0_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a6eb43f",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18afc46a",
   "metadata": {},
   "source": [
    "## Research\n",
    "\n",
    "Prediction whether a car is a good buy or a bad buy (\"lemon\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3995f8",
   "metadata": {},
   "source": [
    "| Field Name\t| Definition |\n",
    "| --- | --- |\n",
    "| RefID\t| Unique (sequential) number assigned to vehicles |\n",
    "| IsBadBuy |\tIdentifies if the kicked vehicle was an avoidable purchase | \n",
    "| PurchDate | The Date the vehicle was Purchased at Auction |\n",
    "| Auction\t| Auction provider at which the  vehicle was purchased |\n",
    "| VehYear\t| The manufacturer's year of the vehicle |\n",
    "| VehicleAge | The Years elapsed since the manufacturer's year |\n",
    "| Make | Vehicle Manufacturer | \n",
    "| Model | Vehicle Model |\n",
    "| Trim | Vehicle Trim Level |\n",
    "| SubModel | Vehicle Submodel |\n",
    "| Color | Vehicle Color |\n",
    "| Transmission | Vehicles transmission type (Automatic, Manual) |\n",
    "| WheelTypeID\t| The type id of the vehicle wheel |\n",
    "| WheelType | The vehicle wheel type description (Alloy, Covers) |\n",
    "| VehOdo | The vehicles odometer reading |\n",
    "| Nationality\t| The Manufacturer's country |\n",
    "| Size | The size category of the vehicle (Compact, SUV, etc.) |\n",
    "| TopThreeAmericanName | Identifies if the manufacturer is one of the top three American manufacturers |\n",
    "| MMRAcquisitionAuctionAveragePrice | Acquisition price for this vehicle in average condition at time of purchase |\t\n",
    "| MMRAcquisitionAuctionCleanPrice\t| Acquisition price for this vehicle in the above Average condition at time of purchase |\n",
    "| MMRAcquisitionRetailAveragePrice | Acquisition price for this vehicle in the retail market in average condition at time of purchase |\n",
    "| MMRAcquisitonRetailCleanPrice | Acquisition price for this vehicle in the retail market in above average condition at time of purchase |\n",
    "| MMRCurrentAuctionAveragePrice |\tAcquisition price for this vehicle in average condition as of current day |\t\n",
    "| MMRCurrentAuctionCleanPrice | Acquisition price for this vehicle in the above condition as of current day |\n",
    "| MMRCurrentRetailAveragePrice | Acquisition price for this vehicle in the retail market in average condition as of current day |\n",
    "| MMRCurrentRetailCleanPrice | Acquisition price for this vehicle in the retail market in above average condition as of current day |\n",
    "| PRIMEUNIT | Identifies if the vehicle would have a higher demand than a standard purchase |\n",
    "| AcquisitionType | Identifies how the vehicle was aquired (Auction buy, trade in, etc) |\n",
    "| AUCGUART | The level guarntee provided by auction for the vehicle (Green light - Guaranteed/arbitratable, Yellow Light - caution/issue, red light - sold as is) |\n",
    "| KickDate | Date the vehicle was kicked back to the auction |\n",
    "| BYRNO | Unique number assigned to the buyer that purchased the vehicle |\n",
    "| VNZIP | Zipcode where the car was purchased |\n",
    "| VNST | State where the the car was purchased |\n",
    "| VehBCost | Acquisition cost paid for the vehicle at time of purchase |\n",
    "| IsOnlineSale | Identifies if the vehicle was originally purchased online |\n",
    "| WarrantyCost | Warranty price (term=36month  and millage=36K) |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b21d16",
   "metadata": {},
   "source": [
    "In this scenario, both recall (what proportion of \"lemons\" was actually correct?) as well as precision (what proportion of actual \"lemons\" was identified correctly?) will be considered equally important for classification\n",
    "\n",
    "$\\rightarrow$ f1 score (harmonic mean of recall and precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5c09a1",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e656a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import relevant modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1929a0a0",
   "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>RefId</th>\n",
       "      <th>IsBadBuy</th>\n",
       "      <th>PurchDate</th>\n",
       "      <th>Auction</th>\n",
       "      <th>VehYear</th>\n",
       "      <th>VehicleAge</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Trim</th>\n",
       "      <th>SubModel</th>\n",
       "      <th>...</th>\n",
       "      <th>MMRCurrentRetailAveragePrice</th>\n",
       "      <th>MMRCurrentRetailCleanPrice</th>\n",
       "      <th>PRIMEUNIT</th>\n",
       "      <th>AUCGUART</th>\n",
       "      <th>BYRNO</th>\n",
       "      <th>VNZIP1</th>\n",
       "      <th>VNST</th>\n",
       "      <th>VehBCost</th>\n",
       "      <th>IsOnlineSale</th>\n",
       "      <th>WarrantyCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12/7/2009</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>MAZDA</td>\n",
       "      <td>MAZDA3</td>\n",
       "      <td>i</td>\n",
       "      <td>4D SEDAN I</td>\n",
       "      <td>...</td>\n",
       "      <td>11597.0</td>\n",
       "      <td>12409.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21973</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12/7/2009</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>2004</td>\n",
       "      <td>5</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>1500 RAM PICKUP 2WD</td>\n",
       "      <td>ST</td>\n",
       "      <td>QUAD CAB 4.7L SLT</td>\n",
       "      <td>...</td>\n",
       "      <td>11374.0</td>\n",
       "      <td>12791.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19638</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12/7/2009</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>STRATUS V6</td>\n",
       "      <td>SXT</td>\n",
       "      <td>4D SEDAN SXT FFV</td>\n",
       "      <td>...</td>\n",
       "      <td>7146.0</td>\n",
       "      <td>8702.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19638</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12/7/2009</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>2004</td>\n",
       "      <td>5</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>NEON</td>\n",
       "      <td>SXT</td>\n",
       "      <td>4D SEDAN</td>\n",
       "      <td>...</td>\n",
       "      <td>4375.0</td>\n",
       "      <td>5518.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19638</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>12/7/2009</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "      <td>FORD</td>\n",
       "      <td>FOCUS</td>\n",
       "      <td>ZX3</td>\n",
       "      <td>2D COUPE ZX3</td>\n",
       "      <td>...</td>\n",
       "      <td>6739.0</td>\n",
       "      <td>7911.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19638</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RefId  IsBadBuy  PurchDate Auction  VehYear  VehicleAge   Make  \\\n",
       "0      1         0  12/7/2009   ADESA     2006           3  MAZDA   \n",
       "1      2         0  12/7/2009   ADESA     2004           5  DODGE   \n",
       "2      3         0  12/7/2009   ADESA     2005           4  DODGE   \n",
       "3      4         0  12/7/2009   ADESA     2004           5  DODGE   \n",
       "4      5         0  12/7/2009   ADESA     2005           4   FORD   \n",
       "\n",
       "                 Model Trim           SubModel  ...  \\\n",
       "0               MAZDA3    i         4D SEDAN I  ...   \n",
       "1  1500 RAM PICKUP 2WD   ST  QUAD CAB 4.7L SLT  ...   \n",
       "2           STRATUS V6  SXT   4D SEDAN SXT FFV  ...   \n",
       "3                 NEON  SXT           4D SEDAN  ...   \n",
       "4                FOCUS  ZX3       2D COUPE ZX3  ...   \n",
       "\n",
       "  MMRCurrentRetailAveragePrice MMRCurrentRetailCleanPrice  PRIMEUNIT AUCGUART  \\\n",
       "0                      11597.0                    12409.0        NaN      NaN   \n",
       "1                      11374.0                    12791.0        NaN      NaN   \n",
       "2                       7146.0                     8702.0        NaN      NaN   \n",
       "3                       4375.0                     5518.0        NaN      NaN   \n",
       "4                       6739.0                     7911.0        NaN      NaN   \n",
       "\n",
       "   BYRNO VNZIP1 VNST VehBCost  IsOnlineSale  WarrantyCost  \n",
       "0  21973  33619   FL   7100.0             0          1113  \n",
       "1  19638  33619   FL   7600.0             0          1053  \n",
       "2  19638  33619   FL   4900.0             0          1389  \n",
       "3  19638  33619   FL   4100.0             0           630  \n",
       "4  19638  33619   FL   4000.0             0          1020  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read train data\n",
    "df = pd.read_csv(\"training.csv\")\n",
    "display(df.head())\n",
    "\n",
    "df_copy = df.copy()  # for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6607a2b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RefId', 'IsBadBuy', 'PurchDate', 'Auction', 'VehYear', 'VehicleAge',\n",
       "       'Make', 'Model', 'Trim', 'SubModel', 'Color', 'Transmission',\n",
       "       'WheelTypeID', 'WheelType', 'VehOdo', 'Nationality', 'Size',\n",
       "       'TopThreeAmericanName', 'MMRAcquisitionAuctionAveragePrice',\n",
       "       'MMRAcquisitionAuctionCleanPrice', 'MMRAcquisitionRetailAveragePrice',\n",
       "       'MMRAcquisitonRetailCleanPrice', 'MMRCurrentAuctionAveragePrice',\n",
       "       'MMRCurrentAuctionCleanPrice', 'MMRCurrentRetailAveragePrice',\n",
       "       'MMRCurrentRetailCleanPrice', 'PRIMEUNIT', 'AUCGUART', 'BYRNO',\n",
       "       'VNZIP1', 'VNST', 'VehBCost', 'IsOnlineSale', 'WarrantyCost'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2728a3be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 72983 entries, 0 to 72982\n",
      "Data columns (total 34 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   RefId                              72983 non-null  int64  \n",
      " 1   IsBadBuy                           72983 non-null  int64  \n",
      " 2   PurchDate                          72983 non-null  object \n",
      " 3   Auction                            72983 non-null  object \n",
      " 4   VehYear                            72983 non-null  int64  \n",
      " 5   VehicleAge                         72983 non-null  int64  \n",
      " 6   Make                               72983 non-null  object \n",
      " 7   Model                              72983 non-null  object \n",
      " 8   Trim                               70623 non-null  object \n",
      " 9   SubModel                           72975 non-null  object \n",
      " 10  Color                              72975 non-null  object \n",
      " 11  Transmission                       72974 non-null  object \n",
      " 12  WheelTypeID                        69814 non-null  float64\n",
      " 13  WheelType                          69809 non-null  object \n",
      " 14  VehOdo                             72983 non-null  int64  \n",
      " 15  Nationality                        72978 non-null  object \n",
      " 16  Size                               72978 non-null  object \n",
      " 17  TopThreeAmericanName               72978 non-null  object \n",
      " 18  MMRAcquisitionAuctionAveragePrice  72965 non-null  float64\n",
      " 19  MMRAcquisitionAuctionCleanPrice    72965 non-null  float64\n",
      " 20  MMRAcquisitionRetailAveragePrice   72965 non-null  float64\n",
      " 21  MMRAcquisitonRetailCleanPrice      72965 non-null  float64\n",
      " 22  MMRCurrentAuctionAveragePrice      72668 non-null  float64\n",
      " 23  MMRCurrentAuctionCleanPrice        72668 non-null  float64\n",
      " 24  MMRCurrentRetailAveragePrice       72668 non-null  float64\n",
      " 25  MMRCurrentRetailCleanPrice         72668 non-null  float64\n",
      " 26  PRIMEUNIT                          3419 non-null   object \n",
      " 27  AUCGUART                           3419 non-null   object \n",
      " 28  BYRNO                              72983 non-null  int64  \n",
      " 29  VNZIP1                             72983 non-null  int64  \n",
      " 30  VNST                               72983 non-null  object \n",
      " 31  VehBCost                           72983 non-null  float64\n",
      " 32  IsOnlineSale                       72983 non-null  int64  \n",
      " 33  WarrantyCost                       72983 non-null  int64  \n",
      "dtypes: float64(10), int64(9), object(15)\n",
      "memory usage: 18.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#check data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfff6add",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop RefId\n",
    "df = df.drop(\"RefId\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db737ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\3000224512.py:2: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, \"PurchDate\"] = pd.to_datetime(df.loc[:, \"PurchDate\"])\n"
     ]
    }
   ],
   "source": [
    "# convert \"PurchDate\" to time series\n",
    "df.loc[:, \"PurchDate\"] = pd.to_datetime(df.loc[:, \"PurchDate\"])\n",
    "\n",
    "# replace PurchDate Year\n",
    "df.loc[:, \"PurchYear\"] = df.loc[:, \"PurchDate\"].dt.year\n",
    "df = df.drop(\"PurchDate\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea520c60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2456265645.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n"
     ]
    }
   ],
   "source": [
    "#convert object dtypes to categorial to save memory\n",
    "for col in df.select_dtypes('object'):\n",
    "    df.loc[:, col] = df.loc[:, col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a14b897",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert 'IsOnlineSale' to category\n",
    "df.loc[:, 'IsOnlineSale'] = df.loc[:, 'IsOnlineSale'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3e1422b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert 'BYRNO' to category\n",
    "df.loc[:, 'BYRNO'] = df.loc[:, 'BYRNO'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53f7b94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsBadBuy                                int64\n",
       "Auction                              category\n",
       "VehYear                                 int64\n",
       "VehicleAge                              int64\n",
       "Make                                 category\n",
       "Model                                category\n",
       "Trim                                 category\n",
       "SubModel                             category\n",
       "Color                                category\n",
       "Transmission                         category\n",
       "WheelTypeID                           float64\n",
       "WheelType                            category\n",
       "VehOdo                                  int64\n",
       "Nationality                          category\n",
       "Size                                 category\n",
       "TopThreeAmericanName                 category\n",
       "MMRAcquisitionAuctionAveragePrice     float64\n",
       "MMRAcquisitionAuctionCleanPrice       float64\n",
       "MMRAcquisitionRetailAveragePrice      float64\n",
       "MMRAcquisitonRetailCleanPrice         float64\n",
       "MMRCurrentAuctionAveragePrice         float64\n",
       "MMRCurrentAuctionCleanPrice           float64\n",
       "MMRCurrentRetailAveragePrice          float64\n",
       "MMRCurrentRetailCleanPrice            float64\n",
       "PRIMEUNIT                            category\n",
       "AUCGUART                             category\n",
       "BYRNO                                category\n",
       "VNZIP1                                  int64\n",
       "VNST                                 category\n",
       "VehBCost                              float64\n",
       "IsOnlineSale                         category\n",
       "WarrantyCost                            int64\n",
       "PurchYear                               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check dtypes again\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64e63a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Auction : 3\n",
      "Make : 33\n",
      "Model : 1063\n",
      "Trim : 134\n",
      "SubModel : 863\n",
      "Color : 16\n",
      "Transmission : 3\n",
      "WheelType : 3\n",
      "Nationality : 4\n",
      "Size : 12\n",
      "TopThreeAmericanName : 4\n",
      "PRIMEUNIT : 2\n",
      "AUCGUART : 2\n",
      "BYRNO : 74\n",
      "VNST : 37\n",
      "IsOnlineSale : 2\n"
     ]
    }
   ],
   "source": [
    "#checking unique values for each categorial column\n",
    "for col in df.select_dtypes('category').columns:\n",
    "    print('{} : {}'.format(col, df.loc[:, col].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db305011",
   "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>IsBadBuy</th>\n",
       "      <th>VehYear</th>\n",
       "      <th>VehicleAge</th>\n",
       "      <th>WheelTypeID</th>\n",
       "      <th>VehOdo</th>\n",
       "      <th>MMRAcquisitionAuctionAveragePrice</th>\n",
       "      <th>MMRAcquisitionAuctionCleanPrice</th>\n",
       "      <th>MMRAcquisitionRetailAveragePrice</th>\n",
       "      <th>MMRAcquisitonRetailCleanPrice</th>\n",
       "      <th>MMRCurrentAuctionAveragePrice</th>\n",
       "      <th>MMRCurrentAuctionCleanPrice</th>\n",
       "      <th>MMRCurrentRetailAveragePrice</th>\n",
       "      <th>MMRCurrentRetailCleanPrice</th>\n",
       "      <th>VNZIP1</th>\n",
       "      <th>VehBCost</th>\n",
       "      <th>WarrantyCost</th>\n",
       "      <th>PurchYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>72983.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "      <td>69814.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "      <td>72965.000000</td>\n",
       "      <td>72965.000000</td>\n",
       "      <td>72965.000000</td>\n",
       "      <td>72965.000000</td>\n",
       "      <td>72668.000000</td>\n",
       "      <td>72668.000000</td>\n",
       "      <td>72668.000000</td>\n",
       "      <td>72668.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "      <td>72983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.122988</td>\n",
       "      <td>2005.343052</td>\n",
       "      <td>4.176644</td>\n",
       "      <td>1.494299</td>\n",
       "      <td>71499.995917</td>\n",
       "      <td>6128.909217</td>\n",
       "      <td>7373.636031</td>\n",
       "      <td>8497.034332</td>\n",
       "      <td>9850.928240</td>\n",
       "      <td>6132.081287</td>\n",
       "      <td>7390.681827</td>\n",
       "      <td>8775.723331</td>\n",
       "      <td>10145.385314</td>\n",
       "      <td>58043.059945</td>\n",
       "      <td>6730.934326</td>\n",
       "      <td>1276.580985</td>\n",
       "      <td>2009.519600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.328425</td>\n",
       "      <td>1.731252</td>\n",
       "      <td>1.712210</td>\n",
       "      <td>0.521290</td>\n",
       "      <td>14578.913128</td>\n",
       "      <td>2461.992768</td>\n",
       "      <td>2722.491986</td>\n",
       "      <td>3156.285284</td>\n",
       "      <td>3385.789541</td>\n",
       "      <td>2434.567723</td>\n",
       "      <td>2686.248852</td>\n",
       "      <td>3090.702941</td>\n",
       "      <td>3310.254351</td>\n",
       "      <td>26151.640415</td>\n",
       "      <td>1767.846435</td>\n",
       "      <td>598.846788</td>\n",
       "      <td>0.499619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4825.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2764.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>462.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61837.000000</td>\n",
       "      <td>4273.000000</td>\n",
       "      <td>5406.000000</td>\n",
       "      <td>6280.000000</td>\n",
       "      <td>7493.000000</td>\n",
       "      <td>4275.000000</td>\n",
       "      <td>5414.000000</td>\n",
       "      <td>6536.000000</td>\n",
       "      <td>7784.000000</td>\n",
       "      <td>32124.000000</td>\n",
       "      <td>5435.000000</td>\n",
       "      <td>837.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73361.000000</td>\n",
       "      <td>6097.000000</td>\n",
       "      <td>7303.000000</td>\n",
       "      <td>8444.000000</td>\n",
       "      <td>9789.000000</td>\n",
       "      <td>6062.000000</td>\n",
       "      <td>7313.000000</td>\n",
       "      <td>8729.000000</td>\n",
       "      <td>10103.000000</td>\n",
       "      <td>73108.000000</td>\n",
       "      <td>6700.000000</td>\n",
       "      <td>1155.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>82436.000000</td>\n",
       "      <td>7765.000000</td>\n",
       "      <td>9021.000000</td>\n",
       "      <td>10651.000000</td>\n",
       "      <td>12088.000000</td>\n",
       "      <td>7736.000000</td>\n",
       "      <td>9013.000000</td>\n",
       "      <td>10911.000000</td>\n",
       "      <td>12309.000000</td>\n",
       "      <td>80022.000000</td>\n",
       "      <td>7900.000000</td>\n",
       "      <td>1623.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>115717.000000</td>\n",
       "      <td>35722.000000</td>\n",
       "      <td>36859.000000</td>\n",
       "      <td>39080.000000</td>\n",
       "      <td>41482.000000</td>\n",
       "      <td>35722.000000</td>\n",
       "      <td>36859.000000</td>\n",
       "      <td>39080.000000</td>\n",
       "      <td>41062.000000</td>\n",
       "      <td>99224.000000</td>\n",
       "      <td>45469.000000</td>\n",
       "      <td>7498.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           IsBadBuy       VehYear    VehicleAge   WheelTypeID         VehOdo  \\\n",
       "count  72983.000000  72983.000000  72983.000000  69814.000000   72983.000000   \n",
       "mean       0.122988   2005.343052      4.176644      1.494299   71499.995917   \n",
       "std        0.328425      1.731252      1.712210      0.521290   14578.913128   \n",
       "min        0.000000   2001.000000      0.000000      0.000000    4825.000000   \n",
       "25%        0.000000   2004.000000      3.000000      1.000000   61837.000000   \n",
       "50%        0.000000   2005.000000      4.000000      1.000000   73361.000000   \n",
       "75%        0.000000   2007.000000      5.000000      2.000000   82436.000000   \n",
       "max        1.000000   2010.000000      9.000000      3.000000  115717.000000   \n",
       "\n",
       "       MMRAcquisitionAuctionAveragePrice  MMRAcquisitionAuctionCleanPrice  \\\n",
       "count                       72965.000000                     72965.000000   \n",
       "mean                         6128.909217                      7373.636031   \n",
       "std                          2461.992768                      2722.491986   \n",
       "min                             0.000000                         0.000000   \n",
       "25%                          4273.000000                      5406.000000   \n",
       "50%                          6097.000000                      7303.000000   \n",
       "75%                          7765.000000                      9021.000000   \n",
       "max                         35722.000000                     36859.000000   \n",
       "\n",
       "       MMRAcquisitionRetailAveragePrice  MMRAcquisitonRetailCleanPrice  \\\n",
       "count                      72965.000000                   72965.000000   \n",
       "mean                        8497.034332                    9850.928240   \n",
       "std                         3156.285284                    3385.789541   \n",
       "min                            0.000000                       0.000000   \n",
       "25%                         6280.000000                    7493.000000   \n",
       "50%                         8444.000000                    9789.000000   \n",
       "75%                        10651.000000                   12088.000000   \n",
       "max                        39080.000000                   41482.000000   \n",
       "\n",
       "       MMRCurrentAuctionAveragePrice  MMRCurrentAuctionCleanPrice  \\\n",
       "count                   72668.000000                 72668.000000   \n",
       "mean                     6132.081287                  7390.681827   \n",
       "std                      2434.567723                  2686.248852   \n",
       "min                         0.000000                     0.000000   \n",
       "25%                      4275.000000                  5414.000000   \n",
       "50%                      6062.000000                  7313.000000   \n",
       "75%                      7736.000000                  9013.000000   \n",
       "max                     35722.000000                 36859.000000   \n",
       "\n",
       "       MMRCurrentRetailAveragePrice  MMRCurrentRetailCleanPrice        VNZIP1  \\\n",
       "count                  72668.000000                72668.000000  72983.000000   \n",
       "mean                    8775.723331                10145.385314  58043.059945   \n",
       "std                     3090.702941                 3310.254351  26151.640415   \n",
       "min                        0.000000                    0.000000   2764.000000   \n",
       "25%                     6536.000000                 7784.000000  32124.000000   \n",
       "50%                     8729.000000                10103.000000  73108.000000   \n",
       "75%                    10911.000000                12309.000000  80022.000000   \n",
       "max                    39080.000000                41062.000000  99224.000000   \n",
       "\n",
       "           VehBCost  WarrantyCost     PurchYear  \n",
       "count  72983.000000  72983.000000  72983.000000  \n",
       "mean    6730.934326   1276.580985   2009.519600  \n",
       "std     1767.846435    598.846788      0.499619  \n",
       "min        1.000000    462.000000   2009.000000  \n",
       "25%     5435.000000    837.000000   2009.000000  \n",
       "50%     6700.000000   1155.000000   2010.000000  \n",
       "75%     7900.000000   1623.000000   2010.000000  \n",
       "max    45469.000000   7498.000000   2010.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check statistical summary\n",
    "df.describe()\n",
    "\n",
    "# zero prices need to be checked, can be exchange trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "527d7acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#downcast to save memory (to be done)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b97aed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check for outliers (visually)\n",
    "# sns.pairplot(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aae2205a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check if outliers are realistic\n",
    "for column in df.drop('IsBadBuy', axis=1).select_dtypes('number'):\n",
    "    plt.figure()\n",
    "    sns.boxplot(data=df.select_dtypes('number'), x=\"IsBadBuy\", y=column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "365b7a9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2407267908.py:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  mask = np.triu(np.ones_like(df.corr(), dtype=bool))\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2407267908.py:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr(), mask=mask, vmin=-1, vmax=1, fmt='.3f', annot=True, ax=ax);\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check correlation matrix\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "mask = np.triu(np.ones_like(df.corr(), dtype=bool))\n",
    "sns.heatmap(df.corr(), mask=mask, vmin=-1, vmax=1, fmt='.3f', annot=True, ax=ax);\n",
    "\n",
    "#MMR columns are highly correlated with each other, will be considered when modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f4fa44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert 'IsOnlineSale' to category as column is categorial\n",
    "df.loc[:, 'IsBadBuy'] = df.loc[:, 'IsBadBuy'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86316909",
   "metadata": {},
   "outputs": [],
   "source": [
    "# VehYear and VehAge are equivalent to each other, thus Year will be dropped\n",
    "df = df.drop(\"VehYear\", axis=1)\n",
    "\n",
    "# drop WheelTypeID as it is equivalent to WheelType (ID may confuse model with ordinal numbers)\n",
    "df = df.drop(\"WheelTypeID\", axis=1)\n",
    "\n",
    "# drop VNZIP1 as it has no correlation to target\n",
    "df = df.drop(\"VNZIP1\", axis=1)\n",
    "\n",
    "# drop SubModel as it's specific than Model and does not add much value\n",
    "df = df.drop(\"SubModel\", axis=1)\n",
    "\n",
    "# drop Model as it has too many unique values (performance)\n",
    "df = df.drop(\"Model\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a238f971",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleaning(df):\n",
    "    \"\"\"\n",
    "    Cleans dataframe from redundant columns and assigns correct data types\n",
    "\n",
    "    Args:\n",
    "        df (pd.DataFrame) : Data set.\n",
    "\n",
    "    Returns:\n",
    "        df (pd.DataFrame) : Cleaned data set.\n",
    "    \"\"\"\n",
    "        \n",
    "    df = df.drop(\"RefId\", axis=1)\n",
    "    \n",
    "    df.loc[:, \"PurchDate\"] = pd.to_datetime(df.loc[:, \"PurchDate\"])\n",
    "    df.loc[:, \"PurchYear\"] = df.loc[:, \"PurchDate\"].dt.year\n",
    "    df = df.drop(\"PurchDate\", axis=1)\n",
    "\n",
    "    for col in df.select_dtypes('object'):\n",
    "        df.loc[:, col] = df.loc[:, col].astype('category')\n",
    "\n",
    "    \n",
    "    df.loc[:, 'IsOnlineSale'] = df.loc[:, 'IsOnlineSale'].astype('category')\n",
    "        \n",
    "    df.loc[:, 'BYRNO'] = df.loc[:, 'BYRNO'].astype('category')\n",
    "    \n",
    "    df.loc[:, 'IsBadBuy'] = df.loc[:, 'IsBadBuy'].astype('category')\n",
    "\n",
    "    df = df.drop(\"VehYear\", axis=1)\n",
    "\n",
    "    df = df.drop(\"WheelTypeID\", axis=1)\n",
    "\n",
    "    df = df.drop(\"VNZIP1\", axis=1)\n",
    "    \n",
    "    df = df.drop(\"SubModel\", axis=1)\n",
    "        \n",
    "    df = df.drop(\"Model\", axis=1)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc29ed5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:14: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, \"PurchDate\"] = pd.to_datetime(df.loc[:, \"PurchDate\"])\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n",
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\2502012669.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, col] = df.loc[:, col].astype('category')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test function on basic level\n",
    "df_copy_cleaned = cleaning(df_copy)\n",
    "df_copy_cleaned.equals(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9035b793",
   "metadata": {},
   "source": [
    "# Train-Test-Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7fe6abbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, random_state=42, test_size = 0.2)\n",
    "\n",
    "df_train_copy = df_train.copy() # for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105e8918",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9007e3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#duplicated rows?\n",
    "df_train.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7d3f33a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AUCGUART                             55633\n",
       "PRIMEUNIT                            55633\n",
       "WheelType                             2530\n",
       "Trim                                  1903\n",
       "MMRCurrentRetailCleanPrice             257\n",
       "MMRCurrentRetailAveragePrice           257\n",
       "MMRCurrentAuctionCleanPrice            257\n",
       "MMRCurrentAuctionAveragePrice          257\n",
       "MMRAcquisitionAuctionAveragePrice       13\n",
       "MMRAcquisitonRetailCleanPrice           13\n",
       "MMRAcquisitionAuctionCleanPrice         13\n",
       "MMRAcquisitionRetailAveragePrice        13\n",
       "Transmission                             7\n",
       "Color                                    6\n",
       "Size                                     4\n",
       "Nationality                              4\n",
       "TopThreeAmericanName                     4\n",
       "BYRNO                                    0\n",
       "VNST                                     0\n",
       "VehBCost                                 0\n",
       "IsOnlineSale                             0\n",
       "WarrantyCost                             0\n",
       "IsBadBuy                                 0\n",
       "Auction                                  0\n",
       "VehOdo                                   0\n",
       "Make                                     0\n",
       "VehicleAge                               0\n",
       "PurchYear                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing values sorted in descending order\n",
    "df_train.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b73f29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop PRIMEUNIT and AUCGUART as too many values are missing that cannot be replaced \n",
    "df_train = df_train.drop([\"PRIMEUNIT\", \"AUCGUART\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ba4c634",
   "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>IsBadBuy</th>\n",
       "      <th>Auction</th>\n",
       "      <th>VehicleAge</th>\n",
       "      <th>Make</th>\n",
       "      <th>Trim</th>\n",
       "      <th>Color</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>WheelType</th>\n",
       "      <th>VehOdo</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>...</th>\n",
       "      <th>MMRCurrentAuctionAveragePrice</th>\n",
       "      <th>MMRCurrentAuctionCleanPrice</th>\n",
       "      <th>MMRCurrentRetailAveragePrice</th>\n",
       "      <th>MMRCurrentRetailCleanPrice</th>\n",
       "      <th>BYRNO</th>\n",
       "      <th>VNST</th>\n",
       "      <th>VehBCost</th>\n",
       "      <th>IsOnlineSale</th>\n",
       "      <th>WarrantyCost</th>\n",
       "      <th>PurchYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>0</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>3</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>Adv</td>\n",
       "      <td>BLUE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>72222</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52117</td>\n",
       "      <td>VA</td>\n",
       "      <td>11340.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1623</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11815</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>6</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LE</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>80971</td>\n",
       "      <td>TOP LINE ASIAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25100</td>\n",
       "      <td>TX</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1373</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>6</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LE</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>82579</td>\n",
       "      <td>TOP LINE ASIAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835</td>\n",
       "      <td>AZ</td>\n",
       "      <td>10240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1373</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68544</th>\n",
       "      <td>0</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>3</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>Adv</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>75238</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22916</td>\n",
       "      <td>CO</td>\n",
       "      <td>11845.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1623</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69722</th>\n",
       "      <td>0</td>\n",
       "      <td>ADESA</td>\n",
       "      <td>2</td>\n",
       "      <td>GMC</td>\n",
       "      <td>SL</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Covers</td>\n",
       "      <td>85899</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835</td>\n",
       "      <td>AZ</td>\n",
       "      <td>7370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1373</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36513</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>2</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>Spo</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>63717</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22808</td>\n",
       "      <td>TX</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1801</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13254</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>3</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>Adv</td>\n",
       "      <td>BLUE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>87956</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52117</td>\n",
       "      <td>NC</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1763</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30278</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>2</td>\n",
       "      <td>CHRYSLER</td>\n",
       "      <td>LX</td>\n",
       "      <td>GREY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Covers</td>\n",
       "      <td>47412</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20928</td>\n",
       "      <td>FL</td>\n",
       "      <td>7950.0</td>\n",
       "      <td>0</td>\n",
       "      <td>834</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64931</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>6</td>\n",
       "      <td>FORD</td>\n",
       "      <td>XL</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>70334</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18822</td>\n",
       "      <td>TX</td>\n",
       "      <td>8655.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1103</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52065</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>6</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LE</td>\n",
       "      <td>GREY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>53383</td>\n",
       "      <td>TOP LINE ASIAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20207</td>\n",
       "      <td>TX</td>\n",
       "      <td>9335.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1003</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7384</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>5</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LE</td>\n",
       "      <td>GREY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>67596</td>\n",
       "      <td>TOP LINE ASIAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18881</td>\n",
       "      <td>GA</td>\n",
       "      <td>10275.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1118</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26135</th>\n",
       "      <td>0</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>6</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LE</td>\n",
       "      <td>GREY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>87310</td>\n",
       "      <td>TOP LINE ASIAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18880</td>\n",
       "      <td>SC</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1373</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26777</th>\n",
       "      <td>0</td>\n",
       "      <td>MANHEIM</td>\n",
       "      <td>6</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LE</td>\n",
       "      <td>GREY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>84790</td>\n",
       "      <td>TOP LINE ASIAN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17212</td>\n",
       "      <td>TX</td>\n",
       "      <td>9895.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1373</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IsBadBuy  Auction  VehicleAge      Make Trim   Color Transmission  \\\n",
       "736          0    ADESA           3     DODGE  Adv    BLUE         AUTO   \n",
       "11815        0  MANHEIM           6    NISSAN   LE   GREEN         AUTO   \n",
       "6550         0  MANHEIM           6    NISSAN   LE   BLACK         AUTO   \n",
       "68544        0    ADESA           3     DODGE  Adv   WHITE         AUTO   \n",
       "69722        0    ADESA           2       GMC   SL   BLACK         AUTO   \n",
       "36513        0  MANHEIM           2      JEEP  Spo  SILVER         AUTO   \n",
       "13254        0  MANHEIM           3     DODGE  Adv    BLUE         AUTO   \n",
       "30278        0  MANHEIM           2  CHRYSLER   LX    GREY         AUTO   \n",
       "64931        0  MANHEIM           6      FORD   XL   WHITE         AUTO   \n",
       "52065        0  MANHEIM           6    NISSAN   LE    GREY         AUTO   \n",
       "7384         0  MANHEIM           5    NISSAN   LE    GREY         AUTO   \n",
       "26135        0    OTHER           6    NISSAN   LE    GREY         AUTO   \n",
       "26777        0  MANHEIM           6    NISSAN   LE    GREY         AUTO   \n",
       "\n",
       "      WheelType  VehOdo     Nationality  ... MMRCurrentAuctionAveragePrice  \\\n",
       "736       Alloy   72222        AMERICAN  ...                           NaN   \n",
       "11815     Alloy   80971  TOP LINE ASIAN  ...                           NaN   \n",
       "6550      Alloy   82579  TOP LINE ASIAN  ...                           NaN   \n",
       "68544     Alloy   75238        AMERICAN  ...                           NaN   \n",
       "69722    Covers   85899        AMERICAN  ...                           NaN   \n",
       "36513     Alloy   63717        AMERICAN  ...                           NaN   \n",
       "13254     Alloy   87956        AMERICAN  ...                           NaN   \n",
       "30278    Covers   47412        AMERICAN  ...                           NaN   \n",
       "64931     Alloy   70334        AMERICAN  ...                           NaN   \n",
       "52065     Alloy   53383  TOP LINE ASIAN  ...                           NaN   \n",
       "7384      Alloy   67596  TOP LINE ASIAN  ...                           NaN   \n",
       "26135     Alloy   87310  TOP LINE ASIAN  ...                           NaN   \n",
       "26777     Alloy   84790  TOP LINE ASIAN  ...                           NaN   \n",
       "\n",
       "      MMRCurrentAuctionCleanPrice  MMRCurrentRetailAveragePrice  \\\n",
       "736                           NaN                           NaN   \n",
       "11815                         NaN                           NaN   \n",
       "6550                          NaN                           NaN   \n",
       "68544                         NaN                           NaN   \n",
       "69722                         NaN                           NaN   \n",
       "36513                         NaN                           NaN   \n",
       "13254                         NaN                           NaN   \n",
       "30278                         NaN                           NaN   \n",
       "64931                         NaN                           NaN   \n",
       "52065                         NaN                           NaN   \n",
       "7384                          NaN                           NaN   \n",
       "26135                         NaN                           NaN   \n",
       "26777                         NaN                           NaN   \n",
       "\n",
       "       MMRCurrentRetailCleanPrice  BYRNO  VNST  VehBCost  IsOnlineSale  \\\n",
       "736                           NaN  52117    VA   11340.0             0   \n",
       "11815                         NaN  25100    TX    9100.0             0   \n",
       "6550                          NaN    835    AZ   10240.0             0   \n",
       "68544                         NaN  22916    CO   11845.0             0   \n",
       "69722                         NaN    835    AZ    7370.0             0   \n",
       "36513                         NaN  22808    TX   10750.0             0   \n",
       "13254                         NaN  52117    NC    9900.0             0   \n",
       "30278                         NaN  20928    FL    7950.0             0   \n",
       "64931                         NaN  18822    TX    8655.0             0   \n",
       "52065                         NaN  20207    TX    9335.0             0   \n",
       "7384                          NaN  18881    GA   10275.0             0   \n",
       "26135                         NaN  18880    SC   10500.0             0   \n",
       "26777                         NaN  17212    TX    9895.0             0   \n",
       "\n",
       "       WarrantyCost  PurchYear  \n",
       "736            1623       2010  \n",
       "11815          1373       2010  \n",
       "6550           1373       2010  \n",
       "68544          1623       2010  \n",
       "69722          1373       2009  \n",
       "36513          1801       2009  \n",
       "13254          1763       2010  \n",
       "30278           834       2010  \n",
       "64931          1103       2009  \n",
       "52065          1003       2010  \n",
       "7384           1118       2009  \n",
       "26135          1373       2010  \n",
       "26777          1373       2010  \n",
       "\n",
       "[13 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_mmr = df_train.loc[:, ['MMRAcquisitionAuctionAveragePrice',\n",
    "                            'MMRAcquisitionAuctionCleanPrice',\n",
    "                            'MMRAcquisitionRetailAveragePrice',\n",
    "                            'MMRAcquisitonRetailCleanPrice',\n",
    "                            'MMRCurrentAuctionAveragePrice',\n",
    "                            'MMRCurrentAuctionCleanPrice',\n",
    "                            'MMRCurrentRetailAveragePrice',\n",
    "                            'MMRCurrentRetailCleanPrice']].isna().all(axis=1)\n",
    "df_train[mask_mmr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "adf45d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop(df_train[mask_mmr].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f26bf94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing missing values of numerical columns (only MMR) with \n",
    "for col in df_train.select_dtypes('number'):\n",
    "    df_train[col] = df_train[col].fillna(df_train[col].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4eb05ce3",
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WheelType                            2530\n",
       "Trim                                 1903\n",
       "Transmission                            7\n",
       "Color                                   6\n",
       "Nationality                             4\n",
       "Size                                    4\n",
       "TopThreeAmericanName                    4\n",
       "IsBadBuy                                0\n",
       "MMRCurrentAuctionCleanPrice             0\n",
       "WarrantyCost                            0\n",
       "IsOnlineSale                            0\n",
       "VehBCost                                0\n",
       "VNST                                    0\n",
       "BYRNO                                   0\n",
       "MMRCurrentRetailCleanPrice              0\n",
       "MMRCurrentRetailAveragePrice            0\n",
       "MMRAcquisitionAuctionCleanPrice         0\n",
       "MMRCurrentAuctionAveragePrice           0\n",
       "MMRAcquisitonRetailCleanPrice           0\n",
       "MMRAcquisitionRetailAveragePrice        0\n",
       "Auction                                 0\n",
       "MMRAcquisitionAuctionAveragePrice       0\n",
       "VehOdo                                  0\n",
       "Make                                    0\n",
       "VehicleAge                              0\n",
       "PurchYear                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking categorial columns for missing values\n",
    "df_train.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "383fefc1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsBadBuy : [0, 1]\n",
      "Categories (2, int64): [0, 1]\n",
      "Auction : ['ADESA', 'OTHER', 'MANHEIM']\n",
      "Categories (3, object): ['ADESA', 'MANHEIM', 'OTHER']\n",
      "Make : ['MAZDA', 'DODGE', 'FORD', 'MITSUBISHI', 'KIA', ..., 'PLYMOUTH', 'LEXUS', 'ACURA', 'TOYOTA SCION', 'HUMMER']\n",
      "Length: 33\n",
      "Categories (33, object): ['ACURA', 'BUICK', 'CADILLAC', 'CHEVROLET', ..., 'TOYOTA', 'TOYOTA SCION', 'VOLKSWAGEN', 'VOLVO']\n",
      "Trim : ['i', 'ST', 'SXT', 'ZX3', 'ES', ..., 'LL', 'Hyb', 'JLX', 'JLS', 'L 3']\n",
      "Length: 135\n",
      "Categories (134, object): ['1', '150', '2', '250', ..., 'eC', 'eL', 'i', 's']\n",
      "Color : ['RED', 'WHITE', 'MAROON', 'SILVER', 'BLACK', ..., 'BROWN', 'YELLOW', 'NOT AVAIL', 'OTHER', NaN]\n",
      "Length: 17\n",
      "Categories (16, object): ['BEIGE', 'BLACK', 'BLUE', 'BROWN', ..., 'RED', 'SILVER', 'WHITE', 'YELLOW']\n",
      "Transmission : ['AUTO', 'MANUAL', NaN, 'Manual']\n",
      "Categories (3, object): ['AUTO', 'MANUAL', 'Manual']\n",
      "WheelType : ['Alloy', 'Covers', NaN, 'Special']\n",
      "Categories (3, object): ['Alloy', 'Covers', 'Special']\n",
      "Nationality : ['OTHER ASIAN', 'AMERICAN', 'TOP LINE ASIAN', 'OTHER', NaN]\n",
      "Categories (4, object): ['AMERICAN', 'OTHER', 'OTHER ASIAN', 'TOP LINE ASIAN']\n",
      "Size : ['MEDIUM', 'LARGE TRUCK', 'COMPACT', 'LARGE', 'VAN', ..., 'SPORTS', 'CROSSOVER', 'SMALL SUV', 'SMALL TRUCK', NaN]\n",
      "Length: 13\n",
      "Categories (12, object): ['COMPACT', 'CROSSOVER', 'LARGE', 'LARGE SUV', ..., 'SMALL TRUCK', 'SPECIALTY', 'SPORTS', 'VAN']\n",
      "TopThreeAmericanName : ['OTHER', 'CHRYSLER', 'FORD', 'GM', NaN]\n",
      "Categories (4, object): ['CHRYSLER', 'FORD', 'GM', 'OTHER']\n",
      "PRIMEUNIT : [NaN, 'NO', 'YES']\n",
      "Categories (2, object): ['NO', 'YES']\n",
      "AUCGUART : [NaN, 'GREEN', 'RED']\n",
      "Categories (2, object): ['GREEN', 'RED']\n",
      "BYRNO : [21973, 19638, 5546, 19619, 20928, ..., 10510, 99760, 10425, 10410, 18091]\n",
      "Length: 74\n",
      "Categories (74, int64): [835, 1031, 1035, 1041, ..., 99741, 99750, 99760, 99761]\n",
      "VNST : ['FL', 'VA', 'IA', 'AR', 'MN', ..., 'NH', 'NJ', 'NY', 'NE', 'KY']\n",
      "Length: 37\n",
      "Categories (37, object): ['AL', 'AR', 'AZ', 'CA', ..., 'UT', 'VA', 'WA', 'WV']\n",
      "IsOnlineSale : [0, 1]\n",
      "Categories (2, int64): [0, 1]\n"
     ]
    }
   ],
   "source": [
    "#checking categorial columns for duplicated unique values\n",
    "for col in df.select_dtypes('category').columns:\n",
    "    print('{} : {}'.format(col, df.loc[:, col].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93bda1d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\348843043.py:2: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df_train.loc[:, 'Transmission'] = df_train.loc[:, 'Transmission'].replace(to_replace='Manual', value='MANUAL')\n"
     ]
    }
   ],
   "source": [
    "# Transmission has 'MANUAL' and 'Manual', will be unified\n",
    "df_train.loc[:, 'Transmission'] = df_train.loc[:, 'Transmission'].replace(to_replace='Manual', value='MANUAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6553071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WheelType                            2530\n",
       "Trim                                 1903\n",
       "Transmission                            7\n",
       "Color                                   6\n",
       "Nationality                             4\n",
       "Size                                    4\n",
       "TopThreeAmericanName                    4\n",
       "IsBadBuy                                0\n",
       "MMRCurrentAuctionCleanPrice             0\n",
       "WarrantyCost                            0\n",
       "IsOnlineSale                            0\n",
       "VehBCost                                0\n",
       "VNST                                    0\n",
       "BYRNO                                   0\n",
       "MMRCurrentRetailCleanPrice              0\n",
       "MMRCurrentRetailAveragePrice            0\n",
       "MMRAcquisitionAuctionCleanPrice         0\n",
       "MMRCurrentAuctionAveragePrice           0\n",
       "MMRAcquisitonRetailCleanPrice           0\n",
       "MMRAcquisitionRetailAveragePrice        0\n",
       "Auction                                 0\n",
       "MMRAcquisitionAuctionAveragePrice       0\n",
       "VehOdo                                  0\n",
       "Make                                    0\n",
       "VehicleAge                              0\n",
       "PurchYear                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6883a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing missing values in categorical variable with mode\n",
    "df_train['Trim'] = df_train['Trim'].fillna(df_train['Trim'].mode()[0])\n",
    "df_train['WheelType'] = df_train['WheelType'].fillna(df_train['WheelType'].mode()[0])\n",
    "df_train['Transmission'] = df_train['Transmission'].fillna(df_train['Transmission'].mode()[0])\n",
    "df_train['Color'] = df_train['Color'].fillna(df_train['Color'].mode()[0])\n",
    "df_train['Nationality'] = df_train['Nationality'].fillna(df_train['Nationality'].mode()[0])\n",
    "df_train['Size'] = df_train['Size'].fillna(df_train['Size'].mode()[0])\n",
    "df_train['TopThreeAmericanName'] = df_train['TopThreeAmericanName'].fillna(df_train['TopThreeAmericanName'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cce82fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsBadBuy                             0\n",
       "Auction                              0\n",
       "WarrantyCost                         0\n",
       "IsOnlineSale                         0\n",
       "VehBCost                             0\n",
       "VNST                                 0\n",
       "BYRNO                                0\n",
       "MMRCurrentRetailCleanPrice           0\n",
       "MMRCurrentRetailAveragePrice         0\n",
       "MMRCurrentAuctionCleanPrice          0\n",
       "MMRCurrentAuctionAveragePrice        0\n",
       "MMRAcquisitonRetailCleanPrice        0\n",
       "MMRAcquisitionRetailAveragePrice     0\n",
       "MMRAcquisitionAuctionCleanPrice      0\n",
       "MMRAcquisitionAuctionAveragePrice    0\n",
       "TopThreeAmericanName                 0\n",
       "Size                                 0\n",
       "Nationality                          0\n",
       "VehOdo                               0\n",
       "WheelType                            0\n",
       "Transmission                         0\n",
       "Color                                0\n",
       "Trim                                 0\n",
       "Make                                 0\n",
       "VehicleAge                           0\n",
       "PurchYear                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7b6e2819",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reset index\n",
    "df_train = df_train.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba489d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imputation function\n",
    "def impute(df):\n",
    "    \"\"\"\n",
    "    Imputes missing values in DataFrame.\n",
    "\n",
    "    Args:\n",
    "        df (pd.DataFrame) : Data set.\n",
    "\n",
    "    Returns:\n",
    "        df (pd.DataFrame) : Imputed data set.\n",
    "    \"\"\"\n",
    "\n",
    "    # numerical columns\n",
    "    df = df.drop([\"PRIMEUNIT\", \"AUCGUART\"], axis=1)\n",
    "\n",
    "    mask_mmr = df.loc[:, ['MMRAcquisitionAuctionAveragePrice',\n",
    "                          'MMRAcquisitionAuctionCleanPrice',\n",
    "                          'MMRAcquisitionRetailAveragePrice',\n",
    "                          'MMRAcquisitonRetailCleanPrice',\n",
    "                          'MMRCurrentAuctionAveragePrice',\n",
    "                          'MMRCurrentAuctionCleanPrice',\n",
    "                          'MMRCurrentRetailAveragePrice',\n",
    "                          'MMRCurrentRetailCleanPrice']].isna().all(axis=1)\n",
    "    df = df.drop(df[mask_mmr].index)\n",
    "\n",
    "    for col in df.select_dtypes('number'):\n",
    "        df[col] = df[col].fillna(df[col].median())\n",
    "\n",
    "    # categorial columns\n",
    "    df.loc[:, 'Transmission'] = df.loc[:, 'Transmission'].replace(to_replace='Manual',\n",
    "                                                                  value='MANUAL')\n",
    "\n",
    "    df['Trim'] = df['Trim'].fillna(df['Trim'].mode()[0])\n",
    "    df['WheelType'] = df['WheelType'].fillna(df['WheelType'].mode()[0])\n",
    "    df['Transmission'] = df['Transmission'].fillna(df['Transmission'].mode()[0])\n",
    "    df['Color'] = df['Color'].fillna(df['Color'].mode()[0])\n",
    "    df['Nationality'] = df['Nationality'].fillna(df['Nationality'].mode()[0])\n",
    "    df['Size'] = df['Size'].fillna(df['Size'].mode()[0])\n",
    "    df['TopThreeAmericanName'] = df['TopThreeAmericanName'].fillna(df['TopThreeAmericanName'].mode()[0])\n",
    "\n",
    "    # reset index\n",
    "    df = df.reset_index(drop=True)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a97563fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\3982782441.py:30: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, 'Transmission'] = df.loc[:, 'Transmission'].replace(to_replace='Manual',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test function on basic level\n",
    "df_train_prep = impute(df_train_copy)\n",
    "(df_train_prep.equals(df_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b0e3dd95",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dk\\AppData\\Local\\Temp\\ipykernel_20832\\3982782441.py:30: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:, 'Transmission'] = df.loc[:, 'Transmission'].replace(to_replace='Manual',\n"
     ]
    }
   ],
   "source": [
    "# imputing test set\n",
    "df_test_prep = impute(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b596655a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save in pickle\n",
    "df_train_prep.to_pickle('df_train_prep.p')\n",
    "df_test_prep.to_pickle('df_test_prep.p')"
   ]
  }
 ],
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