464311\n",
       "12454  6643184  11569.579139\n",
       "12455  6643186  16888.575738\n",
       "12456  6643196  29434.507407\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrame compatible with Kaggle submission requirements \n",
    "df_kagg = pd.DataFrame()\n",
    "df_kagg['SalesID'] = test_data[\"SalesID\"]\n",
    "df_kagg['SalePrice'] = test_preds\n",
    "df_kagg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "8c8b71d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.50942488e-02, 1.06039097e-02, 3.13311738e-02, 1.15036857e-04,\n",
       "       2.26669917e-03, 2.77057228e-01, 1.58226497e-03, 8.53575350e-04,\n",
       "       1.96260755e-02, 8.79733376e-02, 5.32633899e-02, 3.92557408e-03,\n",
       "       3.14954390e-03, 2.42230202e-01, 3.96673561e-02, 4.98718859e-03,\n",
       "       1.36734644e-04, 1.26863089e-04, 1.82035885e-03, 7.76491303e-03,\n",
       "       1.63291367e-04, 2.18145691e-05, 3.61646456e-04, 1.80852533e-04,\n",
       "       1.35083719e-03, 5.69065866e-06, 4.84004752e-06, 2.82860215e-03,\n",
       "       9.36858777e-05, 1.24046292e-05, 2.33837295e-03, 2.57350690e-03,\n",
       "       1.81996161e-03, 3.83083534e-03, 1.35312396e-04, 1.35737981e-03,\n",
       "       4.56998475e-04, 1.60267540e-02, 2.79305588e-06, 4.50374649e-07,\n",
       "       1.34688442e-04, 8.21322230e-04, 8.74268232e-04, 3.84524527e-04,\n",
       "       1.30323762e-04, 1.84000230e-04, 2.03658445e-04, 1.08548350e-03,\n",
       "       7.51482103e-04, 1.11008235e-04, 1.92440257e-04, 1.47989080e-03,\n",
       "       7.82310162e-02, 4.57224674e-03, 2.13263078e-03, 1.05225386e-02,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.14223945e-04, 0.00000000e+00,\n",
       "       0.00000000e+00, 6.81598887e-04, 5.98287806e-04, 7.09148431e-04,\n",
       "       2.53539228e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.67051765e-06, 0.00000000e+00, 4.60241397e-05,\n",
       "       1.77615396e-06, 3.88786714e-05, 7.10526678e-07, 6.32920262e-05,\n",
       "       1.19750632e-06, 4.20223487e-06, 9.78148299e-07, 5.31625551e-04,\n",
       "       6.06395376e-04, 1.36318002e-03, 1.06832688e-03, 6.39429860e-05,\n",
       "       4.89177577e-03, 5.37692110e-04, 4.80564568e-04, 1.42800086e-04,\n",
       "       1.60117414e-02, 5.80294901e-07, 7.91887298e-07, 1.04543387e-05,\n",
       "       1.94855333e-05, 2.25790781e-05, 2.76890361e-05, 1.08964968e-05,\n",
       "       4.17293166e-06, 2.54625459e-04, 9.10173922e-05, 4.30059872e-05,\n",
       "       3.76723431e-05, 2.83754182e-05])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### feature importance, which features where most important predicting the price\n",
    "fast_model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a5b646aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Helper function for plotting feature importance\n",
    "def plot_features(columns, importances, n=20):\n",
    "    df = (pd.DataFrame({\"features\": columns,\n",
    "                        \"feature_importance\": importances})\n",
    "          .sort_values(\"feature_importance\", ascending=False)\n",
    "          .reset_index(drop=True))\n",
    "    fig,ax = plt.subplots(figsize=(10,6))\n",
    "    sns.barplot(ax=ax,\n",
    "                x=\"feature_importance\",\n",
    "                y=\"features\",\n",
    "                data=df[:n],\n",
    "                orient=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "968beb30",
   "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, fast_model.feature_importances_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}