12_Outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "offshore-visibility",
   "metadata": {},
   "source": [
    "# Highlighting outliers / Compare feature magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "civil-advocacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# for the Q-Q plots\n",
    "import scipy.stats as stats\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "pharmaceutical-auditor",
   "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>Color</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>WheelTypeID</th>\n",
       "      <th>WheelType</th>\n",
       "      <th>VehOdo</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Size</th>\n",
       "      <th>TopThreeAmericanName</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>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>RED</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>89046</td>\n",
       "      <td>OTHER ASIAN</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>8155.0</td>\n",
       "      <td>9829.0</td>\n",
       "      <td>11636.0</td>\n",
       "      <td>13600.0</td>\n",
       "      <td>7451.0</td>\n",
       "      <td>8552.0</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>WHITE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Alloy</td>\n",
       "      <td>93593</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>LARGE TRUCK</td>\n",
       "      <td>CHRYSLER</td>\n",
       "      <td>6854.0</td>\n",
       "      <td>8383.0</td>\n",
       "      <td>10897.0</td>\n",
       "      <td>12572.0</td>\n",
       "      <td>7456.0</td>\n",
       "      <td>9222.0</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>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>SILVER</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Covers</td>\n",
       "      <td>69367</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>FORD</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>5054.0</td>\n",
       "      <td>7723.0</td>\n",
       "      <td>8707.0</td>\n",
       "      <td>3247.0</td>\n",
       "      <td>4384.0</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</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>MITSUBISHI</td>\n",
       "      <td>GALANT 4C</td>\n",
       "      <td>ES</td>\n",
       "      <td>4D SEDAN ES</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Covers</td>\n",
       "      <td>81054</td>\n",
       "      <td>OTHER ASIAN</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>4908.0</td>\n",
       "      <td>6706.0</td>\n",
       "      <td>8577.0</td>\n",
       "      <td>4709.0</td>\n",
       "      <td>5827.0</td>\n",
       "      <td>8149.0</td>\n",
       "      <td>9451.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19638</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</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>KIA</td>\n",
       "      <td>SPECTRA</td>\n",
       "      <td>EX</td>\n",
       "      <td>4D SEDAN EX</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Covers</td>\n",
       "      <td>65328</td>\n",
       "      <td>OTHER ASIAN</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>2966.0</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>6240.0</td>\n",
       "      <td>8496.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>4115.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>8603.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19638</td>\n",
       "      <td>33619</td>\n",
       "      <td>FL</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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      5         0  12/7/2009   ADESA     2005           4        FORD   \n",
       "3      6         0  12/7/2009   ADESA     2004           5  MITSUBISHI   \n",
       "4      7         0  12/7/2009   ADESA     2004           5         KIA   \n",
       "\n",
       "                 Model Trim           SubModel   Color Transmission  \\\n",
       "0               MAZDA3    i         4D SEDAN I     RED         AUTO   \n",
       "1  1500 RAM PICKUP 2WD   ST  QUAD CAB 4.7L SLT   WHITE         AUTO   \n",
       "2                FOCUS  ZX3       2D COUPE ZX3  SILVER       MANUAL   \n",
       "3            GALANT 4C   ES        4D SEDAN ES   WHITE         AUTO   \n",
       "4              SPECTRA   EX        4D SEDAN EX   BLACK         AUTO   \n",
       "\n",
       "   WheelTypeID WheelType  VehOdo  Nationality         Size  \\\n",
       "0          1.0     Alloy   89046  OTHER ASIAN       MEDIUM   \n",
       "1          1.0     Alloy   93593     AMERICAN  LARGE TRUCK   \n",
       "2          2.0    Covers   69367     AMERICAN      COMPACT   \n",
       "3          2.0    Covers   81054  OTHER ASIAN       MEDIUM   \n",
       "4          2.0    Covers   65328  OTHER ASIAN       MEDIUM   \n",
       "\n",
       "  TopThreeAmericanName  MMRAcquisitionAuctionAveragePrice  \\\n",
       "0                OTHER                             8155.0   \n",
       "1             CHRYSLER                             6854.0   \n",
       "2                 FORD                             3913.0   \n",
       "3                OTHER                             3901.0   \n",
       "4                OTHER                             2966.0   \n",
       "\n",
       "   MMRAcquisitionAuctionCleanPrice  MMRAcquisitionRetailAveragePrice  \\\n",
       "0                           9829.0                           11636.0   \n",
       "1                           8383.0                           10897.0   \n",
       "2                           5054.0                            7723.0   \n",
       "3                           4908.0                            6706.0   \n",
       "4                           4038.0                            6240.0   \n",
       "\n",
       "   MMRAcquisitonRetailCleanPrice  MMRCurrentAuctionAveragePrice  \\\n",
       "0                        13600.0                         7451.0   \n",
       "1                        12572.0                         7456.0   \n",
       "2                         8707.0                         3247.0   \n",
       "3                         8577.0                         4709.0   \n",
       "4                         8496.0                         2980.0   \n",
       "\n",
       "   MMRCurrentAuctionCleanPrice  MMRCurrentRetailAveragePrice  \\\n",
       "0                       8552.0                       11597.0   \n",
       "1                       9222.0                       11374.0   \n",
       "2                       4384.0                        6739.0   \n",
       "3                       5827.0                        8149.0   \n",
       "4                       4115.0                        6230.0   \n",
       "\n",
       "   MMRCurrentRetailCleanPrice PRIMEUNIT AUCGUART  BYRNO  VNZIP1 VNST  \\\n",
       "0                     12409.0       NaN      NaN  21973   33619   FL   \n",
       "1                     12791.0       NaN      NaN  19638   33619   FL   \n",
       "2                      7911.0       NaN      NaN  19638   33619   FL   \n",
       "3                      9451.0       NaN      NaN  19638   33619   FL   \n",
       "4                      8603.0       NaN      NaN  19638   33619   FL   \n",
       "\n",
       "   VehBCost  IsOnlineSale  WarrantyCost  \n",
       "0    7100.0             0          1113  \n",
       "1    7600.0             0          1053  \n",
       "2    4000.0             0          1020  \n",
       "3    5600.0             0           594  \n",
       "4    4200.0             0           533  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cars = pd.read_csv('data/used_vehicles.csv')\n",
    "df_cars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emerging-visibility",
   "metadata": {},
   "source": [
    "### Visualize the variable distribution with histograms:\n",
    "- create plots to distinguish the variable distributions in the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "prepared-ebony",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalize the histogram by setting density to True\n",
    "df_cars.hist(bins=30, figsize=(12,12), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-talent",
   "metadata": {},
   "source": [
    "An `outlier` is a data point which is significantly different from the remaining data. It deviates so much from the other observations.\n",
    "\n",
    "Identify outliers using `boxplots` and the `inter-quantile range proximity` rule.\n",
    "\n",
    "According to the inter-quantile range proximity rule, a value is an outlier if it falls outside the below boundaries:\n",
    "\n",
    "- `Upper boundary` = 75th quantile + (IQR * 1.5)\n",
    "\n",
    "- `Lower boundary` = 25th quantile - (IQR * 1.5)\n",
    "\n",
    "where the inter-quantile range (IQR) is given by:\n",
    "\n",
    "- `IQR` = 75th quantile - 25th quantile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electronic-equation",
   "metadata": {},
   "source": [
    "In the boxplot displayed below, the IQR is indicated by the box, the median is indicated by the line within the box, the top and bottom edges of the box correspond to the 75th and 25th quantile, and the whiskers  mark the proximity rule boundaries as described above. \n",
    "\n",
    "Values that `fall outside` the whiskers are considered outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "suspended-arctic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot\n",
    "plt.figure(figsize=(3,6)) \n",
    "sns.boxplot(y=df_cars['VehOdo'])\n",
    "plt.title('Boxplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "identified-registration",
   "metadata": {},
   "source": [
    "> ### Find the upper and lower boundaries using the IQR proximity rule function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "taken-aaron",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_boundaries(df, variable, distance):\n",
    "\n",
    "    # distance passed as an argument, gives us the option to\n",
    "    # estimate 1.5 times or 3 times the IQR to calculate the boundaries.\n",
    "    #it is also common practice to find extreme values by multiplying the IQR by 3\n",
    "    \n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_boundary = df[variable].quantile(0.25) - (IQR * distance)\n",
    "    upper_boundary = df[variable].quantile(0.75) + (IQR * distance)\n",
    "\n",
    "    return upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "short-peripheral",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113394.875, 30949.875)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the boudaries for the variable VehOdo \n",
    "\n",
    "upper_boundary, lower_boundary = find_boundaries(df_cars, 'VehOdo', 1.5)\n",
    "upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liked-camel",
   "metadata": {},
   "source": [
    "## NumPy's where() method\n",
    "\n",
    "Produces a `boolean vector` with `True` if the value was an outlier. \n",
    "\n",
    "The `where()` method scans the rows of the VehOdo variable, and if the value was bigger than the upper boundary, it assigned True.\n",
    "\n",
    "Whereas **if the value was smaller**, the `second where()` nested inside the first one and checked whether the value was smaller than the lower boundary, in which case it also assigned True, otherwise False ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "animal-polyester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flag the outliers in the data set\n",
    "outliers = np.where(df_cars['VehOdo'] > upper_boundary, True, np.where(df_cars['VehOdo'] < lower_boundary, True, False))\n",
    "\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "impaired-laundry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outliers found\n",
    "outliers.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "small-banking",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51088, 34)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "86      23881\n",
       "282     25830\n",
       "834     10643\n",
       "837     22500\n",
       "1044    30471\n",
       "Name: VehOdo, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_cars.shape)\n",
    "\n",
    "outliers_df = df_cars.loc[outliers, 'VehOdo']\n",
    "outliers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "instant-clinic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50853,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove outliers\n",
    "tmp = df_cars.loc[~outliers, 'VehOdo']\n",
    "tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "guided-sector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot (VehOdo without outliers)')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot\n",
    "plt.figure(figsize=(3,6))\n",
    "sns.boxplot(y=tmp)\n",
    "plt.title('Boxplot (VehOdo without outliers)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "greatest-enforcement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='VehOdo', ylabel='Count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VehOdo without outliers\n",
    "sns.histplot(tmp, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "paperback-florist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='VehOdo', ylabel='Count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VehOdo with outliers\n",
    "sns.histplot(df_cars['VehOdo'], bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "threatened-shelf",
   "metadata": {},
   "source": [
    "### Compare feature magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "prospective-society",
   "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>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>BYRNO</th>\n",
       "      <th>VNZIP1</th>\n",
       "      <th>VehBCost</th>\n",
       "      <th>IsOnlineSale</th>\n",
       "      <th>WarrantyCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>48819.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51076.000000</td>\n",
       "      <td>51076.000000</td>\n",
       "      <td>51076.000000</td>\n",
       "      <td>51076.000000</td>\n",
       "      <td>50872.000000</td>\n",
       "      <td>50872.000000</td>\n",
       "      <td>50872.000000</td>\n",
       "      <td>50872.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "      <td>51088.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36618.299503</td>\n",
       "      <td>0.123101</td>\n",
       "      <td>2005.345893</td>\n",
       "      <td>4.172780</td>\n",
       "      <td>1.494992</td>\n",
       "      <td>71550.864880</td>\n",
       "      <td>6130.263999</td>\n",
       "      <td>7376.365671</td>\n",
       "      <td>8497.024434</td>\n",
       "      <td>9853.113086</td>\n",
       "      <td>6135.332934</td>\n",
       "      <td>7394.514251</td>\n",
       "      <td>8776.393262</td>\n",
       "      <td>10146.978357</td>\n",
       "      <td>26376.254913</td>\n",
       "      <td>58154.511764</td>\n",
       "      <td>6728.222272</td>\n",
       "      <td>0.025035</td>\n",
       "      <td>1276.712320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21047.637595</td>\n",
       "      <td>0.328557</td>\n",
       "      <td>1.727976</td>\n",
       "      <td>1.709315</td>\n",
       "      <td>0.521792</td>\n",
       "      <td>14610.426618</td>\n",
       "      <td>2461.426275</td>\n",
       "      <td>2721.803428</td>\n",
       "      <td>3155.655885</td>\n",
       "      <td>3385.328527</td>\n",
       "      <td>2432.202199</td>\n",
       "      <td>2682.701099</td>\n",
       "      <td>3087.064877</td>\n",
       "      <td>3305.865439</td>\n",
       "      <td>25727.370369</td>\n",
       "      <td>26148.295399</td>\n",
       "      <td>1768.991083</td>\n",
       "      <td>0.156234</td>\n",
       "      <td>600.140901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5368.000000</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>835.000000</td>\n",
       "      <td>2764.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>462.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18479.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61866.750000</td>\n",
       "      <td>4273.000000</td>\n",
       "      <td>5406.000000</td>\n",
       "      <td>6268.000000</td>\n",
       "      <td>7483.000000</td>\n",
       "      <td>4279.000000</td>\n",
       "      <td>5415.000000</td>\n",
       "      <td>6533.000000</td>\n",
       "      <td>7781.000000</td>\n",
       "      <td>17212.000000</td>\n",
       "      <td>32124.000000</td>\n",
       "      <td>5425.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>837.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36685.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73434.500000</td>\n",
       "      <td>6099.500000</td>\n",
       "      <td>7306.500000</td>\n",
       "      <td>8435.500000</td>\n",
       "      <td>9772.000000</td>\n",
       "      <td>6063.000000</td>\n",
       "      <td>7308.000000</td>\n",
       "      <td>8715.000000</td>\n",
       "      <td>10099.500000</td>\n",
       "      <td>19662.000000</td>\n",
       "      <td>74135.000000</td>\n",
       "      <td>6700.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54851.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>82478.000000</td>\n",
       "      <td>7768.000000</td>\n",
       "      <td>9027.000000</td>\n",
       "      <td>10648.000000</td>\n",
       "      <td>12088.000000</td>\n",
       "      <td>7743.000000</td>\n",
       "      <td>9018.000000</td>\n",
       "      <td>10915.000000</td>\n",
       "      <td>12309.000000</td>\n",
       "      <td>22808.000000</td>\n",
       "      <td>80022.000000</td>\n",
       "      <td>7900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73013.000000</td>\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>99761.000000</td>\n",
       "      <td>99224.000000</td>\n",
       "      <td>38785.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7498.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RefId      IsBadBuy       VehYear    VehicleAge   WheelTypeID  \\\n",
       "count  51088.000000  51088.000000  51088.000000  51088.000000  48819.000000   \n",
       "mean   36618.299503      0.123101   2005.345893      4.172780      1.494992   \n",
       "std    21047.637595      0.328557      1.727976      1.709315      0.521792   \n",
       "min        1.000000      0.000000   2001.000000      0.000000      0.000000   \n",
       "25%    18479.250000      0.000000   2004.000000      3.000000      1.000000   \n",
       "50%    36685.000000      0.000000   2005.000000      4.000000      1.000000   \n",
       "75%    54851.500000      0.000000   2007.000000      5.000000      2.000000   \n",
       "max    73013.000000      1.000000   2010.000000      9.000000      3.000000   \n",
       "\n",
       "              VehOdo  MMRAcquisitionAuctionAveragePrice  \\\n",
       "count   51088.000000                       51076.000000   \n",
       "mean    71550.864880                        6130.263999   \n",
       "std     14610.426618                        2461.426275   \n",
       "min      5368.000000                           0.000000   \n",
       "25%     61866.750000                        4273.000000   \n",
       "50%     73434.500000                        6099.500000   \n",
       "75%     82478.000000                        7768.000000   \n",
       "max    115717.000000                       35722.000000   \n",
       "\n",
       "       MMRAcquisitionAuctionCleanPrice  MMRAcquisitionRetailAveragePrice  \\\n",
       "count                     51076.000000                      51076.000000   \n",
       "mean                       7376.365671                       8497.024434   \n",
       "std                        2721.803428                       3155.655885   \n",
       "min                           0.000000                          0.000000   \n",
       "25%                        5406.000000                       6268.000000   \n",
       "50%                        7306.500000                       8435.500000   \n",
       "75%                        9027.000000                      10648.000000   \n",
       "max                       36859.000000                      39080.000000   \n",
       "\n",
       "       MMRAcquisitonRetailCleanPrice  MMRCurrentAuctionAveragePrice  \\\n",
       "count                   51076.000000                   50872.000000   \n",
       "mean                     9853.113086                    6135.332934   \n",
       "std                      3385.328527                    2432.202199   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                      7483.000000                    4279.000000   \n",
       "50%                      9772.000000                    6063.000000   \n",
       "75%                     12088.000000                    7743.000000   \n",
       "max                     41482.000000                   35722.000000   \n",
       "\n",
       "       MMRCurrentAuctionCleanPrice  MMRCurrentRetailAveragePrice  \\\n",
       "count                 50872.000000                  50872.000000   \n",
       "mean                   7394.514251                   8776.393262   \n",
       "std                    2682.701099                   3087.064877   \n",
       "min                       0.000000                      0.000000   \n",
       "25%                    5415.000000                   6533.000000   \n",
       "50%                    7308.000000                   8715.000000   \n",
       "75%                    9018.000000                  10915.000000   \n",
       "max                   36859.000000                  39080.000000   \n",
       "\n",
       "       MMRCurrentRetailCleanPrice         BYRNO        VNZIP1      VehBCost  \\\n",
       "count                50872.000000  51088.000000  51088.000000  51088.000000   \n",
       "mean                 10146.978357  26376.254913  58154.511764   6728.222272   \n",
       "std                   3305.865439  25727.370369  26148.295399   1768.991083   \n",
       "min                      0.000000    835.000000   2764.000000      1.000000   \n",
       "25%                   7781.000000  17212.000000  32124.000000   5425.000000   \n",
       "50%                  10099.500000  19662.000000  74135.000000   6700.000000   \n",
       "75%                  12309.000000  22808.000000  80022.000000   7900.000000   \n",
       "max                  41062.000000  99761.000000  99224.000000  38785.000000   \n",
       "\n",
       "       IsOnlineSale  WarrantyCost  \n",
       "count  51088.000000  51088.000000  \n",
       "mean       0.025035   1276.712320  \n",
       "std        0.156234    600.140901  \n",
       "min        0.000000    462.000000  \n",
       "25%        0.000000    837.000000  \n",
       "50%        0.000000   1169.000000  \n",
       "75%        0.000000   1623.000000  \n",
       "max        1.000000   7498.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "seasonal-richmond",
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "altered-spring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VehYear                              2010.0\n",
       "VehicleAge                              9.0\n",
       "MMRAcquisitionAuctionCleanPrice     36859.0\n",
       "MMRAcquisitionRetailAveragePrice    39080.0\n",
       "MMRAcquisitonRetailCleanPrice       41482.0\n",
       "MMRCurrentAuctionAveragePrice       35722.0\n",
       "MMRCurrentAuctionCleanPrice         36859.0\n",
       "MMRCurrentRetailAveragePrice        39080.0\n",
       "MMRCurrentRetailCleanPrice          41062.0\n",
       "VehBCost                            38785.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "columns = ['VehYear', 'VehicleAge', 'MMRAcquisitionAuctionCleanPrice', 'MMRAcquisitionRetailAveragePrice',\n",
    "       'MMRAcquisitonRetailCleanPrice', 'MMRCurrentAuctionAveragePrice',\n",
    "       'MMRCurrentAuctionCleanPrice', 'MMRCurrentRetailAveragePrice',\n",
    "       'MMRCurrentRetailCleanPrice','VehBCost']\n",
    "\n",
    "\n",
    "result1 = df_cars[columns].max()\n",
    "\n",
    "result1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "solved-uzbekistan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VehYear                             2001.0\n",
       "VehicleAge                             0.0\n",
       "MMRAcquisitionAuctionCleanPrice        0.0\n",
       "MMRAcquisitionRetailAveragePrice       0.0\n",
       "MMRAcquisitonRetailCleanPrice          0.0\n",
       "MMRCurrentAuctionAveragePrice          0.0\n",
       "MMRCurrentAuctionCleanPrice            0.0\n",
       "MMRCurrentRetailAveragePrice           0.0\n",
       "MMRCurrentRetailCleanPrice             0.0\n",
       "VehBCost                               1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = df_cars[columns].min()\n",
    "\n",
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "vital-thong",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VehYear                                 9.0\n",
       "VehicleAge                              9.0\n",
       "MMRAcquisitionAuctionCleanPrice     36859.0\n",
       "MMRAcquisitionRetailAveragePrice    39080.0\n",
       "MMRAcquisitonRetailCleanPrice       41482.0\n",
       "MMRCurrentAuctionAveragePrice       35722.0\n",
       "MMRCurrentAuctionCleanPrice         36859.0\n",
       "MMRCurrentRetailAveragePrice        39080.0\n",
       "MMRCurrentRetailCleanPrice          41062.0\n",
       "VehBCost                            38784.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = result1 - result2\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "appointed-georgia",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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