Bulldozer Price Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8332c357",
   "metadata": {},
   "source": [
    "# Predicting Bulldozer Prices \n",
    "\n",
    "* Using Machine Learning to predict the sale prices of bulldozers.\n",
    "\n",
    "* The data is downloaded from the Kaggle Bluebook for Bulldozers.\n",
    "\n",
    "* The evaluation metric is the RMSLE (root mean squared Log Error) between the predicted and actual auction sale prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "046e4985",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd28eb97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing training and validation sets\n",
    "\n",
    "df = pd.read_csv(\"TrainAndValid.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b42ecd70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   saledate                  412698 non-null  object \n",
      " 10  fiModelDesc               412698 non-null  object \n",
      " 11  fiBaseModel               412698 non-null  object \n",
      " 12  fiSecondaryDesc           271971 non-null  object \n",
      " 13  fiModelSeries             58667 non-null   object \n",
      " 14  fiModelDescriptor         74816 non-null   object \n",
      " 15  ProductSize               196093 non-null  object \n",
      " 16  fiProductClassDesc        412698 non-null  object \n",
      " 17  state                     412698 non-null  object \n",
      " 18  ProductGroup              412698 non-null  object \n",
      " 19  ProductGroupDesc          412698 non-null  object \n",
      " 20  Drive_System              107087 non-null  object \n",
      " 21  Enclosure                 412364 non-null  object \n",
      " 22  Forks                     197715 non-null  object \n",
      " 23  Pad_Type                  81096 non-null   object \n",
      " 24  Ride_Control              152728 non-null  object \n",
      " 25  Stick                     81096 non-null   object \n",
      " 26  Transmission              188007 non-null  object \n",
      " 27  Turbocharged              81096 non-null   object \n",
      " 28  Blade_Extension           25983 non-null   object \n",
      " 29  Blade_Width               25983 non-null   object \n",
      " 30  Enclosure_Type            25983 non-null   object \n",
      " 31  Engine_Horsepower         25983 non-null   object \n",
      " 32  Hydraulics                330133 non-null  object \n",
      " 33  Pushblock                 25983 non-null   object \n",
      " 34  Ripper                    106945 non-null  object \n",
      " 35  Scarifier                 25994 non-null   object \n",
      " 36  Tip_Control               25983 non-null   object \n",
      " 37  Tire_Size                 97638 non-null   object \n",
      " 38  Coupler                   220679 non-null  object \n",
      " 39  Coupler_System            44974 non-null   object \n",
      " 40  Grouser_Tracks            44875 non-null   object \n",
      " 41  Hydraulics_Flow           44875 non-null   object \n",
      " 42  Track_Type                102193 non-null  object \n",
      " 43  Undercarriage_Pad_Width   102916 non-null  object \n",
      " 44  Stick_Length              102261 non-null  object \n",
      " 45  Thumb                     102332 non-null  object \n",
      " 46  Pattern_Changer           102261 non-null  object \n",
      " 47  Grouser_Type              102193 non-null  object \n",
      " 48  Backhoe_Mounting          80712 non-null   object \n",
      " 49  Blade_Type                81875 non-null   object \n",
      " 50  Travel_Controls           81877 non-null   object \n",
      " 51  Differential_Type         71564 non-null   object \n",
      " 52  Steering_Controls         71522 non-null   object \n",
      "dtypes: float64(3), int64(5), object(45)\n",
      "memory usage: 166.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3992c5da",
   "metadata": {},
   "source": [
    "## Columns with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98f97bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                          0\n",
       "SalePrice                        0\n",
       "MachineID                        0\n",
       "ModelID                          0\n",
       "datasource                       0\n",
       "auctioneerID                 20136\n",
       "YearMade                         0\n",
       "MachineHoursCurrentMeter    265194\n",
       "UsageBand                   339028\n",
       "saledate                         0\n",
       "fiModelDesc                      0\n",
       "fiBaseModel                      0\n",
       "fiSecondaryDesc             140727\n",
       "fiModelSeries               354031\n",
       "fiModelDescriptor           337882\n",
       "ProductSize                 216605\n",
       "fiProductClassDesc               0\n",
       "state                            0\n",
       "ProductGroup                     0\n",
       "ProductGroupDesc                 0\n",
       "Drive_System                305611\n",
       "Enclosure                      334\n",
       "Forks                       214983\n",
       "Pad_Type                    331602\n",
       "Ride_Control                259970\n",
       "Stick                       331602\n",
       "Transmission                224691\n",
       "Turbocharged                331602\n",
       "Blade_Extension             386715\n",
       "Blade_Width                 386715\n",
       "Enclosure_Type              386715\n",
       "Engine_Horsepower           386715\n",
       "Hydraulics                   82565\n",
       "Pushblock                   386715\n",
       "Ripper                      305753\n",
       "Scarifier                   386704\n",
       "Tip_Control                 386715\n",
       "Tire_Size                   315060\n",
       "Coupler                     192019\n",
       "Coupler_System              367724\n",
       "Grouser_Tracks              367823\n",
       "Hydraulics_Flow             367823\n",
       "Track_Type                  310505\n",
       "Undercarriage_Pad_Width     309782\n",
       "Stick_Length                310437\n",
       "Thumb                       310366\n",
       "Pattern_Changer             310437\n",
       "Grouser_Type                310505\n",
       "Backhoe_Mounting            331986\n",
       "Blade_Type                  330823\n",
       "Travel_Controls             330821\n",
       "Differential_Type           341134\n",
       "Steering_Controls           341176\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5648e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2179ce9f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u8vLytGjRIm3fvl1Op1PDhw/XiRMnzJqsrCytWbNG+fn5Ki4u1smTJ5WamqqGhgazJj09XR6PRwUFBSooKJDH45Hb7TbHGxoalJKSolOnTqm4uFj5+flatWqVsrOzzZqamhoNHz5cLpdL27dv1/PPP6/58+crLy/vKswMAAAIRDajlSy52Gw2rVmzRvfee6+kc6tVLpdLWVlZmjZtmqRzq1OxsbGaM2eOxo8fL5/PpxtvvFGvvfaaHnjgAUnS4cOHFRcXp3feeUfJycnau3evevbsqZKSEvXr10+SVFJSosTERH3yySfq3r273n33XaWmpqqiokIul0uSlJ+fr7Fjx6q6ulqRkZFavHixZsyYoSNHjshut0uSZs+ereeff14HDx6UzWa7rOOsqamRw+GQz+dTZGSklVOoLtPXWro/XNzns1NaugUAwLfscj+/W+05Vvv371dVVZWSkpLMbXa7XYMGDdLmzZslSaWlpaqvr/ercblcio+PN2u2bNkih8NhhipJ6t+/vxwOh19NfHy8GaokKTk5WbW1tSotLTVrBg0aZIaq8zWHDx/W559//pXHUVtbq5qaGr8HAABom1ptsKqqqpIkxcbG+m2PjY01x6qqqhQaGqqoqKhL1sTExDTZf0xMjF/Nhe8TFRWl0NDQS9acf36+5mJyc3PNc7scDofi4uIufeAAACBgtdpgdd6FX7EZhvG1X7tdWHOxeitqzn+Leql+ZsyYIZ/PZz4qKiou2TsAAAhcrTZYOZ1OSU1Xg6qrq82VIqfTqbq6Onm93kvWHDlypMn+jx496ldz4ft4vV7V19dfsqa6ulpS01W1L7Pb7YqMjPR7AACAtqnVBquuXbvK6XSqqKjI3FZXV6eNGzdqwIABkqSEhASFhIT41VRWVqqsrMysSUxMlM/n07Zt28yarVu3yufz+dWUlZWpsrLSrCksLJTdbldCQoJZs2nTJr9LMBQWFsrlcqlLly7WTwAAAAg4LRqsTp48KY/HI4/HI+ncCesej0fl5eWy2WzKyspSTk6O1qxZo7KyMo0dO1bt2rVTenq6JMnhcGjcuHHKzs7W+vXrtWvXLo0ePVq9e/fWsGHDJEk9evTQiBEjlJGRoZKSEpWUlCgjI0Opqanq3r27JCkpKUk9e/aU2+3Wrl27tH79ek2dOlUZGRnmClN6errsdrvGjh2rsrIyrVmzRjk5OZoyZcpl/yIQAAC0bcEt+eY7duzQkCFDzOdTpkyRJI0ZM0bLly/XU089pdOnT2vChAnyer3q16+fCgsLFRERYb5mwYIFCg4O1qhRo3T69GkNHTpUy5cvV1BQkFmzcuVKZWZmmr8eTEtL87t2VlBQkNauXasJEyZo4MCBCgsLU3p6uubPn2/WOBwOFRUV6YknnlDfvn0VFRWlKVOmmD0DAAC0mutYXSu4jlXg4zpWAHDtCfjrWAEAAAQaghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYJFmBav9+/db3QcAAEDAa1aw+s53vqMhQ4ZoxYoVOnPmjNU9AQAABKRmBasPP/xQffr0UXZ2tpxOp8aPH69t27ZZ3RsAAEBAaVawio+PV15eng4dOqRly5apqqpKd911l3r16qW8vDwdPXrU6j4BAABavW908npwcLDuu+8+/fGPf9ScOXP097//XVOnTlXHjh31yCOPqLKy0qo+AQAAWr1vFKx27NihCRMm6KabblJeXp6mTp2qv//973r//fd16NAh/fjHP7aqTwAAgFYvuDkvysvL07Jly/Tpp5/qnnvu0auvvqp77rlH1113Lqd17dpVS5Ys0W233WZpswAAAK1Zs4LV4sWL9eijj+qnP/2pnE7nRWs6deqkl19++Rs1BwAAEEiaFaw+++yzr60JDQ3VmDFjmrN7AACAgNSsc6yWLVumP/3pT022/+lPf9Irr7zyjZsCAAAIRM0KVrNnz1Z0dHST7TExMcrJyfnGTQEAAASiZgWrAwcOqGvXrk22d+7cWeXl5d+4KQAAgEDUrGAVExOjjz76qMn2Dz/8UB06dPjGTQEAAASiZgWrBx98UJmZmdqwYYMaGhrU0NCg999/X5MnT9aDDz5odY8AAAABoVm/Cvztb3+rAwcOaOjQoQoOPreLxsZGPfLII5xjBQAArlnNClahoaF688039V//9V/68MMPFRYWpt69e6tz585W9wcAABAwmhWszrv11lt16623WtULAABAQGtWsGpoaNDy5cu1fv16VVdXq7Gx0W/8/ffft6Q5AACAQNKsYDV58mQtX75cKSkpio+Pl81ms7ovAACAgNOsYJWfn68//vGPuueee6zuBwAAIGA163ILoaGh+s53vmN1LwAAAAGtWcEqOztbv/vd72QYhtX9AAAABKxmfRVYXFysDRs26N1331WvXr0UEhLiN7569WpLmgMAAAgkzQpWN9xwg+677z6rewEAAAhozQpWy5Yts7oPAACAgNesc6wk6ezZs1q3bp2WLFmiEydOSJIOHz6skydPWtYcAABAIGnWitWBAwc0YsQIlZeXq7a2VsOHD1dERITmzp2rM2fO6MUXX7S6TwAAgFavWStWkydPVt++feX1ehUWFmZuv++++7R+/XrLmjt79qx++ctfqmvXrgoLC9Mtt9yiWbNm+V3p3TAMzZw5Uy6XS2FhYRo8eLD27Nnjt5/a2lpNmjRJ0dHRCg8PV1pamg4ePOhX4/V65Xa75XA45HA45Ha7dfz4cb+a8vJyjRw5UuHh4YqOjlZmZqbq6uosO14AABDYmhWsiouL9ctf/lKhoaF+2zt37qxDhw5Z0pgkzZkzRy+++KIWLVqkvXv3au7cuZo3b56ef/55s2bu3LnKy8vTokWLtH37djmdTg0fPtz8elKSsrKytGbNGuXn56u4uFgnT55UamqqGhoazJr09HR5PB4VFBSooKBAHo9HbrfbHG9oaFBKSopOnTql4uJi5efna9WqVcrOzrbseAEAQGBr1leBjY2NfqHkvIMHDyoiIuIbN3Xeli1b9OMf/1gpKSmSpC5duuiNN97Qjh07JJ1brVq4cKGefvpp3X///ZKkV155RbGxsXr99dc1fvx4+Xw+vfzyy3rttdc0bNgwSdKKFSsUFxendevWKTk5WXv37lVBQYFKSkrUr18/SdLSpUuVmJioTz/9VN27d1dhYaE+/vhjVVRUyOVySZKee+45jR07Vs8++6wiIyMtO24AABCYmrViNXz4cC1cuNB8brPZdPLkSf3mN7+x9DY3d911l9avX699+/ZJkj788EMVFxeb77F//35VVVUpKSnJfI3dbtegQYO0efNmSVJpaanq6+v9alwul+Lj482aLVu2yOFwmKFKkvr37y+Hw+FXEx8fb4YqSUpOTlZtba1KS0u/8hhqa2tVU1Pj9wAAAG1Ts1asFixYoCFDhqhnz546c+aM0tPT9dlnnyk6OlpvvPGGZc1NmzZNPp9Pt912m4KCgtTQ0KBnn31WDz30kCSpqqpKkhQbG+v3utjYWB04cMCsCQ0NVVRUVJOa86+vqqpSTExMk/ePiYnxq7nwfaKiohQaGmrWXExubq6eeeaZKzlsAAAQoJoVrFwulzwej9544w3t3LlTjY2NGjdunB5++GG/k9m/qTfffFMrVqzQ66+/rl69esnj8SgrK0sul0tjxowx62w2m9/rDMNosu1CF9ZcrL45NReaMWOGpkyZYj6vqalRXFzcJXsDAACBqVnBSpLCwsL06KOP6tFHH7WyHz9PPvmkpk+frgcffFCS1Lt3bx04cEC5ubkaM2aMnE6npHOrSTfddJP5uurqanN1yel0qq6uTl6v12/Vqrq6WgMGDDBrjhw50uT9jx496refrVu3+o17vV7V19c3Wcn6MrvdLrvd3pzDBwAAAaZZwerVV1+95PgjjzzSrGYu9MUXX+i66/xPAwsKCjIvt9C1a1c5nU4VFRWpT58+kqS6ujpt3LhRc+bMkSQlJCQoJCRERUVFGjVqlCSpsrJSZWVlmjt3riQpMTFRPp9P27Zt05133ilJ2rp1q3w+nxm+EhMT9eyzz6qystIMcYWFhbLb7UpISLDkeAEAQGBrVrCaPHmy3/P6+np98cUXCg0NVbt27SwLViNHjtSzzz6rTp06qVevXtq1a5fy8vLMVTKbzaasrCzl5OSoW7du6tatm3JyctSuXTulp6dLkhwOh8aNG6fs7Gx16NBB7du319SpU9W7d2/zV4I9evTQiBEjlJGRoSVLlkiSHnvsMaWmpqp79+6SpKSkJPXs2VNut1vz5s3TsWPHNHXqVGVkZPCLQAAAIKmZwcrr9TbZ9tlnn+nnP/+5nnzyyW/c1HnPP/+8fvWrX2nChAmqrq6Wy+XS+PHj9etf/9qseeqpp3T69GlNmDBBXq9X/fr1U2Fhod9lHxYsWKDg4GCNGjVKp0+f1tChQ7V8+XIFBQWZNStXrlRmZqb568G0tDQtWrTIHA8KCtLatWs1YcIEDRw4UGFhYUpPT9f8+fMtO14AABDYbIZhGFbtbMeOHRo9erQ++eQTq3bZ5tTU1MjhcMjn81m+0tVl+lpL94eL+3x2Sku3AAD4ll3u53ezb8J8MUFBQTp8+LCVuwQAAAgYzfoq8O233/Z7bhiGKisrtWjRIg0cONCSxgAAAAJNs4LVvffe6/fcZrPpxhtv1A9/+EM999xzVvQFAAAQcJp9r0AAAAD4s/QcKwAAgGtZs1asvnyLlq+Tl5fXnLcAAAAIOM0KVrt27dLOnTt19uxZ8wKa+/btU1BQkO644w6z7uvu1wcAANCWNCtYjRw5UhEREXrllVfM++95vV799Kc/1d13363s7GxLmwQAAAgEzTrH6rnnnlNubq7fTY2joqL029/+ll8FAgCAa1azglVNTY2OHDnSZHt1dbVOnDjxjZsCAAAIRM0KVvfdd59++tOf6s9//rMOHjyogwcP6s9//rPGjRun+++/3+oeAQAAAkKzzrF68cUXNXXqVI0ePVr19fXndhQcrHHjxmnevHmWNggAABAomhWs2rVrpxdeeEHz5s3T3//+dxmGoe985zsKDw+3uj8AAICA8Y0uEFpZWanKykrdeuutCg8Pl2EYVvUFAAAQcJoVrP71r39p6NChuvXWW3XPPfeosrJSkvSzn/2MSy0AAIBrVrOC1S9+8QuFhISovLxc7dq1M7c/8MADKigosKw5AACAQNKsc6wKCwv13nvvqWPHjn7bu3XrpgMHDljSGAAAQKBp1orVqVOn/FaqzvvnP/8pu93+jZsCAAAIRM0KVj/4wQ/06quvms9tNpsaGxs1b948DRkyxLLmAAAAAkmzvgqcN2+eBg8erB07dqiurk5PPfWU9uzZo2PHjumvf/2r1T0CAAAEhGatWPXs2VMfffSR7rzzTg0fPlynTp3S/fffr127dunf//3fre4RAAAgIFzxilV9fb2SkpK0ZMkSPfPMM1ejJwAAgIB0xStWISEhKisrk81muxr9AAAABKxmfRX4yCOP6OWXX7a6FwAAgIDWrJPX6+rq9Ic//EFFRUXq27dvk3sE5uXlWdIcAABAILmiYPWPf/xDXbp0UVlZme644w5J0r59+/xq+IoQAABcq64oWHXr1k2VlZXasGGDpHO3sPn973+v2NjYq9IcAABAILmic6wMw/B7/u677+rUqVOWNgQAABComnXy+nkXBi0AAIBr2RUFK5vN1uQcKs6pAgAAOOeKzrEyDENjx441b7R85swZPf74401+Fbh69WrrOgQAAAgQVxSsxowZ4/d89OjRljYDAAAQyK4oWC1btuxq9QEAABDwvtHJ6wAAAPj/EawAAAAs0uqD1aFDhzR69Gh16NBB7dq10/e+9z2Vlpaa44ZhaObMmXK5XAoLC9PgwYO1Z88ev33U1tZq0qRJio6OVnh4uNLS0nTw4EG/Gq/XK7fbLYfDIYfDIbfbrePHj/vVlJeXa+TIkQoPD1d0dLQyMzNVV1d31Y4dAAAEllYdrLxerwYOHKiQkBC9++67+vjjj/Xcc8/phhtuMGvmzp2rvLw8LVq0SNu3b5fT6dTw4cN14sQJsyYrK0tr1qxRfn6+iouLdfLkSaWmpqqhocGsSU9Pl8fjUUFBgQoKCuTxeOR2u83xhoYGpaSk6NSpUyouLlZ+fr5WrVql7Ozsb2UuAABA62czWvFVPqdPn66//vWv+uCDDy46bhiGXC6XsrKyNG3aNEnnVqdiY2M1Z84cjR8/Xj6fTzfeeKNee+01PfDAA5Kkw4cPKy4uTu+8846Sk5O1d+9e9ezZUyUlJerXr58kqaSkRImJifrkk0/UvXt3vfvuu0pNTVVFRYVcLpckKT8/X2PHjlV1dbUiIyMv65hqamrkcDjk8/ku+zWXq8v0tZbuDxf3+eyUlm4BAPAtu9zP71a9YvX222+rb9+++slPfqKYmBj16dNHS5cuNcf379+vqqoqJSUlmdvsdrsGDRqkzZs3S5JKS0tVX1/vV+NyuRQfH2/WbNmyRQ6HwwxVktS/f385HA6/mvj4eDNUSVJycrJqa2v9vpq8UG1trWpqavweAACgbWrVweof//iHFi9erG7duum9997T448/rszMTL366quSpKqqKklqchPo2NhYc6yqqkqhoaGKioq6ZE1MTEyT94+JifGrufB9oqKiFBoaatZcTG5urnnelsPhUFxc3JVMAQAACCCtOlg1NjbqjjvuUE5Ojvr06aPx48crIyNDixcv9qu78LY6hmF87a12Lqy5WH1zai40Y8YM+Xw+81FRUXHJvgAAQOBq1cHqpptuUs+ePf229ejRQ+Xl5ZIkp9MpSU1WjKqrq83VJafTqbq6Onm93kvWHDlypMn7Hz161K/mwvfxer2qr69vspL1ZXa7XZGRkX4PAADQNrXqYDVw4EB9+umnftv27dunzp07S5K6du0qp9OpoqIic7yurk4bN27UgAEDJEkJCQkKCQnxq6msrFRZWZlZk5iYKJ/Pp23btpk1W7dulc/n86spKytTZWWlWVNYWCi73a6EhASLjxwAAASiK7qlzbftF7/4hQYMGKCcnByNGjVK27Zt00svvaSXXnpJ0rmv5rKyspSTk6Nu3bqpW7duysnJUbt27ZSeni5JcjgcGjdunLKzs9WhQwe1b99eU6dOVe/evTVs2DBJ51bBRowYoYyMDC1ZskSS9Nhjjyk1NVXdu3eXJCUlJalnz55yu92aN2+ejh07pqlTpyojI4NVKAAAIKmVB6vvf//7WrNmjWbMmKFZs2apa9euWrhwoR5++GGz5qmnntLp06c1YcIEeb1e9evXT4WFhYqIiDBrFixYoODgYI0aNUqnT5/W0KFDtXz5cgUFBZk1K1euVGZmpvnrwbS0NC1atMgcDwoK0tq1azVhwgQNHDhQYWFhSk9P1/z587+FmUBrEoiXteASEQDw7WjV17Fqi7iOFVoCwQoAvpk2cR0rAACAQEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAiwRUsMrNzZXNZlNWVpa5zTAMzZw5Uy6XS2FhYRo8eLD27Nnj97ra2lpNmjRJ0dHRCg8PV1pamg4ePOhX4/V65Xa75XA45HA45Ha7dfz4cb+a8vJyjRw5UuHh4YqOjlZmZqbq6uqu1uECAIAAEzDBavv27XrppZf03e9+12/73LlzlZeXp0WLFmn79u1yOp0aPny4Tpw4YdZkZWVpzZo1ys/PV3FxsU6ePKnU1FQ1NDSYNenp6fJ4PCooKFBBQYE8Ho/cbrc53tDQoJSUFJ06dUrFxcXKz8/XqlWrlJ2dffUPHgAABISACFYnT57Uww8/rKVLlyoqKsrcbhiGFi5cqKefflr333+/4uPj9corr+iLL77Q66+/Lkny+Xx6+eWX9dxzz2nYsGHq06ePVqxYod27d2vdunWSpL1796qgoEB/+MMflJiYqMTERC1dulR/+ctf9Omnn0qSCgsL9fHHH2vFihXq06ePhg0bpueee05Lly5VTU3Ntz8pAACg1QmIYPXEE08oJSVFw4YN89u+f/9+VVVVKSkpydxmt9s1aNAgbd68WZJUWlqq+vp6vxqXy6X4+HizZsuWLXI4HOrXr59Z079/fzkcDr+a+Ph4uVwusyY5OVm1tbUqLS39yt5ra2tVU1Pj9wAAAG1TcEs38HXy8/O1c+dObd++vclYVVWVJCk2NtZve2xsrA4cOGDWhIaG+q10na85//qqqirFxMQ02X9MTIxfzYXvExUVpdDQULPmYnJzc/XMM8983WECAIA2oFWvWFVUVGjy5MlasWKFrr/++q+ss9lsfs8Nw2iy7UIX1lysvjk1F5oxY4Z8Pp/5qKiouGRfAAAgcLXqYFVaWqrq6molJCQoODhYwcHB2rhxo37/+98rODjYXEG6cMWourraHHM6naqrq5PX671kzZEjR5q8/9GjR/1qLnwfr9er+vr6JitZX2a32xUZGen3AAAAbVOrDlZDhw7V7t275fF4zEffvn318MMPy+Px6JZbbpHT6VRRUZH5mrq6Om3cuFEDBgyQJCUkJCgkJMSvprKyUmVlZWZNYmKifD6ftm3bZtZs3bpVPp/Pr6asrEyVlZVmTWFhoex2uxISEq7qPAAAgMDQqs+xioiIUHx8vN+28PBwdejQwdyelZWlnJwcdevWTd26dVNOTo7atWun9PR0SZLD4dC4ceOUnZ2tDh06qH379po6dap69+5tngzfo0cPjRgxQhkZGVqyZIkk6bHHHlNqaqq6d+8uSUpKSlLPnj3ldrs1b948HTt2TFOnTlVGRgarUAAAQFIrD1aX46mnntLp06c1YcIEeb1e9evXT4WFhYqIiDBrFixYoODgYI0aNUqnT5/W0KFDtXz5cgUFBZk1K1euVGZmpvnrwbS0NC1atMgcDwoK0tq1azVhwgQNHDhQYWFhSk9P1/z587+9gwUAAK2azTAMo6WbuJbU1NTI4XDI5/NZvtLVZfpaS/eHtuPz2Skt3QIABLTL/fxu1edYAQAABBKCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFgn4mzAD+HqBeB9J7m8IIBCxYgUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYJHglm4AAC6my/S1Ld1Cs3w+O6WlWwDQglixAgAAsAjBCgAAwCKtOljl5ubq+9//viIiIhQTE6N7771Xn376qV+NYRiaOXOmXC6XwsLCNHjwYO3Zs8evpra2VpMmTVJ0dLTCw8OVlpamgwcP+tV4vV653W45HA45HA653W4dP37cr6a8vFwjR45UeHi4oqOjlZmZqbq6uqty7AAAIPC06mC1ceNGPfHEEyopKVFRUZHOnj2rpKQknTp1yqyZO3eu8vLytGjRIm3fvl1Op1PDhw/XiRMnzJqsrCytWbNG+fn5Ki4u1smTJ5WamqqGhgazJj09XR6PRwUFBSooKJDH45Hb7TbHGxoalJKSolOnTqm4uFj5+flatWqVsrOzv53JAAAArZ7NMAyjpZu4XEePHlVMTIw2btyoH/zgBzIMQy6XS1lZWZo2bZqkc6tTsbGxmjNnjsaPHy+fz6cbb7xRr732mh544AFJ0uHDhxUXF6d33nlHycnJ2rt3r3r27KmSkhL169dPklRSUqLExER98skn6t69u959912lpqaqoqJCLpdLkpSfn6+xY8equrpakZGRl3UMNTU1cjgc8vl8l/2ayxWoJ/sCbQknrwNt0+V+frfqFasL+Xw+SVL79u0lSfv371dVVZWSkpLMGrvdrkGDBmnz5s2SpNLSUtXX1/vVuFwuxcfHmzVbtmyRw+EwQ5Uk9e/fXw6Hw68mPj7eDFWSlJycrNraWpWWln5lz7W1taqpqfF7AACAtilggpVhGJoyZYruuusuxcfHS5KqqqokSbGxsX61sbGx5lhVVZVCQ0MVFRV1yZqYmJgm7xkTE+NXc+H7REVFKTQ01Ky5mNzcXPO8LYfDobi4uCs5bAAAEEACJlhNnDhRH330kd54440mYzabze+5YRhNtl3owpqL1Ten5kIzZsyQz+czHxUVFZfsCwAABK6ACFaTJk3S22+/rQ0bNqhjx47mdqfTKUlNVoyqq6vN1SWn06m6ujp5vd5L1hw5cqTJ+x49etSv5sL38Xq9qq+vb7KS9WV2u12RkZF+DwAA0Da16mBlGIYmTpyo1atX6/3331fXrl39xrt27Sqn06mioiJzW11dnTZu3KgBAwZIkhISEhQSEuJXU1lZqbKyMrMmMTFRPp9P27ZtM2u2bt0qn8/nV1NWVqbKykqzprCwUHa7XQkJCdYfPAAACDit+pY2TzzxhF5//XX9z//8jyIiIswVI4fDobCwMNlsNmVlZSknJ0fdunVTt27dlJOTo3bt2ik9Pd2sHTdunLKzs9WhQwe1b99eU6dOVe/evTVs2DBJUo8ePTRixAhlZGRoyZIlkqTHHntMqamp6t69uyQpKSlJPXv2lNvt1rx583Ts2DFNnTpVGRkZrEIBAABJrTxYLV68WJI0ePBgv+3Lli3T2LFjJUlPPfWUTp8+rQkTJsjr9apfv34qLCxURESEWb9gwQIFBwdr1KhROn36tIYOHarly5crKCjIrFm5cqUyMzPNXw+mpaVp0aJF5nhQUJDWrl2rCRMmaODAgQoLC1N6errmz59/lY4eAAAEmoC6jlVbwHWsgLaN61gBbVObvI4VAABAa0awAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLtOorrwNAoAnEC/VyUVPAOqxYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhCuvA8A1jqvFA9ZhxQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACzCvQIBAAGH+xuitWLFCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIJ683wwsvvKB58+apsrJSvXr10sKFC3X33Xe3dFsAgFaME+6vDaxYXaE333xTWVlZevrpp7Vr1y7dfffd+tGPfqTy8vKWbg0AALQwgtUVysvL07hx4/Szn/1MPXr00MKFCxUXF6fFixe3dGsAAKCF8VXgFairq1NpaammT5/utz0pKUmbN2++6Gtqa2tVW1trPvf5fJKkmpoay/trrP3C8n0CAK5dnX7xp5Zu4YqVPZN8VfZ7/nPbMIxL1hGsrsA///lPNTQ0KDY21m97bGysqqqqLvqa3NxcPfPMM022x8XFXZUeAQC4ljkWXt39nzhxQg6H4yvHCVbNYLPZ/J4bhtFk23kzZszQlClTzOeNjY06duyYOnTo4PeampoaxcXFqaKiQpGRkVen8QDAPJzDPJzDPDAH5zEP5zAPLTcHhmHoxIkTcrlcl6wjWF2B6OhoBQUFNVmdqq6ubrKKdZ7dbpfdbvfbdsMNN3zle0RGRl6z/2f5MubhHObhHOaBOTiPeTiHeWiZObjUStV5nLx+BUJDQ5WQkKCioiK/7UVFRRowYEALdQUAAFoLVqyu0JQpU+R2u9W3b18lJibqpZdeUnl5uR5//PGWbg0AALQwgtUVeuCBB/Svf/1Ls2bNUmVlpeLj4/XOO++oc+fO32i/drtdv/nNb5p8bXitYR7OYR7OYR6Yg/OYh3OYh9Y/Bzbj6343CAAAgMvCOVYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWrcQLL7ygrl276vrrr1dCQoI++OCDlm7psuTm5ur73/++IiIiFBMTo3vvvVeffvqpX41hGJo5c6ZcLpfCwsI0ePBg7dmzx6+mtrZWkyZNUnR0tMLDw5WWlqaDBw/61Xi9XrndbjkcDjkcDrndbh0/ftyvpry8XCNHjlR4eLiio6OVmZmpurq6q3LsXyU3N1c2m01ZWVnmtmtlDg4dOqTRo0erQ4cOateunb73ve+ptLTUHL8W5uHs2bP65S9/qa5duyosLEy33HKLZs2apcbGRrOmLc7Dpk2bNHLkSLlcLtlsNr311lt+463tmHfv3q1BgwYpLCxMN998s2bNmvW194D7pvNQX1+vadOmqXfv3goPD5fL5dIjjzyiw4cPt6l5+Lp/C182fvx42Ww2LVy40G97QM+BgRaXn59vhISEGEuXLjU+/vhjY/LkyUZ4eLhx4MCBlm7tayUnJxvLli0zysrKDI/HY6SkpBidOnUyTp48adbMnj3biIiIMFatWmXs3r3beOCBB4ybbrrJqKmpMWsef/xx4+abbzaKioqMnTt3GkOGDDFuv/124+zZs2bNiBEjjPj4eGPz5s3G5s2bjfj4eCM1NdUcP3v2rBEfH28MGTLE2Llzp1FUVGS4XC5j4sSJ385kGIaxbds2o0uXLsZ3v/tdY/Lkyeb2a2EOjh07ZnTu3NkYO3assXXrVmP//v3GunXrjL/97W/X1Dz89re/NTp06GD85S9/Mfbv32/86U9/Mv7t3/7NWLhwYZueh3feecd4+umnjVWrVhmSjDVr1viNt6Zj9vl8RmxsrPHggw8au3fvNlatWmVEREQY8+fPv6rzcPz4cWPYsGHGm2++aXzyySfGli1bjH79+hkJCQl++wj0efi6fwvnrVmzxrj99tsNl8tlLFiwoM3MAcGqFbjzzjuNxx9/3G/bbbfdZkyfPr2FOmq+6upqQ5KxceNGwzAMo7Gx0XA6ncbs2bPNmjNnzhgOh8N48cUXDcM498cmJCTEyM/PN2sOHTpkXHfddUZBQYFhGIbx8ccfG5KMkpISs2bLli2GJOOTTz4xDOPc/5mvu+4649ChQ2bNG2+8YdjtdsPn8129g/5/Tpw4YXTr1s0oKioyBg0aZAara2UOpk2bZtx1111fOX6tzENKSorx6KOP+m27//77jdGjRxuGcW3Mw4Ufpq3tmF944QXD4XAYZ86cMWtyc3MNl8tlNDY2XrV5uJht27YZksz/kG5r8/BVc3Dw4EHj5ptvNsrKyozOnTv7BatAnwO+CmxhdXV1Ki0tVVJSkt/2pKQkbd68uYW6aj6fzydJat++vSRp//79qqqq8js+u92uQYMGmcdXWlqq+vp6vxqXy6X4+HizZsuWLXI4HOrXr59Z079/fzkcDr+a+Ph4vxtkJicnq7a21u/rqKvliSeeUEpKioYNG+a3/VqZg7ffflt9+/bVT37yE8XExKhPnz5aunSpOX6tzMNdd92l9evXa9++fZKkDz/8UMXFxbrnnnskXTvz8GWt7Zi3bNmiQYMG+V1gMjk5WYcPH9bnn39u/QRcgs/nk81mM+8hey3MQ2Njo9xut5588kn16tWryXigzwHBqoX985//VENDQ5ObOMfGxja52XNrZxiGpkyZorvuukvx8fGSZB7DpY6vqqpKoaGhioqKumRNTExMk/eMiYnxq7nwfaKiohQaGnrV5zI/P187d+5Ubm5uk7FrZQ7+8Y9/aPHixerWrZvee+89Pf7448rMzNSrr75q9ia1/XmYNm2aHnroId12220KCQlRnz59lJWVpYceesjsTWr78/Blre2YL1Zz/vm3OS9nzpzR9OnTlZ6ebt5M+FqYhzlz5ig4OFiZmZkXHQ/0OeCWNq2EzWbze24YRpNtrd3EiRP10Ucfqbi4uMlYc47vwpqL1TenxmoVFRWaPHmyCgsLdf31139lXVueA+ncf4X27dtXOTk5kqQ+ffpoz549Wrx4sR555JGv7K+tzcObb76pFStW6PXXX1evXr3k8XiUlZUll8ulMWPGfGV/bW0eLqY1HfPFevmq114N9fX1evDBB9XY2KgXXnjha+vbyjyUlpbqd7/7nXbu3HnF7xEoc8CKVQuLjo5WUFBQk2RcXV3dJEW3ZpMmTdLbb7+tDRs2qGPHjuZ2p9MpqWny//LxOZ1O1dXVyev1XrLmyJEjTd736NGjfjUXvo/X61V9ff1VncvS0lJVV1crISFBwcHBCg4O1saNG/X73/9ewcHBX/lfP21pDiTppptuUs+ePf229ejRQ+Xl5WZvUtufhyeffFLTp0/Xgw8+qN69e8vtdusXv/iFuZp5rczDl7W2Y75YTXV1taSmq2pXQ319vUaNGqX9+/erqKjIXK0631tbnocPPvhA1dXV6tSpk/n38sCBA8rOzlaXLl3MvgJ5DghWLSw0NFQJCQkqKiry215UVKQBAwa0UFeXzzAMTZw4UatXr9b777+vrl27+o137dpVTqfT7/jq6uq0ceNG8/gSEhIUEhLiV1NZWamysjKzJjExUT6fT9u2bTNrtm7dKp/P51dTVlamyspKs6awsFB2u10JCQnWH/z/M3ToUO3evVsej8d89O3bVw8//LA8Ho9uueWWNj8HkjRw4MAml9rYt2+feYPya+HfgiR98cUXuu46/z+tQUFB5uUWrpV5+LLWdsyJiYnatGmT38/uCwsL5XK5zA/3q+V8qPrss8+0bt06dejQwW+8rc+D2+3WRx995Pf30uVy6cknn9R7773XNuagWae8w1LnL7fw8ssvGx9//LGRlZVlhIeHG59//nlLt/a1fv7znxsOh8P4v//7P6OystJ8fPHFF2bN7NmzDYfDYaxevdrYvXu38dBDD130Z9YdO3Y01q1bZ+zcudP44Q9/eNGf1n73u981tmzZYmzZssXo3bv3RX9aO3ToUGPnzp3GunXrjI4dO36rl1s478u/CjSMa2MOtm3bZgQHBxvPPvus8dlnnxkrV6402rVrZ6xYseKamocxY8YYN998s3m5hdWrVxvR0dHGU0891abn4cSJE8auXbuMXbt2GZKMvLw8Y9euXeav3VrTMR8/ftyIjY01HnroIWP37t3G6tWrjcjISEsut3CpeaivrzfS0tKMjh07Gh6Px+9vZm1tbZuZh6/7t3ChC38VGOhzQLBqJf77v//b6Ny5sxEaGmrccccd5uUKWjtJF30sW7bMrGlsbDR+85vfGE6n07Db7cYPfvADY/fu3X77OX36tDFx4kSjffv2RlhYmJGammqUl5f71fzrX/8yHn74YSMiIsKIiIgwHn74YcPr9frVHDhwwEhJSTHCwsKM9u3bGxMnTvT7Ge235cJgda3Mwf/+7/8a8fHxht1uN2677TbjpZde8hu/FuahpqbGmDx5stGpUyfj+uuvN2655Rbj6aef9vvgbIvzsGHDhov+LRgzZkyrPOaPPvrIuPvuuw273W44nU5j5syZllxi4FLzsH///q/8m7lhw4Y2Mw9f92/hQhcLVoE8BzbDsOBSswAAAOAcKwAAAKsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCL/H/Wg8bJJ9gm1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SalePrice.plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e4372db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing Dates\n",
    "\n",
    "df = pd.read_csv(\"TrainAndValid.csv\", low_memory=False, parse_dates=['saledate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f7d789e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df[\"SalePrice\"][:1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f344c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1139246</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>999089</td>\n",
       "      <td>3157</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2006-11-16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1139248</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>117657</td>\n",
       "      <td>77</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2004-03-26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1139249</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>434808</td>\n",
       "      <td>7009</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2004-02-26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1139251</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>1026470</td>\n",
       "      <td>332</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2011-05-19</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1139253</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1057373</td>\n",
       "      <td>17311</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>722.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2009-07-23</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1139246    66000.0     999089     3157         121           3.0      2004   \n",
       "1  1139248    57000.0     117657       77         121           3.0      1996   \n",
       "2  1139249    10000.0     434808     7009         121           3.0      2001   \n",
       "3  1139251    38500.0    1026470      332         121           3.0      2001   \n",
       "4  1139253    11000.0    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate  ... Undercarriage_Pad_Width  \\\n",
       "0                      68.0       Low 2006-11-16  ...                     NaN   \n",
       "1                    4640.0       Low 2004-03-26  ...                     NaN   \n",
       "2                    2838.0      High 2004-02-26  ...                     NaN   \n",
       "3                    3486.0      High 2011-05-19  ...                     NaN   \n",
       "4                     722.0    Medium 2009-07-23  ...                     NaN   \n",
       "\n",
       "  Stick_Length Thumb Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type  \\\n",
       "0          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "1          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "2          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "3          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "4          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "\n",
       "  Travel_Controls Differential_Type Steering_Controls  \n",
       "0             NaN          Standard      Conventional  \n",
       "1             NaN          Standard      Conventional  \n",
       "2             NaN               NaN               NaN  \n",
       "3             NaN               NaN               NaN  \n",
       "4             NaN               NaN               NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a90f48",
   "metadata": {},
   "source": [
    "## Sorting DataFrame by the SalesDate Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bcf5569a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting DataFrame by Salesdate\n",
    "\n",
    "df.sort_values(by=[\"saledate\"], inplace = True, ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b676dbe",
   "metadata": {},
   "source": [
    "## Make a copy of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de912138",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy\n",
    "\n",
    "dfCopy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7298bd71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding extra columns based on saledate\n",
    "\n",
    "dfCopy['saleYear'] = dfCopy.saledate.dt.year\n",
    "dfCopy['saleMonth'] = dfCopy.saledate.dt.month\n",
    "dfCopy['saleDay'] = dfCopy.saledate.dt.day\n",
    "dfCopy['saleDayOfWeek'] = dfCopy.saledate.dt.day_of_week\n",
    "dfCopy['saleDayOfYear'] = dfCopy.saledate.dt.day_of_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a23eb73",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfCopy.drop('saledate',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0d5e748f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida           67320\n",
       "Texas             53110\n",
       "California        29761\n",
       "Washington        16222\n",
       "Georgia           14633\n",
       "Maryland          13322\n",
       "Mississippi       13240\n",
       "Ohio              12369\n",
       "Illinois          11540\n",
       "Colorado          11529\n",
       "New Jersey        11156\n",
       "North Carolina    10636\n",
       "Tennessee         10298\n",
       "Alabama           10292\n",
       "Pennsylvania      10234\n",
       "South Carolina     9951\n",
       "Arizona            9364\n",
       "New York           8639\n",
       "Connecticut        8276\n",
       "Minnesota          7885\n",
       "Missouri           7178\n",
       "Nevada             6932\n",
       "Louisiana          6627\n",
       "Kentucky           5351\n",
       "Maine              5096\n",
       "Indiana            4124\n",
       "Arkansas           3933\n",
       "New Mexico         3631\n",
       "Utah               3046\n",
       "Unspecified        2801\n",
       "Wisconsin          2745\n",
       "New Hampshire      2738\n",
       "Virginia           2353\n",
       "Idaho              2025\n",
       "Oregon             1911\n",
       "Michigan           1831\n",
       "Wyoming            1672\n",
       "Montana            1336\n",
       "Iowa               1336\n",
       "Oklahoma           1326\n",
       "Nebraska            866\n",
       "West Virginia       840\n",
       "Kansas              667\n",
       "Delaware            510\n",
       "North Dakota        480\n",
       "Alaska              430\n",
       "Massachusetts       347\n",
       "Vermont             300\n",
       "South Dakota        244\n",
       "Hawaii              118\n",
       "Rhode Island         83\n",
       "Puerto Rico          42\n",
       "Washington DC         2\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the value of different columns\n",
    "\n",
    "dfCopy.state.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5c29fd",
   "metadata": {},
   "source": [
    "## Converting string columns to categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7b5bd66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.api.types.is_string_dtype(dfCopy[\"UsageBand\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03a87164",
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, columns in dfCopy.items():\n",
    "    if pd.api.types.is_string_dtype(columns):\n",
    "        dfCopy[label] = columns.astype(\"category\").cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42bf4f77",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCopy.state.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "275687e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    43\n",
       "274835     8\n",
       "141296     8\n",
       "212552     8\n",
       "62755      8\n",
       "          ..\n",
       "410879     4\n",
       "412476     4\n",
       "411927     4\n",
       "407124     4\n",
       "409203     4\n",
       "Length: 412698, dtype: int8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCopy.state.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c790ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save and export dfCopy\n",
    "\n",
    "dfCopy.to_csv(\"train_tmp.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e91813f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import preprocessed data\n",
    "\n",
    "dfCopy = pd.read_csv(\"train_tmp.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e786900b",
   "metadata": {},
   "source": [
    "## Fill missing Values\n",
    "\n",
    "* Numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27343033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalesID\n",
      "SalePrice\n",
      "MachineID\n",
      "ModelID\n",
      "datasource\n",
      "auctioneerID\n",
      "YearMade\n",
      "MachineHoursCurrentMeter\n",
      "saleYear\n",
      "saleMonth\n",
      "saleDay\n",
      "saleDayOfWeek\n",
      "saleDayOfYear\n"
     ]
    }
   ],
   "source": [
    "for label, columns in dfCopy.items():\n",
    "    if pd.api.types.is_numeric_dtype(columns):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73e4b416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auctioneerID\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "# Check which numeric columns has missing values\n",
    "\n",
    "for label,columns in dfCopy.items():\n",
    "    if pd.api.types.is_numeric_dtype(columns):\n",
    "        if pd.isnull(columns).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e0eecfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill numeric rows with median\n",
    "\n",
    "for label,columns in dfCopy.items():\n",
    "    if pd.api.types.is_numeric_dtype(columns):\n",
    "        if pd.isnull(columns).sum():\n",
    "            dfCopy[label + \"_is_missing\"] = pd.isnull(columns)\n",
    "            dfCopy[label] = columns.fillna(columns.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5eebfe81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    392562\n",
       "True      20136\n",
       "Name: auctioneerID_is_missing, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCopy.auctioneerID_is_missing.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6182a8",
   "metadata": {},
   "source": [
    "* Categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "017a02e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand\n",
      "fiModelDesc\n",
      "fiBaseModel\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "fiProductClassDesc\n",
      "state\n",
      "ProductGroup\n",
      "ProductGroupDesc\n",
      "Drive_System\n",
      "Enclosure\n",
      "Forks\n",
      "Pad_Type\n",
      "Ride_Control\n",
      "Stick\n",
      "Transmission\n",
      "Turbocharged\n",
      "Blade_Extension\n",
      "Blade_Width\n",
      "Enclosure_Type\n",
      "Engine_Horsepower\n",
      "Hydraulics\n",
      "Pushblock\n",
      "Ripper\n",
      "Scarifier\n",
      "Tip_Control\n",
      "Tire_Size\n",
      "Coupler\n",
      "Coupler_System\n",
      "Grouser_Tracks\n",
      "Hydraulics_Flow\n",
      "Track_Type\n",
      "Undercarriage_Pad_Width\n",
      "Stick_Length\n",
      "Thumb\n",
      "Pattern_Changer\n",
      "Grouser_Type\n",
      "Backhoe_Mounting\n",
      "Blade_Type\n",
      "Travel_Controls\n",
      "Differential_Type\n",
      "Steering_Controls\n"
     ]
    }
   ],
   "source": [
    "for label,columns in dfCopy.items():\n",
    "    if not pd.api.types.is_numeric_dtype(columns):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a18c4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for label,columns in dfCopy.items():\n",
    "    if not pd.api.types.is_numeric_dtype(columns):\n",
    "        dfCopy[label+\"_is_missing\"] = pd.isnull(columns)\n",
    "        dfCopy[label] = pd.Categorical(columns).codes+1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd3bc20",
   "metadata": {},
   "source": [
    "## Modelling \n",
    "\n",
    "Building the Machine Learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bbd9ac1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b3999e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "\n",
    "#Fitting model\n",
    "model.fit(dfCopy.drop('SalePrice', axis=1), dfCopy['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5205dfb0",
   "metadata": {},
   "source": [
    "### Splitting data into training and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a3aea7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfVal = dfCopy[dfCopy.saleYear == 2012]\n",
    "dfTrain = dfCopy[dfCopy.saleYear != 2012]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df82220e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 102), (401125,), (11573, 102), (11573,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xTrain, yTrain = dfTrain.drop('SalePrice', axis=1), dfTrain.SalePrice\n",
    "xValid, yValid = dfVal.drop('SalePrice', axis=1), dfVal.SalePrice\n",
    "\n",
    "xTrain.shape, yTrain.shape, xValid.shape, yValid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3ab62b",
   "metadata": {},
   "source": [
    "## Building evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "26f50d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, r2_score\n",
    "\n",
    "def rmsle(ytest, ypred):\n",
    "    '''\n",
    "        Calculate root mean squared log error between predictions and true labels\n",
    "    '''\n",
    "    \n",
    "    return np.sqrt(mean_squared_log_error(ytest,ypred))\n",
    "\n",
    "# Create function to evaluate model \n",
    "def evaluateModel(model):\n",
    "    trainPreds = model.predict(xTrain)\n",
    "    valPreds = model.predict(xValid)\n",
    "    scores = {\n",
    "        \"Training MAE\": mean_absolute_error(yTrain, trainPreds),\n",
    "        \"Valid MAE\": mean_absolute_error(yValid, valPreds),\n",
    "        \"Training RMSLE\": rmsle(yTrain, trainPreds),\n",
    "        \"Valid RMSLE\": rmsle(yValid, valPreds),\n",
    "        \"Training R^2\": r2_score(yTrain,trainPreds),\n",
    "        \"Valid R^2\": r2_score(yValid, valPreds)\n",
    "    }\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2dc151",
   "metadata": {},
   "source": [
    "## Testing our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bd84c2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_samples=100000, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=100000, n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_samples=100000, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_jobs=-1, random_state=42, max_samples=100000)\n",
    "model.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "40624cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 3588.5677039077596,\n",
       " 'Valid MAE': 6193.733662835912,\n",
       " 'Training RMSLE': 0.17533085989490504,\n",
       " 'Valid RMSLE': 0.25674596551158657,\n",
       " 'Training R^2': 0.9389791835237717,\n",
       " 'Valid R^2': 0.8710523572862298}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluateModel(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f9b367",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3ecdfdd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\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,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\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,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\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(n_jobs=-1, random_state=42)</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(n_jobs=-1, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "#Different RandomForestRegressor Hyperparameters\n",
    "\n",
    "rfGrid = {\n",
    "    \"n_estimators\": np.arange(10,100,10),\n",
    "    \"max_depth\": [None, 3, 5, 10],\n",
    "    \"min_samples_split\": np.arange(2,20,2),\n",
    "    \"min_samples_leaf\": np.arange(1,20,2),\n",
    "    \"max_features\": [0.5,1,\"sqrt\",\"auto\"],\n",
    "    \"max_samples\": [10000]\n",
    "}\n",
    "\n",
    "#Instantiating RandomizedSearchCV model\n",
    "\n",
    "rsModel = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1,random_state=42), param_distributions=rfGrid, n_iter=2, cv=5, verbose=True)\n",
    "\n",
    "rsModel.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4bfc4f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 30,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 13,\n",
       " 'max_samples': 10000,\n",
       " 'max_features': 0.5,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsModel.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "00642d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 11859.630534789067,\n",
       " 'Valid MAE': 13578.956584640538,\n",
       " 'Training RMSLE': 0.5086723438148965,\n",
       " 'Valid RMSLE': 0.5139954020572264,\n",
       " 'Training R^2': 0.48208492069694453,\n",
       " 'Valid R^2': 0.4819502052754251}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluateModel(rsModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e6f229",
   "metadata": {},
   "source": [
    "## Making predictions on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d303d346",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the test data\n",
    "\n",
    "dfTest = pd.read_csv(\"./Test.csv\",low_memory=False, parse_dates=[\"saledate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ca18b851",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>saledate</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
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       "      <th>Backhoe_Mounting</th>\n",
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       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
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       "      <td>Low</td>\n",
       "      <td>2012-05-03</td>\n",
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       "      <td>936</td>\n",
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       "      <td>Conventional</td>\n",
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       "      <td>22805</td>\n",
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       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>EC210BLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>9' 6\"</td>\n",
       "      <td>Manual</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>330CL</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Triple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>650K</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
       "0                    3688.0       Low 2012-05-03        580G  ...   \n",
       "1                   28555.0      High 2012-05-10         936  ...   \n",
       "2                    6038.0    Medium 2012-05-10    EC210BLC  ...   \n",
       "3                    8940.0      High 2012-05-10       330CL  ...   \n",
       "4                    2286.0       Low 2012-05-10        650K  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width         Stick_Length   Thumb      Pattern_Changer  \\\n",
       "0                     NaN                  NaN     NaN                  NaN   \n",
       "1                     NaN                  NaN     NaN                  NaN   \n",
       "2     None or Unspecified                9' 6\"  Manual  None or Unspecified   \n",
       "3     None or Unspecified  None or Unspecified  Manual                  Yes   \n",
       "4                     NaN                  NaN     NaN                  NaN   \n",
       "\n",
       "  Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "0          NaN                  NaN        NaN                  NaN   \n",
       "1          NaN                  NaN        NaN                  NaN   \n",
       "2       Double                  NaN        NaN                  NaN   \n",
       "3       Triple                  NaN        NaN                  NaN   \n",
       "4          NaN  None or Unspecified        PAT  None or Unspecified   \n",
       "\n",
       "  Differential_Type Steering_Controls  \n",
       "0               NaN               NaN  \n",
       "1          Standard      Conventional  \n",
       "2               NaN               NaN  \n",
       "3               NaN               NaN  \n",
       "4               NaN               NaN  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ba8f0ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                         0\n",
       "MachineID                       0\n",
       "ModelID                         0\n",
       "datasource                      0\n",
       "auctioneerID                    0\n",
       "YearMade                        0\n",
       "MachineHoursCurrentMeter    10328\n",
       "UsageBand                   10623\n",
       "saledate                        0\n",
       "fiModelDesc                     0\n",
       "fiBaseModel                     0\n",
       "fiSecondaryDesc              3975\n",
       "fiModelSeries               10451\n",
       "fiModelDescriptor            9433\n",
       "ProductSize                  6409\n",
       "fiProductClassDesc              0\n",
       "state                           0\n",
       "ProductGroup                    0\n",
       "ProductGroupDesc                0\n",
       "Drive_System                 9698\n",
       "Enclosure                       2\n",
       "Forks                        6149\n",
       "Pad_Type                    10349\n",
       "Ride_Control                 8216\n",
       "Stick                       10349\n",
       "Transmission                 7639\n",
       "Turbocharged                10349\n",
       "Blade_Extension             11806\n",
       "Blade_Width                 11806\n",
       "Enclosure_Type              11806\n",
       "Engine_Horsepower           11806\n",
       "Hydraulics                   2142\n",
       "Pushblock                   11806\n",
       "Ripper                       9753\n",
       "Scarifier                   11806\n",
       "Tip_Control                 11806\n",
       "Tire_Size                    9679\n",
       "Coupler                      4856\n",
       "Coupler_System              10391\n",
       "Grouser_Tracks              10391\n",
       "Hydraulics_Flow             10391\n",
       "Track_Type                   9063\n",
       "Undercarriage_Pad_Width      9059\n",
       "Stick_Length                 9063\n",
       "Thumb                        9062\n",
       "Pattern_Changer              9063\n",
       "Grouser_Type                 9063\n",
       "Backhoe_Mounting            10406\n",
       "Blade_Type                  10399\n",
       "Travel_Controls             10399\n",
       "Differential_Type           10328\n",
       "Steering_Controls           10328\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTest.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59bb26e",
   "metadata": {},
   "source": [
    "## Preprocessing the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cae189b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessData(df):\n",
    "    '''\n",
    "        Perform transformations on given DataFrame and returns the new DataFrame\n",
    "    '''\n",
    "    df['saleYear'] = df.saledate.dt.year\n",
    "    df['saleMonth'] = df.saledate.dt.month\n",
    "    df['saleDay'] = df.saledate.dt.day\n",
    "    df['saleDayOfWeek'] = df.saledate.dt.day_of_week\n",
    "    df['saleDayOfYear'] = df.saledate.dt.day_of_year\n",
    "    \n",
    "    df.drop(['saledate'], axis=1, inplace=True)\n",
    "    \n",
    "    #Fill numeric columns with median\n",
    "    for label,columns in df.items():\n",
    "        if pd.api.types.is_numeric_dtype(columns):\n",
    "            if pd.isnull(columns).sum():\n",
    "                df[label + \"_is_missing\"] = pd.isnull(columns)\n",
    "                df[label] = columns.fillna(columns.median())\n",
    "\n",
    "    \n",
    "        # fill categorical missing data and turn categories into numbers\n",
    "        if not pd.api.types.is_numeric_dtype(columns):\n",
    "            df[label+\"_is_missing\"] = pd.isnull(columns)\n",
    "            df[label] = pd.Categorical(columns).codes + 1\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7ac0a793",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
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       "      <th>fiBaseModel</th>\n",
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       "      <th>Thumb_is_missing</th>\n",
       "      <th>Pattern_Changer_is_missing</th>\n",
       "      <th>Grouser_Type_is_missing</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
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       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
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       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1177</td>\n",
       "      <td>404</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
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       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
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       "      <td>287</td>\n",
       "      <td>113</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                               False                    False   \n",
       "3                               False                    False   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2             False                       False                    False   \n",
       "3             False                       False                    False   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                         True                   True   \n",
       "1                         True                   True   \n",
       "2                         True                   True   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                        True                          True   \n",
       "1                        True                         False   \n",
       "2                        True                          True   \n",
       "3                        True                          True   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                          True  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTest = preprocessData(dfTest)\n",
    "dfTest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "88934adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12452</th>\n",
       "      <td>6643171</td>\n",
       "      <td>2558317</td>\n",
       "      <td>21450</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>0</td>\n",
       "      <td>713</td>\n",
       "      <td>235</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>6643173</td>\n",
       "      <td>2558332</td>\n",
       "      <td>21434</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>2005</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>6643184</td>\n",
       "      <td>2558342</td>\n",
       "      <td>21437</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>0</td>\n",
       "      <td>325</td>\n",
       "      <td>123</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>6643186</td>\n",
       "      <td>2558343</td>\n",
       "      <td>21437</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>0</td>\n",
       "      <td>325</td>\n",
       "      <td>123</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>6643196</td>\n",
       "      <td>2558346</td>\n",
       "      <td>21446</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>0</td>\n",
       "      <td>483</td>\n",
       "      <td>171</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12457 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0      1227829    1006309     3168         121             3      1999   \n",
       "1      1227844    1022817     7271         121             3      1000   \n",
       "2      1227847    1031560    22805         121             3      2004   \n",
       "3      1227848      56204     1269         121             3      2006   \n",
       "4      1227863    1053887    22312         121             3      2005   \n",
       "...        ...        ...      ...         ...           ...       ...   \n",
       "12452  6643171    2558317    21450         149             2      2008   \n",
       "12453  6643173    2558332    21434         149             2      2005   \n",
       "12454  6643184    2558342    21437         149             2      1000   \n",
       "12455  6643186    2558343    21437         149             2      2006   \n",
       "12456  6643196    2558346    21446         149             2      2008   \n",
       "\n",
       "       MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                        3688.0          2          499          180  ...   \n",
       "1                       28555.0          1          831          292  ...   \n",
       "2                        6038.0          3         1177          404  ...   \n",
       "3                        8940.0          1          287          113  ...   \n",
       "4                        2286.0          2          566          196  ...   \n",
       "...                         ...        ...          ...          ...  ...   \n",
       "12452                    3525.0          0          713          235  ...   \n",
       "12453                    3525.0          0          186           80  ...   \n",
       "12454                    3525.0          0          325          123  ...   \n",
       "12455                    3525.0          0          325          123  ...   \n",
       "12456                    3525.0          0          483          171  ...   \n",
       "\n",
       "       Stick_Length_is_missing  Thumb_is_missing  Pattern_Changer_is_missing  \\\n",
       "0                         True              True                        True   \n",
       "1                         True              True                        True   \n",
       "2                        False             False                       False   \n",
       "3                        False             False                       False   \n",
       "4                         True              True                        True   \n",
       "...                        ...               ...                         ...   \n",
       "12452                    False             False                       False   \n",
       "12453                    False             False                       False   \n",
       "12454                    False             False                       False   \n",
       "12455                    False             False                       False   \n",
       "12456                    False             False                       False   \n",
       "\n",
       "       Grouser_Type_is_missing  Backhoe_Mounting_is_missing  \\\n",
       "0                         True                         True   \n",
       "1                         True                         True   \n",
       "2                        False                         True   \n",
       "3                        False                         True   \n",
       "4                         True                        False   \n",
       "...                        ...                          ...   \n",
       "12452                    False                         True   \n",
       "12453                    False                         True   \n",
       "12454                    False                         True   \n",
       "12455                    False                         True   \n",
       "12456                    False                         True   \n",
       "\n",
       "       Blade_Type_is_missing  Travel_Controls_is_missing  \\\n",
       "0                       True                        True   \n",
       "1                       True                        True   \n",
       "2                       True                        True   \n",
       "3                       True                        True   \n",
       "4                      False                       False   \n",
       "...                      ...                         ...   \n",
       "12452                   True                        True   \n",
       "12453                   True                        True   \n",
       "12454                   True                        True   \n",
       "12455                   True                        True   \n",
       "12456                   True                        True   \n",
       "\n",
       "       Differential_Type_is_missing  Steering_Controls_is_missing  \\\n",
       "0                              True                          True   \n",
       "1                             False                         False   \n",
       "2                              True                          True   \n",
       "3                              True                          True   \n",
       "4                              True                          True   \n",
       "...                             ...                           ...   \n",
       "12452                          True                          True   \n",
       "12453                          True                          True   \n",
       "12454                          True                          True   \n",
       "12455                          True                          True   \n",
       "12456                          True                          True   \n",
       "\n",
       "       auctioneerID_is_missing  \n",
       "0                        False  \n",
       "1                        False  \n",
       "2                        False  \n",
       "3                        False  \n",
       "4                        False  \n",
       "...                        ...  \n",
       "12452                    False  \n",
       "12453                    False  \n",
       "12454                    False  \n",
       "12455                    False  \n",
       "12456                    False  \n",
       "\n",
       "[12457 rows x 102 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding missing column\n",
    "\n",
    "dfTest['auctioneerID_is_missing'] = False\n",
    "dfTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f629b9b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on test dataset\n",
    "\n",
    "testPreds = model.predict(dfTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4310ede0",
   "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>SalePrices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>19952.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>19603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>45635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>83175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>49933.0</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>50435.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>6643173</td>\n",
       "      <td>11345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>6643184</td>\n",
       "      <td>12307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>6643186</td>\n",
       "      <td>16810.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>6643196</td>\n",
       "      <td>29372.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID  SalePrices\n",
       "0      1227829     19952.5\n",
       "1      1227844     19603.0\n",
       "2      1227847     45635.0\n",
       "3      1227848     83175.0\n",
       "4      1227863     49933.0\n",
       "...        ...         ...\n",
       "12452  6643171     50435.0\n",
       "12453  6643173     11345.0\n",
       "12454  6643184     12307.0\n",
       "12455  6643186     16810.5\n",
       "12456  6643196     29372.5\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfPreds = pd.DataFrame()\n",
    "\n",
    "dfPreds['SalesID'] = dfTest[\"SalesID\"]\n",
    "dfPreds[\"SalePrices\"] = testPreds\n",
    "\n",
    "dfPreds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ace530fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export prediction data\n",
    "\n",
    "dfPreds.to_csv(\"./testpredictions.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459c7f57",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "943dcc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.98732065e-02, 1.78890269e-02, 3.13189236e-02, 5.79534574e-04,\n",
       "       4.82963514e-03, 2.61245297e-01, 3.40338127e-03, 1.16118041e-03,\n",
       "       1.94100450e-02, 6.37782457e-02, 5.53807653e-02, 4.64542825e-03,\n",
       "       3.63845450e-03, 2.26648662e-01, 3.86240265e-02, 9.26549672e-03,\n",
       "       1.88624493e-04, 2.15893272e-04, 1.55659752e-03, 9.35066135e-03,\n",
       "       4.69672864e-04, 4.42167849e-05, 7.14869262e-04, 1.80833090e-04,\n",
       "       1.31578196e-03, 2.68746627e-05, 9.99356049e-04, 1.50081831e-02,\n",
       "       1.91855000e-03, 2.32881046e-04, 3.26728942e-03, 4.03993585e-03,\n",
       "       2.45306323e-03, 1.72093540e-03, 1.39643131e-03, 2.04926151e-03,\n",
       "       1.14937982e-03, 1.45067584e-02, 1.07468207e-05, 3.63571372e-06,\n",
       "       2.00297234e-04, 1.22297517e-03, 1.39198557e-03, 8.11232730e-04,\n",
       "       3.46572893e-04, 5.88478119e-04, 2.56622635e-04, 1.89500516e-03,\n",
       "       9.64332952e-04, 1.97657250e-04, 2.05193461e-04, 7.34877872e-02,\n",
       "       3.80125143e-03, 9.96123868e-03, 5.00776191e-03, 1.40899895e-02,\n",
       "       6.30110061e-04, 3.36396791e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       7.02086785e-04, 8.15811849e-04, 8.20619198e-04, 2.98921310e-03,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.63274113e-05, 5.87164882e-06, 8.00354832e-05, 2.00009232e-06,\n",
       "       7.09814517e-05, 4.75158107e-06, 1.41287437e-04, 2.89115521e-06,\n",
       "       1.26699279e-03, 7.64101011e-04, 1.75695075e-04, 3.27663261e-04,\n",
       "       2.24345270e-03, 1.94199378e-04, 2.24438940e-04, 1.57481668e-03,\n",
       "       9.43513712e-04, 3.23583561e-04, 1.45925090e-04, 1.49942654e-02,\n",
       "       3.54242678e-06, 7.06091718e-07, 4.38336347e-05, 5.10249030e-05,\n",
       "       4.78486308e-05, 4.41976037e-05, 4.90646641e-05, 5.66282554e-05,\n",
       "       2.05646292e-04, 1.83517897e-04, 1.03137103e-04, 6.05949891e-05,\n",
       "       5.87445164e-05, 3.54356463e-04])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find feature importance of our best model\n",
    "\n",
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8cc484de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting Feature importance\n",
    "\n",
    "def plotFeatures(columns, importance, n=20):\n",
    "    df = (pd.DataFrame({\"features\":columns, \"feature_importances\":importance}).sort_values(\"feature_importances\", ascending=False).reset_index(drop=True))\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(df[\"features\"][:n], df[\"feature_importances\"][:n])\n",
    "    ax.set_ylabel(\"Features\")\n",
    "    ax.set_xlabel(\"Feature Importance\")\n",
    "    ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f2b24127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotFeatures(xTrain.columns, 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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}