sklearn -Multiple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this practical example we will need the following libraries and modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1991</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>Sprinter 212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>S 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>Q7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>Rav 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
       "\n",
       "  Registration  Year         Model  \n",
       "0          yes  1991           320  \n",
       "1          yes  1999  Sprinter 212  \n",
       "2          yes  2003         S 500  \n",
       "3          yes  2007            Q7  \n",
       "4          yes  2011         Rav 4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data from a .csv in the same folder\n",
    "raw_data = pd.read_csv('1.04. Real-life example.csv')\n",
    "\n",
    "# Let's explore the top 5 rows of the df\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the descriptive statistics of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4345</td>\n",
       "      <td>4173.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4195.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>3947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19418.746935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.237284</td>\n",
       "      <td>2.790734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.550058</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25584.242620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.705797</td>\n",
       "      <td>5.066437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.719097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           936            NaN   1649          NaN          NaN   \n",
       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year    Model  \n",
       "count         4345         4345  4345.000000     4345  \n",
       "unique           4            2          NaN      312  \n",
       "top         Diesel          yes          NaN  E-Class  \n",
       "freq          2019         3947          NaN      199  \n",
       "mean           NaN          NaN  2006.550058      NaN  \n",
       "std            NaN          NaN     6.719097      NaN  \n",
       "min            NaN          NaN  1969.000000      NaN  \n",
       "25%            NaN          NaN  2003.000000      NaN  \n",
       "50%            NaN          NaN  2008.000000      NaN  \n",
       "75%            NaN          NaN  2012.000000      NaN  \n",
       "max            NaN          NaN  2016.000000      NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics are very useful for initial exploration of the variables\n",
    "# By default, only descriptives for the numerical variables are shown\n",
    "# To include the categorical ones, you should specify this with an argument\n",
    "raw_data.describe(include='all')\n",
    "\n",
    "# Note that categorical variables don't have some types of numerical descriptives\n",
    "# and numerical variables don't have some types of categorical descriptives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining the variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
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       "      <th>count</th>\n",
       "      <td>4345</td>\n",
       "      <td>4173.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
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       "      <td>4345</td>\n",
       "      <td>4345</td>\n",
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       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>3947</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19418.746935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.237284</td>\n",
       "      <td>2.790734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.550058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25584.242620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.705797</td>\n",
       "      <td>5.066437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.719097</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
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      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           936            NaN   1649          NaN          NaN   \n",
       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         4345         4345  4345.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          2019         3947          NaN  \n",
       "mean           NaN          NaN  2006.550058  \n",
       "std            NaN          NaN     6.719097  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2008.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Certainly, when you work on the problem on your own, you could create a regression with 'Model'\n",
    "data = raw_data.drop(['Model'],axis=1)\n",
    "\n",
    "# Let's check the descriptives without 'Model'\n",
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand             0\n",
       "Price           172\n",
       "Body              0\n",
       "Mileage           0\n",
       "EngineV         150\n",
       "Engine Type       0\n",
       "Registration      0\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.isnull() # shows a df with the information whether a data point is null \n",
    "# Since True = the data point is missing, while False = the data point is not missing, we can sum them\n",
    "# This will give us the total number of missing values feature-wise\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's simply drop all missing values\n",
    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay\n",
    "data_no_mv = data.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1861</td>\n",
       "      <td>3654</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19552.308065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.572174</td>\n",
       "      <td>2.764586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.379627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25815.734988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.394703</td>\n",
       "      <td>4.935941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.695595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21900.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           880            NaN   1534          NaN          NaN   \n",
       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         4025         4025  4025.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1861         3654          NaN  \n",
       "mean           NaN          NaN  2006.379627  \n",
       "std            NaN          NaN     6.695595  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2007.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the descriptives without the missing values\n",
    "data_no_mv.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the PDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897ab4940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b897a96438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A great step in the data exploration is to display the probability distribution function (PDF) of a variable\n",
    "# The PDF will show us how that variable is distributed \n",
    "# This makes it very easy to spot anomalies, such as outliers\n",
    "# The PDF is often the basis on which we decide whether we want to transform a feature\n",
    "sns.distplot(data_no_mv['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3984</td>\n",
       "      <td>3984.000000</td>\n",
       "      <td>3984</td>\n",
       "      <td>3984.000000</td>\n",
       "      <td>3984.000000</td>\n",
       "      <td>3984</td>\n",
       "      <td>3984</td>\n",
       "      <td>3984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1853</td>\n",
       "      <td>3613</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17837.117460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.116466</td>\n",
       "      <td>2.743770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.292922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18976.268315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.766126</td>\n",
       "      <td>4.956057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.672745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6980.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11400.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>129222.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         3984    3984.000000   3984  3984.000000  3984.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           880            NaN   1528          NaN          NaN   \n",
       "mean           NaN   17837.117460    NaN   165.116466     2.743770   \n",
       "std            NaN   18976.268315    NaN   102.766126     4.956057   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6980.000000    NaN    93.000000     1.800000   \n",
       "50%            NaN   11400.000000    NaN   160.000000     2.200000   \n",
       "75%            NaN   21000.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  129222.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3984         3984  3984.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1853         3613          NaN  \n",
       "mean           NaN          NaN  2006.292922  \n",
       "std            NaN          NaN     6.672745  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2002.750000  \n",
       "50%            NaN          NaN  2007.000000  \n",
       "75%            NaN          NaN  2011.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obviously there are some outliers present \n",
    "\n",
    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
    "# Logic should also be applied\n",
    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
    "\n",
    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
    "# It may be a useful exercise to try training a model without removing the outliers\n",
    "\n",
    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
    "q = data_no_mv['Price'].quantile(0.99)\n",
    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
    "data_1.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897eacfd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b897e699e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can check the PDF once again to ensure that the result is still distributed in the same way overall\n",
    "# however, there are much fewer outliers\n",
    "sns.distplot(data_1['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897e36e80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b897ec4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can treat the other numerical variables in a similar way\n",
    "sns.distplot(data_no_mv['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_1['Mileage'].quantile(0.99)\n",
    "data_2 = data_1[data_1['Mileage']<q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8980359b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b89803a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This plot looks kind of normal, doesn't it?\n",
    "sns.distplot(data_2['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b897f681d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b89806f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The situation with engine volume is very strange\n",
    "# In such cases it makes sense to manually check what may be causing the problem\n",
    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
    "# There are also some incorrect entries like 75\n",
    "sns.distplot(data_no_mv['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A simple Google search can indicate the natural domain of this variable\n",
    "# Car engine volumes are usually (always?) below 6.5l\n",
    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
    "# may find it much easier to determine problems with the data than an outsider\n",
    "data_3 = data_2[data_2['EngineV']<6.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8981a0b00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b898106240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Following this graph, we realize we can actually treat EngineV as a categorical variable\n",
    "# Even so, in this course we won't, but that's yet something else you may try on your own\n",
    "sns.distplot(data_3['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b89825e6a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b89827c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
    "# However, the outliers are on the low end\n",
    "sns.distplot(data_no_mv['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'll simply remove them\n",
    "q = data_3['Year'].quantile(0.01)\n",
    "data_4 = data_3[data_3['Year']>q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b898296d30>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b898339160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the new result\n",
    "sns.distplot(data_4['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When we remove observations, the original indexes are preserved\n",
    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
    "# That's very problematic as we tend to forget about it (later you will see an example of such a problem)\n",
    "\n",
    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
    "data_cleaned = data_4.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1807</td>\n",
       "      <td>3505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18194.455679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.542539</td>\n",
       "      <td>2.450440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.709853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19085.855165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.633291</td>\n",
       "      <td>0.949366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.103870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>129222.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           848            NaN   1467          NaN          NaN   \n",
       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3867         3867  3867.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1807         3505          NaN  \n",
       "mean           NaN          NaN  2006.709853  \n",
       "std            NaN          NaN     6.103870  \n",
       "min            NaN          NaN  1988.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2008.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see what's left\n",
    "data_cleaned.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the OLS assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b898401828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we decided to use some matplotlib code, without explaining it\n",
    "# You can simply use plt.scatter() for each of them (with your current knowledge)\n",
    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
    "ax1.set_title('Price and Year')\n",
    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
    "ax2.set_title('Price and EngineV')\n",
    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
    "ax3.set_title('Price and Mileage')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b8994ccdd8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8983c59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
    "# A good transformation in that case is a log transformation\n",
    "sns.distplot(data_cleaned['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relaxing the assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.342840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.974618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.495519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.20</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>10.043249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.814656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Audi</td>\n",
       "      <td>14200.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>200</td>\n",
       "      <td>2.70</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2006</td>\n",
       "      <td>9.560997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Renault</td>\n",
       "      <td>10799.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>193</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>9.287209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>other</td>\n",
       "      <td>212</td>\n",
       "      <td>1.80</td>\n",
       "      <td>Gas</td>\n",
       "      <td>no</td>\n",
       "      <td>1999</td>\n",
       "      <td>7.244228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Renault</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>177</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.388487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Renault</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>260</td>\n",
       "      <td>1.79</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1994</td>\n",
       "      <td>7.824046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Audi</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>165</td>\n",
       "      <td>2.70</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>9.159047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>100</td>\n",
       "      <td>1.80</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.259131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>250</td>\n",
       "      <td>4.70</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2001</td>\n",
       "      <td>9.680344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Renault</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>hatch</td>\n",
       "      <td>84</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>9.059517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2990.0</td>\n",
       "      <td>other</td>\n",
       "      <td>203</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>no</td>\n",
       "      <td>2001</td>\n",
       "      <td>8.003029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>26500.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>21</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>10.184900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Audi</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>250</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>no</td>\n",
       "      <td>1998</td>\n",
       "      <td>8.160518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>38233.0</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016</td>\n",
       "      <td>10.551454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>hatch</td>\n",
       "      <td>132</td>\n",
       "      <td>1.40</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2006</td>\n",
       "      <td>8.922658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Audi</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>225</td>\n",
       "      <td>2.40</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>1998</td>\n",
       "      <td>8.824678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>130</td>\n",
       "      <td>2.40</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2006</td>\n",
       "      <td>9.259131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Audi</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>163</td>\n",
       "      <td>4.20</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>10.122623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>20800.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>151</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.942708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Audi</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>330</td>\n",
       "      <td>2.40</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.779557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>13566.0</td>\n",
       "      <td>other</td>\n",
       "      <td>171</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Other</td>\n",
       "      <td>no</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.515322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>hatch</td>\n",
       "      <td>65</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>9.047821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Audi</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1</td>\n",
       "      <td>2.30</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>1989</td>\n",
       "      <td>7.972466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BMW</td>\n",
       "      <td>21500.0</td>\n",
       "      <td>other</td>\n",
       "      <td>72</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>9.975808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>87</td>\n",
       "      <td>3.80</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.792556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BMW</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>160</td>\n",
       "      <td>4.80</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>10.257659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3837</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>180</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.350102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>van</td>\n",
       "      <td>247</td>\n",
       "      <td>2.40</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.179881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>Renault</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>185</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.259131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3840</th>\n",
       "      <td>Renault</td>\n",
       "      <td>10900.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>180</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>9.296518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3841</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>77</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>10.146434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3842</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>80</td>\n",
       "      <td>1.40</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>9.648595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3843</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>9750.0</td>\n",
       "      <td>van</td>\n",
       "      <td>159</td>\n",
       "      <td>1.90</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2009</td>\n",
       "      <td>9.185023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3844</th>\n",
       "      <td>BMW</td>\n",
       "      <td>16100.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>194</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2004</td>\n",
       "      <td>9.686575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3845</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>171</td>\n",
       "      <td>1.60</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.126959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>5150.0</td>\n",
       "      <td>van</td>\n",
       "      <td>240</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2005</td>\n",
       "      <td>8.546752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3847</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.512925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3848</th>\n",
       "      <td>Renault</td>\n",
       "      <td>8999.0</td>\n",
       "      <td>other</td>\n",
       "      <td>126</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>9.104869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3849</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>16800.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>125</td>\n",
       "      <td>1.80</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.729134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3850</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>8200.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>280</td>\n",
       "      <td>2.40</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>1997</td>\n",
       "      <td>9.011889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3851</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>1.80</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>10.124629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3852</th>\n",
       "      <td>Audi</td>\n",
       "      <td>80999.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.302192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3853</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>van</td>\n",
       "      <td>207</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>8.895630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3854</th>\n",
       "      <td>BMW</td>\n",
       "      <td>21335.0</td>\n",
       "      <td>other</td>\n",
       "      <td>105</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.968104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3855</th>\n",
       "      <td>BMW</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>10.714418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3856</th>\n",
       "      <td>Renault</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>van</td>\n",
       "      <td>155</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>8.817298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3857</th>\n",
       "      <td>Renault</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>van</td>\n",
       "      <td>210</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2005</td>\n",
       "      <td>8.853665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3858</th>\n",
       "      <td>BMW</td>\n",
       "      <td>12090.0</td>\n",
       "      <td>hatch</td>\n",
       "      <td>145</td>\n",
       "      <td>1.60</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>9.400134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>BMW</td>\n",
       "      <td>27900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>38</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>10.236382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3860</th>\n",
       "      <td>Renault</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>vagon</td>\n",
       "      <td>237</td>\n",
       "      <td>1.90</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>no</td>\n",
       "      <td>2001</td>\n",
       "      <td>7.649693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>Renault</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>152</td>\n",
       "      <td>1.60</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>8.824678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>163</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.350102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>35</td>\n",
       "      <td>1.60</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.792556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2014</td>\n",
       "      <td>11.736069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3865</th>\n",
       "      <td>BMW</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.779557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3866</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>124</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>9.510445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3867 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
       "0               BMW    4200.0      sedan      277     2.00      Petrol   \n",
       "1     Mercedes-Benz    7900.0        van      427     2.90      Diesel   \n",
       "2     Mercedes-Benz   13300.0      sedan      358     5.00         Gas   \n",
       "3              Audi   23000.0  crossover      240     4.20      Petrol   \n",
       "4            Toyota   18300.0  crossover      120     2.00      Petrol   \n",
       "5              Audi   14200.0      vagon      200     2.70      Diesel   \n",
       "6           Renault   10799.0      vagon      193     1.50      Diesel   \n",
       "7        Volkswagen    1400.0      other      212     1.80         Gas   \n",
       "8           Renault   11950.0      vagon      177     1.50      Diesel   \n",
       "9           Renault    2500.0      sedan      260     1.79      Petrol   \n",
       "10             Audi    9500.0      vagon      165     2.70         Gas   \n",
       "11       Volkswagen   10500.0      sedan      100     1.80      Petrol   \n",
       "12           Toyota   16000.0  crossover      250     4.70         Gas   \n",
       "13          Renault    8600.0      hatch       84     1.50      Diesel   \n",
       "14              BMW    2990.0      other      203     2.00      Petrol   \n",
       "15           Toyota   26500.0  crossover       21     2.00      Petrol   \n",
       "16             Audi    3500.0      vagon      250     2.50      Diesel   \n",
       "17           Toyota   38233.0      other        0     2.40      Diesel   \n",
       "18       Volkswagen    7500.0      hatch      132     1.40      Diesel   \n",
       "19             Audi    6800.0      sedan      225     2.40         Gas   \n",
       "20       Mitsubishi   10500.0  crossover      130     2.40         Gas   \n",
       "21             Audi   24900.0      sedan      163     4.20      Diesel   \n",
       "22       Volkswagen   20800.0  crossover      151     3.00      Diesel   \n",
       "23             Audi    6500.0      sedan      330     2.40      Petrol   \n",
       "24    Mercedes-Benz   13566.0      other      171     2.20       Other   \n",
       "25       Mitsubishi    8500.0      hatch       65     1.30      Petrol   \n",
       "26             Audi    2900.0      sedan        1     2.30         Gas   \n",
       "27              BMW   21500.0      other       72     3.00      Petrol   \n",
       "28       Mitsubishi   17900.0  crossover       87     3.80         Gas   \n",
       "29              BMW   28500.0  crossover      160     4.80         Gas   \n",
       "...             ...       ...        ...      ...      ...         ...   \n",
       "3837  Mercedes-Benz   11500.0        van      180     2.20      Diesel   \n",
       "3838     Mitsubishi    9700.0        van      247     2.40         Gas   \n",
       "3839        Renault   10500.0      vagon      185     1.50      Diesel   \n",
       "3840        Renault   10900.0      vagon      180     1.50      Diesel   \n",
       "3841  Mercedes-Benz   25500.0  crossover       77     3.50      Petrol   \n",
       "3842     Volkswagen   15500.0      sedan       80     1.40      Petrol   \n",
       "3843     Volkswagen    9750.0        van      159     1.90      Diesel   \n",
       "3844            BMW   16100.0  crossover      194     3.00      Diesel   \n",
       "3845     Volkswagen    9200.0      vagon      171     1.60      Petrol   \n",
       "3846     Volkswagen    5150.0        van      240     2.00      Diesel   \n",
       "3847         Toyota  100000.0  crossover        0     4.50      Diesel   \n",
       "3848        Renault    8999.0      other      126     2.00      Diesel   \n",
       "3849  Mercedes-Benz   16800.0      sedan      125     1.80      Petrol   \n",
       "3850  Mercedes-Benz    8200.0      sedan      280     2.40         Gas   \n",
       "3851  Mercedes-Benz   24950.0      other       60     1.80      Petrol   \n",
       "3852           Audi   80999.0  crossover        0     3.00      Diesel   \n",
       "3853  Mercedes-Benz    7300.0        van      207     2.20      Diesel   \n",
       "3854            BMW   21335.0      other      105     3.00      Petrol   \n",
       "3855            BMW   45000.0  crossover       80     3.00      Petrol   \n",
       "3856        Renault    6750.0        van      155     1.50      Diesel   \n",
       "3857        Renault    7000.0        van      210     1.50      Diesel   \n",
       "3858            BMW   12090.0      hatch      145     1.60      Petrol   \n",
       "3859            BMW   27900.0      sedan       38     2.00      Petrol   \n",
       "3860        Renault    2100.0      vagon      237     1.90      Diesel   \n",
       "3861        Renault    6800.0      sedan      152     1.60      Petrol   \n",
       "3862     Volkswagen   11500.0        van      163     2.50      Diesel   \n",
       "3863         Toyota   17900.0      sedan       35     1.60      Petrol   \n",
       "3864  Mercedes-Benz  125000.0      sedan        9     3.00      Diesel   \n",
       "3865            BMW    6500.0      sedan        1     3.50      Petrol   \n",
       "3866     Volkswagen   13500.0        van      124     2.00      Diesel   \n",
       "\n",
       "     Registration  Year  log_price  \n",
       "0             yes  1991   8.342840  \n",
       "1             yes  1999   8.974618  \n",
       "2             yes  2003   9.495519  \n",
       "3             yes  2007  10.043249  \n",
       "4             yes  2011   9.814656  \n",
       "5             yes  2006   9.560997  \n",
       "6             yes  2012   9.287209  \n",
       "7              no  1999   7.244228  \n",
       "8             yes  2011   9.388487  \n",
       "9             yes  1994   7.824046  \n",
       "10            yes  2003   9.159047  \n",
       "11            yes  2008   9.259131  \n",
       "12            yes  2001   9.680344  \n",
       "13            yes  2012   9.059517  \n",
       "14             no  2001   8.003029  \n",
       "15            yes  2013  10.184900  \n",
       "16             no  1998   8.160518  \n",
       "17            yes  2016  10.551454  \n",
       "18            yes  2006   8.922658  \n",
       "19            yes  1998   8.824678  \n",
       "20            yes  2006   9.259131  \n",
       "21            yes  2008  10.122623  \n",
       "22            yes  2008   9.942708  \n",
       "23            yes  1999   8.779557  \n",
       "24             no  2011   9.515322  \n",
       "25            yes  2010   9.047821  \n",
       "26            yes  1989   7.972466  \n",
       "27            yes  2007   9.975808  \n",
       "28            yes  2008   9.792556  \n",
       "29            yes  2008  10.257659  \n",
       "...           ...   ...        ...  \n",
       "3837          yes  2011   9.350102  \n",
       "3838          yes  2008   9.179881  \n",
       "3839          yes  2011   9.259131  \n",
       "3840          yes  2012   9.296518  \n",
       "3841          yes  2008  10.146434  \n",
       "3842          yes  2013   9.648595  \n",
       "3843          yes  2009   9.185023  \n",
       "3844          yes  2004   9.686575  \n",
       "3845          yes  2008   9.126959  \n",
       "3846          yes  2005   8.546752  \n",
       "3847          yes  2016  11.512925  \n",
       "3848          yes  2012   9.104869  \n",
       "3849          yes  2008   9.729134  \n",
       "3850          yes  1997   9.011889  \n",
       "3851          yes  2013  10.124629  \n",
       "3852          yes  2016  11.302192  \n",
       "3853          yes  2003   8.895630  \n",
       "3854          yes  2008   9.968104  \n",
       "3855          yes  2011  10.714418  \n",
       "3856          yes  2012   8.817298  \n",
       "3857          yes  2005   8.853665  \n",
       "3858          yes  2010   9.400134  \n",
       "3859          yes  2013  10.236382  \n",
       "3860           no  2001   7.649693  \n",
       "3861          yes  2007   8.824678  \n",
       "3862          yes  2008   9.350102  \n",
       "3863          yes  2014   9.792556  \n",
       "3864          yes  2014  11.736069  \n",
       "3865          yes  1999   8.779557  \n",
       "3866          yes  2013   9.510445  \n",
       "\n",
       "[3867 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's transform 'Price' with a log transformation\n",
    "log_price = np.log(data_cleaned['Price'])\n",
    "\n",
    "# Then we add it to our data frame\n",
    "data_cleaned['log_price'] = log_price\n",
    "data_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b89959bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check the three scatters once again\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\n",
    "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
    "ax1.set_title('Log Price and Year')\n",
    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
    "ax2.set_title('Log Price and EngineV')\n",
    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
    "ax3.set_title('Log Price and Mileage')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# The relationships show a clear linear relationship\n",
    "# This is some good linear regression material\n",
    "\n",
    "# Alternatively we could have transformed each of the independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we will be using the log price variable, we can drop the old 'Price' one\n",
    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
       "       'Registration', 'Year', 'log_price'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's quickly see the columns of our data frame\n",
    "data_cleaned.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn does not have a built-in way to check for multicollinearity\n",
    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
    "# surely it is an issue nonetheless, thus we will try to deal with it\n",
    "\n",
    "# Here's the relevant module\n",
    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# To make this as easy as possible to use, we declare a variable where we put\n",
    "# all features where we want to check for multicollinearity\n",
    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
    "\n",
    "# we create a new data frame which will include all the VIFs\n",
    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "# Finally, I like to include names so it is easier to explore the result\n",
    "vif[\"Features\"] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.791584</td>\n",
       "      <td>Mileage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.354854</td>\n",
       "      <td>Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.662068</td>\n",
       "      <td>EngineV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF Features\n",
       "0   3.791584  Mileage\n",
       "1  10.354854     Year\n",
       "2   7.662068  EngineV"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's explore the result\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since Year has the highest VIF, I will remove it from the model\n",
    "# This will drive the VIF of other variables down!!! \n",
    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To include the categorical data in the regression, let's create dummies\n",
    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>log_price</th>\n",
       "      <th>Brand_BMW</th>\n",
       "      <th>Brand_Mercedes-Benz</th>\n",
       "      <th>Brand_Mitsubishi</th>\n",
       "      <th>Brand_Renault</th>\n",
       "      <th>Brand_Toyota</th>\n",
       "      <th>Brand_Volkswagen</th>\n",
       "      <th>Body_hatch</th>\n",
       "      <th>Body_other</th>\n",
       "      <th>Body_sedan</th>\n",
       "      <th>Body_vagon</th>\n",
       "      <th>Body_van</th>\n",
       "      <th>Engine Type_Gas</th>\n",
       "      <th>Engine Type_Other</th>\n",
       "      <th>Engine Type_Petrol</th>\n",
       "      <th>Registration_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.342840</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>8.974618</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.495519</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.043249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.814656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0      277      2.0   8.342840          1                    0   \n",
       "1      427      2.9   8.974618          0                    1   \n",
       "2      358      5.0   9.495519          0                    1   \n",
       "3      240      4.2  10.043249          0                    0   \n",
       "4      120      2.0   9.814656          0                    0   \n",
       "\n",
       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
       "0                 0              0             0                 0   \n",
       "1                 0              0             0                 0   \n",
       "2                 0              0             0                 0   \n",
       "3                 0              0             0                 0   \n",
       "4                 0              0             1                 0   \n",
       "\n",
       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
       "0           0           0           1           0         0                0   \n",
       "1           0           0           0           0         1                0   \n",
       "2           0           0           1           0         0                1   \n",
       "3           0           0           0           0         0                0   \n",
       "4           0           0           0           0         0                0   \n",
       "\n",
       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                  0                   1                 1  \n",
       "1                  0                   0                 1  \n",
       "2                  0                   0                 1  \n",
       "3                  0                   1                 1  \n",
       "4                  0                   1                 1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's the result\n",
    "data_with_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rearrange a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
       "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
       "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
       "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
       "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To make our data frame more organized, we prefer to place the dependent variable in the beginning of the df\n",
    "# Since each problem is different, that must be done manually\n",
    "# We can display all possible features and then choose the desired order\n",
    "data_with_dummies.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make the code a bit more parametrized, let's declare a new variable that will contain the preferred order\n",
    "# If you want a different order, just specify it here\n",
    "# Conventionally, the most intuitive order is: dependent variable, indepedendent numerical variables, dummies\n",
    "cols = ['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>log_price</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Brand_BMW</th>\n",
       "      <th>Brand_Mercedes-Benz</th>\n",
       "      <th>Brand_Mitsubishi</th>\n",
       "      <th>Brand_Renault</th>\n",
       "      <th>Brand_Toyota</th>\n",
       "      <th>Brand_Volkswagen</th>\n",
       "      <th>Body_hatch</th>\n",
       "      <th>Body_other</th>\n",
       "      <th>Body_sedan</th>\n",
       "      <th>Body_vagon</th>\n",
       "      <th>Body_van</th>\n",
       "      <th>Engine Type_Gas</th>\n",
       "      <th>Engine Type_Other</th>\n",
       "      <th>Engine Type_Petrol</th>\n",
       "      <th>Registration_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.342840</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.974618</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.495519</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.043249</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.814656</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0   8.342840      277      2.0          1                    0   \n",
       "1   8.974618      427      2.9          0                    1   \n",
       "2   9.495519      358      5.0          0                    1   \n",
       "3  10.043249      240      4.2          0                    0   \n",
       "4   9.814656      120      2.0          0                    0   \n",
       "\n",
       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
       "0                 0              0             0                 0   \n",
       "1                 0              0             0                 0   \n",
       "2                 0              0             0                 0   \n",
       "3                 0              0             0                 0   \n",
       "4                 0              0             1                 0   \n",
       "\n",
       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
       "0           0           0           1           0         0                0   \n",
       "1           0           0           0           0         1                0   \n",
       "2           0           0           1           0         0                1   \n",
       "3           0           0           0           0         0                0   \n",
       "4           0           0           0           0         0                0   \n",
       "\n",
       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                  0                   1                 1  \n",
       "1                  0                   0                 1  \n",
       "2                  0                   0                 1  \n",
       "3                  0                   1                 1  \n",
       "4                  0                   1                 1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To implement the reordering, we will create a new df, which is equal to the old one but with the new order of features\n",
    "data_preprocessed = data_with_dummies[cols]\n",
    "data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Declare the inputs and the targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The target(s) (dependent variable) is 'log price'\n",
    "targets = data_preprocessed['log_price']\n",
    "\n",
    "# The inputs are everything BUT the dependent variable, so we can simply drop it\n",
    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the scaling module\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a scaler object\n",
    "scaler = StandardScaler()\n",
    "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
    "scaler.fit(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
    "inputs_scaled = scaler.transform(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the module for the split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the variables with an 80-20 split and some random state\n",
    "# To have the same split as mine, use random_state = 365\n",
    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a linear regression object\n",
    "reg = LinearRegression()\n",
    "# Fit the regression with the scaled TRAIN inputs and targets\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's check the outputs of the regression\n",
    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
    "y_hat = reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8983e8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
    "# The closer the points to the 45-degree line, the better the prediction\n",
    "plt.scatter(y_train, y_hat)\n",
    "# Let's also name the axes\n",
    "plt.xlabel('Targets (y_train)',size=18)\n",
    "plt.ylabel('Predictions (y_hat)',size=18)\n",
    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
    "# We want the x-axis and the y-axis to be the same\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Residuals PDF')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b8996b8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Another useful check of our model is a residual plot\n",
    "# We can plot the PDF of the residuals and check for anomalies\n",
    "sns.distplot(y_train - y_hat)\n",
    "\n",
    "# Include a title\n",
    "plt.title(\"Residuals PDF\", size=18)\n",
    "\n",
    "# In the best case scenario this plot should be normally distributed\n",
    "# In our case we notice that there are many negative residuals (far away from the mean)\n",
    "# Given the definition of the residuals (y_train - y_hat), negative values imply\n",
    "# that y_hat (predictions) are much higher than y_train (the targets)\n",
    "# This is food for thought to improve our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.744996578792662"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the R-squared of the model\n",
    "reg.score(x_train,y_train)\n",
    "\n",
    "# Note that this is NOT the adjusted R-squared\n",
    "# in other words... find the Adjusted R-squared to have the appropriate measure :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the weights and bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.415239458021299"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the bias (intercept) of the regression\n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
       "       -0.14690868,  0.32047333])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the weights (coefficients) of the regression\n",
    "reg.coef_\n",
    "\n",
    "# Note that they are barely interpretable if at all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mileage</td>\n",
       "      <td>-0.448713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EngineV</td>\n",
       "      <td>0.209035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brand_BMW</td>\n",
       "      <td>0.014250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brand_Mercedes-Benz</td>\n",
       "      <td>0.012882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brand_Mitsubishi</td>\n",
       "      <td>-0.140552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brand_Renault</td>\n",
       "      <td>-0.179909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brand_Toyota</td>\n",
       "      <td>-0.060550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brand_Volkswagen</td>\n",
       "      <td>-0.089924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Body_hatch</td>\n",
       "      <td>-0.145469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Body_other</td>\n",
       "      <td>-0.101444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Body_sedan</td>\n",
       "      <td>-0.200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body_vagon</td>\n",
       "      <td>-0.129887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Body_van</td>\n",
       "      <td>-0.168597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Engine Type_Gas</td>\n",
       "      <td>-0.121490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Engine Type_Other</td>\n",
       "      <td>-0.033368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Engine Type_Petrol</td>\n",
       "      <td>-0.146909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Registration_yes</td>\n",
       "      <td>0.320473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Features   Weights\n",
       "0               Mileage -0.448713\n",
       "1               EngineV  0.209035\n",
       "2             Brand_BMW  0.014250\n",
       "3   Brand_Mercedes-Benz  0.012882\n",
       "4      Brand_Mitsubishi -0.140552\n",
       "5         Brand_Renault -0.179909\n",
       "6          Brand_Toyota -0.060550\n",
       "7      Brand_Volkswagen -0.089924\n",
       "8            Body_hatch -0.145469\n",
       "9            Body_other -0.101444\n",
       "10           Body_sedan -0.200630\n",
       "11           Body_vagon -0.129887\n",
       "12             Body_van -0.168597\n",
       "13      Engine Type_Gas -0.121490\n",
       "14    Engine Type_Other -0.033368\n",
       "15   Engine Type_Petrol -0.146909\n",
       "16     Registration_yes  0.320473"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a regression summary where we can compare them with one-another\n",
    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
    "reg_summary['Weights'] = reg.coef_\n",
    "reg_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
       "       'Mitsubishi'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the different categories in the 'Brand' variable\n",
    "data_cleaned['Brand'].unique()\n",
    "\n",
    "# In this way we can see which 'Brand' is actually the benchmark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
    "# Testing is done on a dataset that the algorithm has never seen\n",
    "# Luckily we have prepared such a dataset\n",
    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
    "# If the predictions are far off, we will know that our model overfitted\n",
    "y_hat_test = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b89971f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot with the test targets and the test predictions\n",
    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
    "plt.xlabel('Targets (y_test)',size=18)\n",
    "plt.ylabel('Predictions (y_hat_test)',size=18)\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.501696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.255242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.285218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.963017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.490075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Prediction\n",
       "0  10685.501696\n",
       "1   3499.255242\n",
       "2   7553.285218\n",
       "3   7463.963017\n",
       "4  11353.490075"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, let's manually check these predictions\n",
    "# To obtain the actual prices, we take the exponential of the log_price\n",
    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
    "df_pf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Prediction</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.501696</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.255242</td>\n",
       "      <td>7900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.285218</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.963017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.490075</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21289.799394</td>\n",
       "      <td>14200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20159.189144</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20349.617702</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11581.537864</td>\n",
       "      <td>11950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33614.617349</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7241.068243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5175.769541</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5484.015362</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13292.711243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8248.666686</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10621.836767</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23721.581637</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11770.636010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37600.146722</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16178.143307</td>\n",
       "      <td>6800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11876.820988</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31557.804999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6102.358118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13111.914144</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23650.150725</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45272.248411</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2178.941672</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2555.022542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35991.510539</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26062.229419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2379.583414</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>6421.180201</td>\n",
       "      <td>7777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>13355.106770</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>8453.281424</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>48699.979367</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6082.849234</td>\n",
       "      <td>4100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>10381.621436</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>8493.042746</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>8591.658845</td>\n",
       "      <td>13999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>6358.547301</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>17028.451182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>15885.658673</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>3752.540952</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>12028.905190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>9380.459827</td>\n",
       "      <td>16999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>10125.265176</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>13443.324968</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9097.127448</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>12201.288474</td>\n",
       "      <td>4700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>12383.352887</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>14049.760996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>11034.660068</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>18982.148845</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>24323.483753</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>38260.361723</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.726363</td>\n",
       "      <td>6950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.071179</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.446953</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.751303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.163043</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction   Target\n",
       "0    10685.501696      NaN\n",
       "1     3499.255242   7900.0\n",
       "2     7553.285218      NaN\n",
       "3     7463.963017      NaN\n",
       "4    11353.490075      NaN\n",
       "5    21289.799394  14200.0\n",
       "6    20159.189144      NaN\n",
       "7    20349.617702      NaN\n",
       "8    11581.537864  11950.0\n",
       "9    33614.617349      NaN\n",
       "10    7241.068243      NaN\n",
       "11    5175.769541  10500.0\n",
       "12    5484.015362      NaN\n",
       "13   13292.711243      NaN\n",
       "14    8248.666686      NaN\n",
       "15   10621.836767      NaN\n",
       "16   23721.581637   3500.0\n",
       "17   11770.636010      NaN\n",
       "18   37600.146722   7500.0\n",
       "19   16178.143307   6800.0\n",
       "20   11876.820988      NaN\n",
       "21   31557.804999      NaN\n",
       "22    6102.358118      NaN\n",
       "23   13111.914144      NaN\n",
       "24   23650.150725      NaN\n",
       "25   45272.248411      NaN\n",
       "26    2178.941672      NaN\n",
       "27    2555.022542      NaN\n",
       "28   35991.510539      NaN\n",
       "29   26062.229419      NaN\n",
       "..            ...      ...\n",
       "744   2379.583414      NaN\n",
       "745   6421.180201   7777.0\n",
       "746  13355.106770      NaN\n",
       "747   8453.281424  10500.0\n",
       "748  48699.979367      NaN\n",
       "749   6082.849234   4100.0\n",
       "750  10381.621436      NaN\n",
       "751   8493.042746      NaN\n",
       "752   8591.658845  13999.0\n",
       "753   6358.547301      NaN\n",
       "754  17028.451182      NaN\n",
       "755  15885.658673      NaN\n",
       "756   3752.540952      NaN\n",
       "757  12028.905190      NaN\n",
       "758   9380.459827  16999.0\n",
       "759  10125.265176      NaN\n",
       "760  13443.324968      NaN\n",
       "761   9097.127448      NaN\n",
       "762  12201.288474   4700.0\n",
       "763  12383.352887      NaN\n",
       "764  14049.760996      NaN\n",
       "765  11034.660068   3750.0\n",
       "766  18982.148845      NaN\n",
       "767  24323.483753      NaN\n",
       "768  38260.361723      NaN\n",
       "769  29651.726363   6950.0\n",
       "770  10732.071179      NaN\n",
       "771  13922.446953      NaN\n",
       "772  27487.751303      NaN\n",
       "773  13491.163043      NaN\n",
       "\n",
       "[774 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf\n",
    "\n",
    "# Note that we have a lot of missing values\n",
    "# There is no reason to have ANY missing values, though\n",
    "# This suggests that something is wrong with the data frame / indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.740664\n",
       "1    7.937375\n",
       "2    7.824046\n",
       "3    8.764053\n",
       "4    9.121509\n",
       "Name: log_price, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After displaying y_test, we find what the issue is\n",
    "# The old indexes are preserved (recall earlier in that code we made a note on that)\n",
    "# The code was: data_cleaned = data_4.reset_index(drop=True)\n",
    "\n",
    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
    "y_test = y_test.reset_index(drop=True)\n",
    "\n",
    "# Check the result\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.501696</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.255242</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.285218</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.963017</td>\n",
       "      <td>6400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.490075</td>\n",
       "      <td>9150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21289.799394</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20159.189144</td>\n",
       "      <td>38888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20349.617702</td>\n",
       "      <td>16999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11581.537864</td>\n",
       "      <td>12500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33614.617349</td>\n",
       "      <td>41000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7241.068243</td>\n",
       "      <td>12800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5175.769541</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5484.015362</td>\n",
       "      <td>7900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13292.711243</td>\n",
       "      <td>16999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8248.666686</td>\n",
       "      <td>9200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10621.836767</td>\n",
       "      <td>11999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23721.581637</td>\n",
       "      <td>20500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11770.636010</td>\n",
       "      <td>9700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37600.146722</td>\n",
       "      <td>39900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16178.143307</td>\n",
       "      <td>16400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11876.820988</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31557.804999</td>\n",
       "      <td>24500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6102.358118</td>\n",
       "      <td>5650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13111.914144</td>\n",
       "      <td>12900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23650.150725</td>\n",
       "      <td>20900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45272.248411</td>\n",
       "      <td>31990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2178.941672</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2555.022542</td>\n",
       "      <td>11600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35991.510539</td>\n",
       "      <td>43999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26062.229419</td>\n",
       "      <td>42500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2379.583414</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>6421.180201</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>13355.106770</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>8453.281424</td>\n",
       "      <td>10900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>48699.979367</td>\n",
       "      <td>77500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6082.849234</td>\n",
       "      <td>7450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>10381.621436</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>8493.042746</td>\n",
       "      <td>12800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>8591.658845</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>6358.547301</td>\n",
       "      <td>4850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>17028.451182</td>\n",
       "      <td>18700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>15885.658673</td>\n",
       "      <td>17300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>3752.540952</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>12028.905190</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>9380.459827</td>\n",
       "      <td>7950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>10125.265176</td>\n",
       "      <td>6700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>13443.324968</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9097.127448</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>12201.288474</td>\n",
       "      <td>12999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>12383.352887</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>14049.760996</td>\n",
       "      <td>10700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>11034.660068</td>\n",
       "      <td>9800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>18982.148845</td>\n",
       "      <td>17900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>24323.483753</td>\n",
       "      <td>18800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>38260.361723</td>\n",
       "      <td>75555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.726363</td>\n",
       "      <td>29500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.071179</td>\n",
       "      <td>9600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.446953</td>\n",
       "      <td>18300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.751303</td>\n",
       "      <td>68500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.163043</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction   Target\n",
       "0    10685.501696   2300.0\n",
       "1     3499.255242   2800.0\n",
       "2     7553.285218   2500.0\n",
       "3     7463.963017   6400.0\n",
       "4    11353.490075   9150.0\n",
       "5    21289.799394  20000.0\n",
       "6    20159.189144  38888.0\n",
       "7    20349.617702  16999.0\n",
       "8    11581.537864  12500.0\n",
       "9    33614.617349  41000.0\n",
       "10    7241.068243  12800.0\n",
       "11    5175.769541   5000.0\n",
       "12    5484.015362   7900.0\n",
       "13   13292.711243  16999.0\n",
       "14    8248.666686   9200.0\n",
       "15   10621.836767  11999.0\n",
       "16   23721.581637  20500.0\n",
       "17   11770.636010   9700.0\n",
       "18   37600.146722  39900.0\n",
       "19   16178.143307  16400.0\n",
       "20   11876.820988  15200.0\n",
       "21   31557.804999  24500.0\n",
       "22    6102.358118   5650.0\n",
       "23   13111.914144  12900.0\n",
       "24   23650.150725  20900.0\n",
       "25   45272.248411  31990.0\n",
       "26    2178.941672   3600.0\n",
       "27    2555.022542  11600.0\n",
       "28   35991.510539  43999.0\n",
       "29   26062.229419  42500.0\n",
       "..            ...      ...\n",
       "744   2379.583414   3000.0\n",
       "745   6421.180201   4400.0\n",
       "746  13355.106770   7500.0\n",
       "747   8453.281424  10900.0\n",
       "748  48699.979367  77500.0\n",
       "749   6082.849234   7450.0\n",
       "750  10381.621436   3000.0\n",
       "751   8493.042746  12800.0\n",
       "752   8591.658845  12000.0\n",
       "753   6358.547301   4850.0\n",
       "754  17028.451182  18700.0\n",
       "755  15885.658673  17300.0\n",
       "756   3752.540952   2600.0\n",
       "757  12028.905190  10500.0\n",
       "758   9380.459827   7950.0\n",
       "759  10125.265176   6700.0\n",
       "760  13443.324968   9000.0\n",
       "761   9097.127448   8000.0\n",
       "762  12201.288474  12999.0\n",
       "763  12383.352887  10800.0\n",
       "764  14049.760996  10700.0\n",
       "765  11034.660068   9800.0\n",
       "766  18982.148845  17900.0\n",
       "767  24323.483753  18800.0\n",
       "768  38260.361723  75555.0\n",
       "769  29651.726363  29500.0\n",
       "770  10732.071179   9600.0\n",
       "771  13922.446953  18300.0\n",
       "772  27487.751303  68500.0\n",
       "773  13491.163043  10800.0\n",
       "\n",
       "[774 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's overwrite the 'Target' column with the appropriate values\n",
    "# Again, we need the exponential of the test log price\n",
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additionally, we can calculate the difference between the targets and the predictions\n",
    "# Note that this is actually the residual (we already plotted the residuals)\n",
    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']\n",
    "\n",
    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
    "# this comparison makes a lot of sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.501696</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>-8385.501696</td>\n",
       "      <td>364.587030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.255242</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>-699.255242</td>\n",
       "      <td>24.973402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.285218</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>-5053.285218</td>\n",
       "      <td>202.131409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.963017</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>-1063.963017</td>\n",
       "      <td>16.624422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.490075</td>\n",
       "      <td>9150.0</td>\n",
       "      <td>-2203.490075</td>\n",
       "      <td>24.081859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21289.799394</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>-1289.799394</td>\n",
       "      <td>6.448997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20159.189144</td>\n",
       "      <td>38888.0</td>\n",
       "      <td>18728.810856</td>\n",
       "      <td>48.160900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20349.617702</td>\n",
       "      <td>16999.0</td>\n",
       "      <td>-3350.617702</td>\n",
       "      <td>19.710675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11581.537864</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>918.462136</td>\n",
       "      <td>7.347697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33614.617349</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>7385.382651</td>\n",
       "      <td>18.013128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7241.068243</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>5558.931757</td>\n",
       "      <td>43.429154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5175.769541</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>-175.769541</td>\n",
       "      <td>3.515391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5484.015362</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>2415.984638</td>\n",
       "      <td>30.582084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13292.711243</td>\n",
       "      <td>16999.0</td>\n",
       "      <td>3706.288757</td>\n",
       "      <td>21.802981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8248.666686</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>951.333314</td>\n",
       "      <td>10.340580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10621.836767</td>\n",
       "      <td>11999.0</td>\n",
       "      <td>1377.163233</td>\n",
       "      <td>11.477317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23721.581637</td>\n",
       "      <td>20500.0</td>\n",
       "      <td>-3221.581637</td>\n",
       "      <td>15.715032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11770.636010</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>-2070.636010</td>\n",
       "      <td>21.346763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37600.146722</td>\n",
       "      <td>39900.0</td>\n",
       "      <td>2299.853278</td>\n",
       "      <td>5.764043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16178.143307</td>\n",
       "      <td>16400.0</td>\n",
       "      <td>221.856693</td>\n",
       "      <td>1.352785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11876.820988</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>3323.179012</td>\n",
       "      <td>21.863020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31557.804999</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>-7057.804999</td>\n",
       "      <td>28.807367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6102.358118</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>-452.358118</td>\n",
       "      <td>8.006338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13111.914144</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>-211.914144</td>\n",
       "      <td>1.642745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23650.150725</td>\n",
       "      <td>20900.0</td>\n",
       "      <td>-2750.150725</td>\n",
       "      <td>13.158616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45272.248411</td>\n",
       "      <td>31990.0</td>\n",
       "      <td>-13282.248411</td>\n",
       "      <td>41.520001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2178.941672</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>1421.058328</td>\n",
       "      <td>39.473842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2555.022542</td>\n",
       "      <td>11600.0</td>\n",
       "      <td>9044.977458</td>\n",
       "      <td>77.973944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35991.510539</td>\n",
       "      <td>43999.0</td>\n",
       "      <td>8007.489461</td>\n",
       "      <td>18.199253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26062.229419</td>\n",
       "      <td>42500.0</td>\n",
       "      <td>16437.770581</td>\n",
       "      <td>38.677107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2379.583414</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>620.416586</td>\n",
       "      <td>20.680553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>6421.180201</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>-2021.180201</td>\n",
       "      <td>45.935914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>13355.106770</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>-5855.106770</td>\n",
       "      <td>78.068090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>8453.281424</td>\n",
       "      <td>10900.0</td>\n",
       "      <td>2446.718576</td>\n",
       "      <td>22.446959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>48699.979367</td>\n",
       "      <td>77500.0</td>\n",
       "      <td>28800.020633</td>\n",
       "      <td>37.161317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6082.849234</td>\n",
       "      <td>7450.0</td>\n",
       "      <td>1367.150766</td>\n",
       "      <td>18.351017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>10381.621436</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>-7381.621436</td>\n",
       "      <td>246.054048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>8493.042746</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>4306.957254</td>\n",
       "      <td>33.648104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>8591.658845</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>3408.341155</td>\n",
       "      <td>28.402843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>6358.547301</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>-1508.547301</td>\n",
       "      <td>31.104068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>17028.451182</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>1671.548818</td>\n",
       "      <td>8.938764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>15885.658673</td>\n",
       "      <td>17300.0</td>\n",
       "      <td>1414.341327</td>\n",
       "      <td>8.175383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>3752.540952</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>-1152.540952</td>\n",
       "      <td>44.328498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>12028.905190</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>-1528.905190</td>\n",
       "      <td>14.561002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>9380.459827</td>\n",
       "      <td>7950.0</td>\n",
       "      <td>-1430.459827</td>\n",
       "      <td>17.993205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>10125.265176</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>-3425.265176</td>\n",
       "      <td>51.123361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>13443.324968</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>-4443.324968</td>\n",
       "      <td>49.370277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9097.127448</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>-1097.127448</td>\n",
       "      <td>13.714093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>12201.288474</td>\n",
       "      <td>12999.0</td>\n",
       "      <td>797.711526</td>\n",
       "      <td>6.136715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>12383.352887</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>-1583.352887</td>\n",
       "      <td>14.660675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>14049.760996</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>-3349.760996</td>\n",
       "      <td>31.306178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>11034.660068</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>-1234.660068</td>\n",
       "      <td>12.598572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>18982.148845</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>-1082.148845</td>\n",
       "      <td>6.045524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>24323.483753</td>\n",
       "      <td>18800.0</td>\n",
       "      <td>-5523.483753</td>\n",
       "      <td>29.380233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>38260.361723</td>\n",
       "      <td>75555.0</td>\n",
       "      <td>37294.638277</td>\n",
       "      <td>49.360914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.726363</td>\n",
       "      <td>29500.0</td>\n",
       "      <td>-151.726363</td>\n",
       "      <td>0.514327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.071179</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>-1132.071179</td>\n",
       "      <td>11.792408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.446953</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>4377.553047</td>\n",
       "      <td>23.921055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.751303</td>\n",
       "      <td>68500.0</td>\n",
       "      <td>41012.248697</td>\n",
       "      <td>59.871896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.163043</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>-2691.163043</td>\n",
       "      <td>24.918176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction   Target      Residual  Difference%\n",
       "0    10685.501696   2300.0  -8385.501696   364.587030\n",
       "1     3499.255242   2800.0   -699.255242    24.973402\n",
       "2     7553.285218   2500.0  -5053.285218   202.131409\n",
       "3     7463.963017   6400.0  -1063.963017    16.624422\n",
       "4    11353.490075   9150.0  -2203.490075    24.081859\n",
       "5    21289.799394  20000.0  -1289.799394     6.448997\n",
       "6    20159.189144  38888.0  18728.810856    48.160900\n",
       "7    20349.617702  16999.0  -3350.617702    19.710675\n",
       "8    11581.537864  12500.0    918.462136     7.347697\n",
       "9    33614.617349  41000.0   7385.382651    18.013128\n",
       "10    7241.068243  12800.0   5558.931757    43.429154\n",
       "11    5175.769541   5000.0   -175.769541     3.515391\n",
       "12    5484.015362   7900.0   2415.984638    30.582084\n",
       "13   13292.711243  16999.0   3706.288757    21.802981\n",
       "14    8248.666686   9200.0    951.333314    10.340580\n",
       "15   10621.836767  11999.0   1377.163233    11.477317\n",
       "16   23721.581637  20500.0  -3221.581637    15.715032\n",
       "17   11770.636010   9700.0  -2070.636010    21.346763\n",
       "18   37600.146722  39900.0   2299.853278     5.764043\n",
       "19   16178.143307  16400.0    221.856693     1.352785\n",
       "20   11876.820988  15200.0   3323.179012    21.863020\n",
       "21   31557.804999  24500.0  -7057.804999    28.807367\n",
       "22    6102.358118   5650.0   -452.358118     8.006338\n",
       "23   13111.914144  12900.0   -211.914144     1.642745\n",
       "24   23650.150725  20900.0  -2750.150725    13.158616\n",
       "25   45272.248411  31990.0 -13282.248411    41.520001\n",
       "26    2178.941672   3600.0   1421.058328    39.473842\n",
       "27    2555.022542  11600.0   9044.977458    77.973944\n",
       "28   35991.510539  43999.0   8007.489461    18.199253\n",
       "29   26062.229419  42500.0  16437.770581    38.677107\n",
       "..            ...      ...           ...          ...\n",
       "744   2379.583414   3000.0    620.416586    20.680553\n",
       "745   6421.180201   4400.0  -2021.180201    45.935914\n",
       "746  13355.106770   7500.0  -5855.106770    78.068090\n",
       "747   8453.281424  10900.0   2446.718576    22.446959\n",
       "748  48699.979367  77500.0  28800.020633    37.161317\n",
       "749   6082.849234   7450.0   1367.150766    18.351017\n",
       "750  10381.621436   3000.0  -7381.621436   246.054048\n",
       "751   8493.042746  12800.0   4306.957254    33.648104\n",
       "752   8591.658845  12000.0   3408.341155    28.402843\n",
       "753   6358.547301   4850.0  -1508.547301    31.104068\n",
       "754  17028.451182  18700.0   1671.548818     8.938764\n",
       "755  15885.658673  17300.0   1414.341327     8.175383\n",
       "756   3752.540952   2600.0  -1152.540952    44.328498\n",
       "757  12028.905190  10500.0  -1528.905190    14.561002\n",
       "758   9380.459827   7950.0  -1430.459827    17.993205\n",
       "759  10125.265176   6700.0  -3425.265176    51.123361\n",
       "760  13443.324968   9000.0  -4443.324968    49.370277\n",
       "761   9097.127448   8000.0  -1097.127448    13.714093\n",
       "762  12201.288474  12999.0    797.711526     6.136715\n",
       "763  12383.352887  10800.0  -1583.352887    14.660675\n",
       "764  14049.760996  10700.0  -3349.760996    31.306178\n",
       "765  11034.660068   9800.0  -1234.660068    12.598572\n",
       "766  18982.148845  17900.0  -1082.148845     6.045524\n",
       "767  24323.483753  18800.0  -5523.483753    29.380233\n",
       "768  38260.361723  75555.0  37294.638277    49.360914\n",
       "769  29651.726363  29500.0   -151.726363     0.514327\n",
       "770  10732.071179   9600.0  -1132.071179    11.792408\n",
       "771  13922.446953  18300.0   4377.553047    23.921055\n",
       "772  27487.751303  68500.0  41012.248697    59.871896\n",
       "773  13491.163043  10800.0  -2691.163043    24.918176\n",
       "\n",
       "[774 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>774.000000</td>\n",
       "      <td>774.000000</td>\n",
       "      <td>774.000000</td>\n",
       "      <td>774.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15946.760167</td>\n",
       "      <td>18165.817106</td>\n",
       "      <td>2219.056939</td>\n",
       "      <td>36.256693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13133.197604</td>\n",
       "      <td>19967.858908</td>\n",
       "      <td>10871.218143</td>\n",
       "      <td>55.066507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1320.562768</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>-29456.498331</td>\n",
       "      <td>0.062794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7413.644234</td>\n",
       "      <td>6900.000000</td>\n",
       "      <td>-2044.191251</td>\n",
       "      <td>12.108022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11568.168859</td>\n",
       "      <td>11600.000000</td>\n",
       "      <td>142.518577</td>\n",
       "      <td>23.467728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20162.408805</td>\n",
       "      <td>20500.000000</td>\n",
       "      <td>3147.343497</td>\n",
       "      <td>39.563570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77403.055224</td>\n",
       "      <td>126000.000000</td>\n",
       "      <td>85106.162329</td>\n",
       "      <td>512.688080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Prediction         Target      Residual  Difference%\n",
       "count    774.000000     774.000000    774.000000   774.000000\n",
       "mean   15946.760167   18165.817106   2219.056939    36.256693\n",
       "std    13133.197604   19967.858908  10871.218143    55.066507\n",
       "min     1320.562768    1200.000000 -29456.498331     0.062794\n",
       "25%     7413.644234    6900.000000  -2044.191251    12.108022\n",
       "50%    11568.168859   11600.000000    142.518577    23.467728\n",
       "75%    20162.408805   20500.000000   3147.343497    39.563570\n",
       "max    77403.055224  126000.000000  85106.162329   512.688080"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the descriptives here gives us additional insights\n",
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>30480.85</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>19.15</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>16960.31</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>38.69</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12469.21</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>30.79</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>25614.14</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>-114.14</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>42703.68</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>-203.68</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>3084.69</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>15.31</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.73</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>-151.73</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>9749.53</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>50.47</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>23118.07</td>\n",
       "      <td>22999.00</td>\n",
       "      <td>-119.07</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>8734.58</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>65.42</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>3473.79</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>26.21</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>21174.10</td>\n",
       "      <td>21335.00</td>\n",
       "      <td>160.90</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>8967.74</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-67.74</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>17858.02</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>141.98</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>17654.84</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>145.16</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>11391.95</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>108.05</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>28625.56</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>274.44</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>7724.17</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>75.83</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>6429.03</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>70.97</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>7597.39</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-97.39</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>18555.09</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>244.91</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7396.87</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-96.87</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16178.14</td>\n",
       "      <td>16400.00</td>\n",
       "      <td>221.86</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>12327.10</td>\n",
       "      <td>12499.00</td>\n",
       "      <td>171.90</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>51287.19</td>\n",
       "      <td>52055.25</td>\n",
       "      <td>768.06</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>6009.63</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>90.37</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4973.17</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-73.17</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>27716.14</td>\n",
       "      <td>27300.00</td>\n",
       "      <td>-416.14</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>28498.91</td>\n",
       "      <td>28950.00</td>\n",
       "      <td>451.09</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2953.17</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>46.83</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>26425.14</td>\n",
       "      <td>25999.00</td>\n",
       "      <td>-426.14</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13111.91</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>-211.91</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12858.08</td>\n",
       "      <td>12650.00</td>\n",
       "      <td>-208.08</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>13421.16</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>-221.16</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>7327.18</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-127.18</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>3816.33</td>\n",
       "      <td>3750.00</td>\n",
       "      <td>-66.33</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>9721.50</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>178.50</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>44173.72</td>\n",
       "      <td>44999.00</td>\n",
       "      <td>825.28</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>40753.58</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>-753.58</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>22262.80</td>\n",
       "      <td>22711.65</td>\n",
       "      <td>448.85</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>18231.44</td>\n",
       "      <td>18600.00</td>\n",
       "      <td>368.56</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>9596.94</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>-196.94</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>6067.79</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>132.21</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>35371.16</td>\n",
       "      <td>34600.00</td>\n",
       "      <td>-771.16</td>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>11967.39</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-267.39</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>17908.00</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-408.00</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>8707.13</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-207.13</td>\n",
       "      <td>2.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>29049.27</td>\n",
       "      <td>28350.00</td>\n",
       "      <td>-699.27</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>14513.46</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>386.54</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>20453.89</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>546.11</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>15383.35</td>\n",
       "      <td>14990.00</td>\n",
       "      <td>-393.35</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>24642.50</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-642.50</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>50099.92</td>\n",
       "      <td>51500.00</td>\n",
       "      <td>1400.08</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>16133.86</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>-433.86</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>17489.92</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>510.08</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1894.40</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>55.60</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>30810.25</td>\n",
       "      <td>29900.00</td>\n",
       "      <td>-910.25</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>8721.97</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>277.03</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>18866.50</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>-566.50</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5958.63</td>\n",
       "      <td>6150.00</td>\n",
       "      <td>191.37</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>5811.74</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>188.26</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>9800.43</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-300.43</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>7324.63</td>\n",
       "      <td>7100.00</td>\n",
       "      <td>-224.63</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>12583.52</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>416.48</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>10115.13</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-315.13</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>11156.38</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-356.38</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>9279.28</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>320.72</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>31369.37</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>1130.63</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2315.71</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>84.29</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5175.77</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-175.77</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>21611.83</td>\n",
       "      <td>22400.00</td>\n",
       "      <td>788.17</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7976.26</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-276.26</td>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>24874.86</td>\n",
       "      <td>23999.00</td>\n",
       "      <td>-875.86</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>36516.35</td>\n",
       "      <td>37900.00</td>\n",
       "      <td>1383.65</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>4666.05</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-166.05</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>18672.68</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>-672.68</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>14815.83</td>\n",
       "      <td>15400.00</td>\n",
       "      <td>584.17</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>10581.62</td>\n",
       "      <td>10999.00</td>\n",
       "      <td>417.38</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>12663.54</td>\n",
       "      <td>12200.00</td>\n",
       "      <td>-463.54</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>1825.44</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>74.56</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>12247.90</td>\n",
       "      <td>12750.00</td>\n",
       "      <td>502.10</td>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>5930.73</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>-230.73</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>9782.47</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>417.53</td>\n",
       "      <td>4.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>12452.22</td>\n",
       "      <td>11960.00</td>\n",
       "      <td>-492.22</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>23163.87</td>\n",
       "      <td>24171.42</td>\n",
       "      <td>1007.55</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>3246.94</td>\n",
       "      <td>3390.00</td>\n",
       "      <td>143.06</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>12354.16</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>545.84</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>33499.44</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>1500.56</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>16257.03</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>741.97</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>8415.81</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>384.19</td>\n",
       "      <td>4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>12318.39</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>581.61</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2765.14</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>134.86</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>11420.95</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>578.05</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2725.40</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-125.40</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>23624.69</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>-1124.69</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11377.84</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>621.16</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>12096.18</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-596.18</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>9363.27</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-463.27</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>12311.47</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-611.47</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>14218.94</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>-718.94</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>8431.89</td>\n",
       "      <td>7999.00</td>\n",
       "      <td>-432.89</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37600.15</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>2299.85</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>39882.67</td>\n",
       "      <td>37700.00</td>\n",
       "      <td>-2182.67</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>20588.57</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>1311.43</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>6861.84</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>438.16</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>18982.15</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-1082.15</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>15067.85</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>-867.85</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>12201.29</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>797.71</td>\n",
       "      <td>6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>11147.91</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-647.91</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>20183.30</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>-1184.30</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>17334.11</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>1165.89</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21289.80</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-1289.80</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>6826.78</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>473.22</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>4472.47</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-272.47</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>53294.61</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>3705.39</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>6658.73</td>\n",
       "      <td>6250.00</td>\n",
       "      <td>-408.73</td>\n",
       "      <td>6.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>19001.29</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>-1201.29</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>4590.49</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-290.49</td>\n",
       "      <td>6.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>43236.92</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>-2736.92</td>\n",
       "      <td>6.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>9076.42</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-576.42</td>\n",
       "      <td>6.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>10159.18</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>740.82</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>16027.02</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-1027.02</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>13502.35</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>997.65</td>\n",
       "      <td>6.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>6413.26</td>\n",
       "      <td>5999.00</td>\n",
       "      <td>-414.26</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>49221.63</td>\n",
       "      <td>52999.00</td>\n",
       "      <td>3777.37</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>21816.43</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>1683.57</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>11969.66</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>930.34</td>\n",
       "      <td>7.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>8263.95</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-563.95</td>\n",
       "      <td>7.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11581.54</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>918.46</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>6977.90</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-477.90</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>6978.91</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-478.91</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>16392.22</td>\n",
       "      <td>17700.00</td>\n",
       "      <td>1307.78</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>15742.41</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>1256.59</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>7869.47</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>630.53</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>21297.65</td>\n",
       "      <td>19800.00</td>\n",
       "      <td>-1497.65</td>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>6893.62</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-493.62</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>15627.03</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>-1127.03</td>\n",
       "      <td>7.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>3411.53</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>288.47</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>11336.71</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>963.29</td>\n",
       "      <td>7.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>8195.34</td>\n",
       "      <td>7600.00</td>\n",
       "      <td>-595.34</td>\n",
       "      <td>7.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>16717.08</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>-1217.08</td>\n",
       "      <td>7.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>7772.57</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-572.57</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>16197.42</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-1197.42</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6102.36</td>\n",
       "      <td>5650.00</td>\n",
       "      <td>-452.36</td>\n",
       "      <td>8.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>4488.43</td>\n",
       "      <td>4150.00</td>\n",
       "      <td>-338.43</td>\n",
       "      <td>8.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>15885.66</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>1414.34</td>\n",
       "      <td>8.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>17983.10</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>1616.90</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>7045.72</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-545.72</td>\n",
       "      <td>8.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>3795.06</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-295.06</td>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>6130.96</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>569.04</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>5947.51</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>552.49</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>7609.28</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-609.28</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>8117.44</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>782.56</td>\n",
       "      <td>8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>11098.18</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-898.18</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>17028.45</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>1671.55</td>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>19847.25</td>\n",
       "      <td>18200.00</td>\n",
       "      <td>-1647.25</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>5454.34</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-454.34</td>\n",
       "      <td>9.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>20662.46</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-1762.46</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>11696.25</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>1203.75</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>10716.84</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-916.84</td>\n",
       "      <td>9.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>13459.92</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>-1159.92</td>\n",
       "      <td>9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>8767.54</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>932.46</td>\n",
       "      <td>9.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>23272.83</td>\n",
       "      <td>25749.75</td>\n",
       "      <td>2476.92</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>5484.97</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-485.97</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>9495.62</td>\n",
       "      <td>8650.00</td>\n",
       "      <td>-845.62</td>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>11858.48</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-1058.48</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>48535.50</td>\n",
       "      <td>-4777.31</td>\n",
       "      <td>9.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>47047.70</td>\n",
       "      <td>52300.00</td>\n",
       "      <td>5252.30</td>\n",
       "      <td>10.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6717.17</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>-617.17</td>\n",
       "      <td>10.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>5391.77</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>608.23</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>17591.09</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>2008.91</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>17591.09</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>2008.91</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>25576.58</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>2923.42</td>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>20367.25</td>\n",
       "      <td>22700.00</td>\n",
       "      <td>2332.75</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>7267.36</td>\n",
       "      <td>8100.00</td>\n",
       "      <td>832.64</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>1792.36</td>\n",
       "      <td>1999.00</td>\n",
       "      <td>206.64</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8248.67</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>951.33</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>5643.45</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>656.55</td>\n",
       "      <td>10.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6839.57</td>\n",
       "      <td>7650.00</td>\n",
       "      <td>810.43</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>41503.90</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>-4003.90</td>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>9310.69</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>-910.69</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>35662.26</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>4337.74</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>16407.29</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>-1607.29</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>21303.57</td>\n",
       "      <td>23900.00</td>\n",
       "      <td>2596.43</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>15872.91</td>\n",
       "      <td>14300.00</td>\n",
       "      <td>-1572.91</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>10332.54</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-1032.54</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>18575.43</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>2324.57</td>\n",
       "      <td>11.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>8882.65</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>1117.35</td>\n",
       "      <td>11.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>7797.18</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-798.18</td>\n",
       "      <td>11.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10621.84</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>1377.16</td>\n",
       "      <td>11.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.07</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-1132.07</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>2381.01</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>318.99</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>11200.20</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-1201.20</td>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>2373.66</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>326.34</td>\n",
       "      <td>12.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>10696.56</td>\n",
       "      <td>12179.00</td>\n",
       "      <td>1482.44</td>\n",
       "      <td>12.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>3590.97</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-390.97</td>\n",
       "      <td>12.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>53063.22</td>\n",
       "      <td>60500.00</td>\n",
       "      <td>7436.78</td>\n",
       "      <td>12.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>7300.05</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-800.05</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>6392.70</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>907.30</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6746.43</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-746.43</td>\n",
       "      <td>12.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>9192.06</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>1307.94</td>\n",
       "      <td>12.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>11034.66</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-1234.66</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>16939.86</td>\n",
       "      <td>19400.00</td>\n",
       "      <td>2460.14</td>\n",
       "      <td>12.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2479.43</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-279.43</td>\n",
       "      <td>12.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>9593.52</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>1406.48</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>23967.81</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>3532.19</td>\n",
       "      <td>12.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>7491.01</td>\n",
       "      <td>8600.00</td>\n",
       "      <td>1108.99</td>\n",
       "      <td>12.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>8242.62</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-942.62</td>\n",
       "      <td>12.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>9489.56</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1410.44</td>\n",
       "      <td>12.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>44312.98</td>\n",
       "      <td>50900.00</td>\n",
       "      <td>6587.02</td>\n",
       "      <td>12.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>9829.53</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-1129.53</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>9269.68</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>-1069.68</td>\n",
       "      <td>13.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>4524.77</td>\n",
       "      <td>3999.00</td>\n",
       "      <td>-525.77</td>\n",
       "      <td>13.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23650.15</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-2750.15</td>\n",
       "      <td>13.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>8850.21</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>1349.79</td>\n",
       "      <td>13.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>4770.52</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>729.48</td>\n",
       "      <td>13.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>29479.97</td>\n",
       "      <td>34000.00</td>\n",
       "      <td>4520.03</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>8316.92</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>1283.08</td>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>24974.57</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>3925.43</td>\n",
       "      <td>13.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>14943.75</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>2356.25</td>\n",
       "      <td>13.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>28498.91</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>4501.09</td>\n",
       "      <td>13.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>14591.35</td>\n",
       "      <td>16900.00</td>\n",
       "      <td>2308.65</td>\n",
       "      <td>13.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>10358.58</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>1641.42</td>\n",
       "      <td>13.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>13074.74</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-1574.74</td>\n",
       "      <td>13.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9097.13</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-1097.13</td>\n",
       "      <td>13.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>15010.51</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>-1810.51</td>\n",
       "      <td>13.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>12593.00</td>\n",
       "      <td>14600.00</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>20246.04</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>3253.96</td>\n",
       "      <td>13.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>8768.29</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-1068.29</td>\n",
       "      <td>13.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>39744.80</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>-4844.80</td>\n",
       "      <td>13.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>9909.62</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-1209.62</td>\n",
       "      <td>13.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>44680.77</td>\n",
       "      <td>51900.00</td>\n",
       "      <td>7219.23</td>\n",
       "      <td>13.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>21942.05</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>3557.95</td>\n",
       "      <td>13.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>12900.31</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>2099.69</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>18128.32</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>-2228.32</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>4296.77</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>703.23</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>6006.27</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>992.73</td>\n",
       "      <td>14.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>1972.28</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>327.72</td>\n",
       "      <td>14.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>8236.36</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-1036.36</td>\n",
       "      <td>14.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>10612.51</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>1787.49</td>\n",
       "      <td>14.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>14197.26</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>-1797.26</td>\n",
       "      <td>14.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>47013.28</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>7986.72</td>\n",
       "      <td>14.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>12028.91</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-1528.91</td>\n",
       "      <td>14.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3666.31</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-466.31</td>\n",
       "      <td>14.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2860.97</td>\n",
       "      <td>3350.00</td>\n",
       "      <td>489.03</td>\n",
       "      <td>14.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>7256.86</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>1243.14</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10499.60</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>1800.40</td>\n",
       "      <td>14.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>12383.35</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-1583.35</td>\n",
       "      <td>14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>11937.31</td>\n",
       "      <td>13995.00</td>\n",
       "      <td>2057.69</td>\n",
       "      <td>14.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2810.71</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-360.71</td>\n",
       "      <td>14.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>7664.32</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>1335.68</td>\n",
       "      <td>14.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>8041.62</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-1042.62</td>\n",
       "      <td>14.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>13334.19</td>\n",
       "      <td>11600.00</td>\n",
       "      <td>-1734.19</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>13680.48</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>-1780.48</td>\n",
       "      <td>14.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>13500.69</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>2399.31</td>\n",
       "      <td>15.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>11638.89</td>\n",
       "      <td>13708.50</td>\n",
       "      <td>2069.61</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>15700.53</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>2799.47</td>\n",
       "      <td>15.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>11425.01</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>2074.99</td>\n",
       "      <td>15.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>63000.00</td>\n",
       "      <td>9687.19</td>\n",
       "      <td>15.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>6599.62</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>1200.38</td>\n",
       "      <td>15.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>61639.73</td>\n",
       "      <td>72900.00</td>\n",
       "      <td>11260.27</td>\n",
       "      <td>15.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>7098.53</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>1301.47</td>\n",
       "      <td>15.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>10889.39</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>2010.61</td>\n",
       "      <td>15.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2613.97</td>\n",
       "      <td>3099.00</td>\n",
       "      <td>485.03</td>\n",
       "      <td>15.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23721.58</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-3221.58</td>\n",
       "      <td>15.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>10133.68</td>\n",
       "      <td>8750.00</td>\n",
       "      <td>-1383.68</td>\n",
       "      <td>15.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>22261.50</td>\n",
       "      <td>19200.00</td>\n",
       "      <td>-3061.50</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>10448.50</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-1449.50</td>\n",
       "      <td>16.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>24271.90</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-3371.90</td>\n",
       "      <td>16.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>14926.69</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>2873.31</td>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>11619.16</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-1619.16</td>\n",
       "      <td>16.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>46465.02</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>-6565.02</td>\n",
       "      <td>16.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>50954.68</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>10045.32</td>\n",
       "      <td>16.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>6848.88</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>1351.12</td>\n",
       "      <td>16.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>16547.03</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>-2347.03</td>\n",
       "      <td>16.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>4590.16</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>909.84</td>\n",
       "      <td>16.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>13348.91</td>\n",
       "      <td>15999.00</td>\n",
       "      <td>2650.09</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>11191.05</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-1591.05</td>\n",
       "      <td>16.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2126.49</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>423.51</td>\n",
       "      <td>16.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.96</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-1063.96</td>\n",
       "      <td>16.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2749.08</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>550.92</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>18282.75</td>\n",
       "      <td>21950.00</td>\n",
       "      <td>3667.25</td>\n",
       "      <td>16.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>19071.43</td>\n",
       "      <td>22900.00</td>\n",
       "      <td>3828.57</td>\n",
       "      <td>16.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>32886.86</td>\n",
       "      <td>39500.00</td>\n",
       "      <td>6613.14</td>\n",
       "      <td>16.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>25752.19</td>\n",
       "      <td>30990.00</td>\n",
       "      <td>5237.81</td>\n",
       "      <td>16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>7719.69</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>1580.31</td>\n",
       "      <td>16.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>25986.31</td>\n",
       "      <td>31310.00</td>\n",
       "      <td>5323.69</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>8250.47</td>\n",
       "      <td>9950.00</td>\n",
       "      <td>1699.53</td>\n",
       "      <td>17.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>28937.50</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>5962.50</td>\n",
       "      <td>17.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>8392.42</td>\n",
       "      <td>7150.00</td>\n",
       "      <td>-1242.42</td>\n",
       "      <td>17.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>8033.61</td>\n",
       "      <td>9750.00</td>\n",
       "      <td>1716.39</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>8156.11</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>1743.89</td>\n",
       "      <td>17.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>2470.61</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>529.39</td>\n",
       "      <td>17.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>19659.97</td>\n",
       "      <td>16700.00</td>\n",
       "      <td>-2959.97</td>\n",
       "      <td>17.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>17540.72</td>\n",
       "      <td>14899.00</td>\n",
       "      <td>-2641.72</td>\n",
       "      <td>17.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>7814.16</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>1685.84</td>\n",
       "      <td>17.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>21097.66</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-3197.66</td>\n",
       "      <td>17.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>4266.27</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>933.73</td>\n",
       "      <td>17.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>9380.46</td>\n",
       "      <td>7950.00</td>\n",
       "      <td>-1430.46</td>\n",
       "      <td>17.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>33614.62</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>7385.38</td>\n",
       "      <td>18.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33614.62</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>7385.38</td>\n",
       "      <td>18.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>9089.35</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-1389.35</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>27740.81</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-4240.81</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3542.72</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-542.72</td>\n",
       "      <td>18.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>25037.87</td>\n",
       "      <td>21200.00</td>\n",
       "      <td>-3837.87</td>\n",
       "      <td>18.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35991.51</td>\n",
       "      <td>43999.00</td>\n",
       "      <td>8007.49</td>\n",
       "      <td>18.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>13733.33</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>3066.67</td>\n",
       "      <td>18.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>11431.35</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>2567.65</td>\n",
       "      <td>18.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6082.85</td>\n",
       "      <td>7450.00</td>\n",
       "      <td>1367.15</td>\n",
       "      <td>18.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>12674.57</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-1974.57</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>25975.12</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>-4075.12</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>3026.25</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>-476.25</td>\n",
       "      <td>18.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>33172.20</td>\n",
       "      <td>40800.00</td>\n",
       "      <td>7627.80</td>\n",
       "      <td>18.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>13159.16</td>\n",
       "      <td>16200.00</td>\n",
       "      <td>3040.84</td>\n",
       "      <td>18.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>20216.67</td>\n",
       "      <td>24900.00</td>\n",
       "      <td>4683.33</td>\n",
       "      <td>18.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>10220.74</td>\n",
       "      <td>8600.00</td>\n",
       "      <td>-1620.74</td>\n",
       "      <td>18.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>29127.72</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-4627.72</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>5839.08</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>1360.92</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>53063.22</td>\n",
       "      <td>65500.00</td>\n",
       "      <td>12436.78</td>\n",
       "      <td>18.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>28256.79</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>6643.21</td>\n",
       "      <td>19.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>4524.77</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-724.77</td>\n",
       "      <td>19.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2543.57</td>\n",
       "      <td>3149.25</td>\n",
       "      <td>605.68</td>\n",
       "      <td>19.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>60493.51</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>14506.49</td>\n",
       "      <td>19.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>8962.29</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-1462.29</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>14832.32</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>-2432.32</td>\n",
       "      <td>19.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>19676.51</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>4823.49</td>\n",
       "      <td>19.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20349.62</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>-3350.62</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>1926.60</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>473.40</td>\n",
       "      <td>19.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>8381.60</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-1382.60</td>\n",
       "      <td>19.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>38334.67</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>-6334.67</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>20972.54</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-3472.54</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6732.97</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>1667.03</td>\n",
       "      <td>19.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>10419.20</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>2580.80</td>\n",
       "      <td>19.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>24572.28</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-4072.28</td>\n",
       "      <td>19.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>10014.71</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>2485.29</td>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>4209.17</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-709.17</td>\n",
       "      <td>20.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>34678.95</td>\n",
       "      <td>43500.00</td>\n",
       "      <td>8821.05</td>\n",
       "      <td>20.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>18893.76</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>-3193.76</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>8121.22</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>2078.78</td>\n",
       "      <td>20.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>11443.85</td>\n",
       "      <td>9499.00</td>\n",
       "      <td>-1944.85</td>\n",
       "      <td>20.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>33392.40</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>8607.60</td>\n",
       "      <td>20.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6746.48</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>1753.52</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>17251.60</td>\n",
       "      <td>14300.00</td>\n",
       "      <td>-2951.60</td>\n",
       "      <td>20.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6981.34</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>1818.66</td>\n",
       "      <td>20.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2379.58</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>620.42</td>\n",
       "      <td>20.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>10932.17</td>\n",
       "      <td>13800.00</td>\n",
       "      <td>2867.83</td>\n",
       "      <td>20.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>2779.04</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-479.04</td>\n",
       "      <td>20.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>11002.01</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>2897.99</td>\n",
       "      <td>20.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>5144.02</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>1355.98</td>\n",
       "      <td>20.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>7110.32</td>\n",
       "      <td>8990.00</td>\n",
       "      <td>1879.68</td>\n",
       "      <td>20.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>67431.00</td>\n",
       "      <td>14118.19</td>\n",
       "      <td>20.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>7745.02</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-1345.02</td>\n",
       "      <td>21.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>63921.33</td>\n",
       "      <td>80999.00</td>\n",
       "      <td>17077.67</td>\n",
       "      <td>21.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>3940.49</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>1058.51</td>\n",
       "      <td>21.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>23630.85</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-4130.85</td>\n",
       "      <td>21.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>14792.29</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>4007.71</td>\n",
       "      <td>21.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11770.64</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>-2070.64</td>\n",
       "      <td>21.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>9424.34</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>2575.66</td>\n",
       "      <td>21.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>8139.48</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-1439.48</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>22367.52</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>6132.48</td>\n",
       "      <td>21.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13004.34</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-2304.34</td>\n",
       "      <td>21.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8512.03</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-1512.03</td>\n",
       "      <td>21.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>32121.20</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>8878.80</td>\n",
       "      <td>21.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>12523.73</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>3476.27</td>\n",
       "      <td>21.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>19845.36</td>\n",
       "      <td>16300.00</td>\n",
       "      <td>-3545.36</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13292.71</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>3706.29</td>\n",
       "      <td>21.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>8467.17</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-1517.17</td>\n",
       "      <td>21.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11876.82</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>3323.18</td>\n",
       "      <td>21.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>22547.55</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>-4047.55</td>\n",
       "      <td>21.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>50728.89</td>\n",
       "      <td>65000.00</td>\n",
       "      <td>14271.11</td>\n",
       "      <td>21.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>2625.10</td>\n",
       "      <td>2150.00</td>\n",
       "      <td>-475.10</td>\n",
       "      <td>22.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>5863.41</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-1063.41</td>\n",
       "      <td>22.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>9175.06</td>\n",
       "      <td>7499.00</td>\n",
       "      <td>-1676.06</td>\n",
       "      <td>22.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>8453.28</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>2446.72</td>\n",
       "      <td>22.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>5350.91</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>1549.09</td>\n",
       "      <td>22.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>6819.44</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>1980.56</td>\n",
       "      <td>22.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>10303.21</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>2996.79</td>\n",
       "      <td>22.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>9139.04</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>2660.96</td>\n",
       "      <td>22.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>18621.12</td>\n",
       "      <td>24100.00</td>\n",
       "      <td>5478.88</td>\n",
       "      <td>22.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>15954.72</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>-2955.72</td>\n",
       "      <td>22.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>12270.01</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>3629.99</td>\n",
       "      <td>22.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>11490.44</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>3409.56</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>14136.01</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-2636.01</td>\n",
       "      <td>22.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>11095.57</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-2096.57</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>10350.25</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>3149.75</td>\n",
       "      <td>23.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>20680.40</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>6319.60</td>\n",
       "      <td>23.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>34446.96</td>\n",
       "      <td>27900.00</td>\n",
       "      <td>-6546.96</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4209.17</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>1290.83</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3086.38</td>\n",
       "      <td>2499.00</td>\n",
       "      <td>-587.38</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>16427.12</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-3127.12</td>\n",
       "      <td>23.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>43163.25</td>\n",
       "      <td>-10149.56</td>\n",
       "      <td>23.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>3747.21</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>1152.79</td>\n",
       "      <td>23.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>14410.15</td>\n",
       "      <td>18932.55</td>\n",
       "      <td>4522.40</td>\n",
       "      <td>23.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.45</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>4377.55</td>\n",
       "      <td>23.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>9232.79</td>\n",
       "      <td>7450.00</td>\n",
       "      <td>-1782.79</td>\n",
       "      <td>23.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>18165.16</td>\n",
       "      <td>23900.00</td>\n",
       "      <td>5734.84</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.49</td>\n",
       "      <td>9150.00</td>\n",
       "      <td>-2203.49</td>\n",
       "      <td>24.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>6205.48</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-1205.48</td>\n",
       "      <td>24.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>11004.05</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>3495.95</td>\n",
       "      <td>24.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>10505.98</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>3394.02</td>\n",
       "      <td>24.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>19920.63</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>-3920.63</td>\n",
       "      <td>24.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>3621.08</td>\n",
       "      <td>4799.00</td>\n",
       "      <td>1177.92</td>\n",
       "      <td>24.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>17316.59</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>-3416.59</td>\n",
       "      <td>24.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>8471.55</td>\n",
       "      <td>6799.00</td>\n",
       "      <td>-1672.55</td>\n",
       "      <td>24.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>36883.77</td>\n",
       "      <td>49000.00</td>\n",
       "      <td>12116.23</td>\n",
       "      <td>24.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>8242.62</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>-1642.62</td>\n",
       "      <td>24.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.16</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-2691.16</td>\n",
       "      <td>24.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>3001.37</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>998.63</td>\n",
       "      <td>24.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.26</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-699.26</td>\n",
       "      <td>24.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>23485.45</td>\n",
       "      <td>18777.00</td>\n",
       "      <td>-4708.45</td>\n",
       "      <td>25.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>10835.95</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>3664.05</td>\n",
       "      <td>25.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6013.05</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-1213.05</td>\n",
       "      <td>25.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>3735.04</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>1263.96</td>\n",
       "      <td>25.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>15623.39</td>\n",
       "      <td>20999.00</td>\n",
       "      <td>5375.61</td>\n",
       "      <td>25.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>8667.12</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-1767.12</td>\n",
       "      <td>25.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>8916.19</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>3082.81</td>\n",
       "      <td>25.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>8688.92</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>3011.08</td>\n",
       "      <td>25.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>12027.56</td>\n",
       "      <td>16200.00</td>\n",
       "      <td>4172.44</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>12976.15</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>4523.85</td>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>44407.14</td>\n",
       "      <td>59999.00</td>\n",
       "      <td>15591.86</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>31059.18</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>10940.82</td>\n",
       "      <td>26.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>8110.06</td>\n",
       "      <td>10990.00</td>\n",
       "      <td>2879.94</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>5679.39</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-1179.39</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>4255.62</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>1544.38</td>\n",
       "      <td>26.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>3164.66</td>\n",
       "      <td>2499.00</td>\n",
       "      <td>-665.66</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>3154.48</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>1145.52</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>4307.68</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>-907.68</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>5195.69</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-1095.69</td>\n",
       "      <td>26.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>34863.53</td>\n",
       "      <td>47600.00</td>\n",
       "      <td>12736.47</td>\n",
       "      <td>26.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>8638.63</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>3161.37</td>\n",
       "      <td>26.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>32771.45</td>\n",
       "      <td>44800.00</td>\n",
       "      <td>12028.55</td>\n",
       "      <td>26.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>28154.84</td>\n",
       "      <td>38500.00</td>\n",
       "      <td>10345.16</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>67952.07</td>\n",
       "      <td>53500.00</td>\n",
       "      <td>-14452.07</td>\n",
       "      <td>27.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>13471.84</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>-2871.84</td>\n",
       "      <td>27.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>23227.15</td>\n",
       "      <td>31900.00</td>\n",
       "      <td>8672.85</td>\n",
       "      <td>27.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7131.92</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>2668.08</td>\n",
       "      <td>27.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8071.70</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>3028.30</td>\n",
       "      <td>27.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>26478.15</td>\n",
       "      <td>20800.00</td>\n",
       "      <td>-5678.15</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7116.68</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>2683.32</td>\n",
       "      <td>27.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>9312.21</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-2012.21</td>\n",
       "      <td>27.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10848.85</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-2348.85</td>\n",
       "      <td>27.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>10597.72</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-2297.72</td>\n",
       "      <td>27.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7940.44</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>3059.56</td>\n",
       "      <td>27.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>5040.46</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>1959.54</td>\n",
       "      <td>27.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>10716.00</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>4184.00</td>\n",
       "      <td>28.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>9205.10</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>3594.90</td>\n",
       "      <td>28.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>31656.01</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>-6956.01</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>13853.36</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-3053.36</td>\n",
       "      <td>28.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>7098.53</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>2801.47</td>\n",
       "      <td>28.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>8591.66</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>3408.34</td>\n",
       "      <td>28.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>12201.29</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-2701.29</td>\n",
       "      <td>28.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>50074.56</td>\n",
       "      <td>69990.00</td>\n",
       "      <td>19915.44</td>\n",
       "      <td>28.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>11432.63</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-2532.63</td>\n",
       "      <td>28.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6437.21</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>2561.79</td>\n",
       "      <td>28.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>8351.34</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-1851.34</td>\n",
       "      <td>28.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>35336.33</td>\n",
       "      <td>49500.00</td>\n",
       "      <td>14163.67</td>\n",
       "      <td>28.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>74232.55</td>\n",
       "      <td>103999.00</td>\n",
       "      <td>29766.45</td>\n",
       "      <td>28.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>45664.45</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>-10164.45</td>\n",
       "      <td>28.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>19298.61</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>-4299.61</td>\n",
       "      <td>28.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>11580.54</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-2580.54</td>\n",
       "      <td>28.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>23496.36</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>9503.64</td>\n",
       "      <td>28.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31557.80</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-7057.80</td>\n",
       "      <td>28.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>24323.48</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>-5523.48</td>\n",
       "      <td>29.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>18285.07</td>\n",
       "      <td>25900.00</td>\n",
       "      <td>7614.93</td>\n",
       "      <td>29.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>25640.67</td>\n",
       "      <td>19800.00</td>\n",
       "      <td>-5840.67</td>\n",
       "      <td>29.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>5983.44</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>2516.56</td>\n",
       "      <td>29.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>8973.16</td>\n",
       "      <td>12750.00</td>\n",
       "      <td>3776.84</td>\n",
       "      <td>29.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>23336.06</td>\n",
       "      <td>33200.00</td>\n",
       "      <td>9863.94</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>6110.94</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>2589.06</td>\n",
       "      <td>29.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>7724.17</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>3275.83</td>\n",
       "      <td>29.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>10094.58</td>\n",
       "      <td>7777.00</td>\n",
       "      <td>-2317.58</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>21690.99</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>9209.01</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>10809.48</td>\n",
       "      <td>15400.00</td>\n",
       "      <td>4590.52</td>\n",
       "      <td>29.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>11018.79</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>4681.21</td>\n",
       "      <td>29.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>13377.99</td>\n",
       "      <td>10300.00</td>\n",
       "      <td>-3077.99</td>\n",
       "      <td>29.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>12731.26</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-2931.26</td>\n",
       "      <td>29.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>11178.46</td>\n",
       "      <td>8600.00</td>\n",
       "      <td>-2578.46</td>\n",
       "      <td>29.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>21681.00</td>\n",
       "      <td>30999.00</td>\n",
       "      <td>9318.00</td>\n",
       "      <td>30.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>27293.13</td>\n",
       "      <td>39040.00</td>\n",
       "      <td>11746.87</td>\n",
       "      <td>30.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>6510.80</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-1510.80</td>\n",
       "      <td>30.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>8298.32</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>3601.68</td>\n",
       "      <td>30.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>10821.19</td>\n",
       "      <td>8299.00</td>\n",
       "      <td>-2522.19</td>\n",
       "      <td>30.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>5346.97</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-1246.97</td>\n",
       "      <td>30.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5484.02</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>2415.98</td>\n",
       "      <td>30.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>5238.05</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-1238.05</td>\n",
       "      <td>30.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>17597.10</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>7902.90</td>\n",
       "      <td>30.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7727.53</td>\n",
       "      <td>11200.00</td>\n",
       "      <td>3472.47</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>6358.55</td>\n",
       "      <td>4850.00</td>\n",
       "      <td>-1508.55</td>\n",
       "      <td>31.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>2886.35</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>1313.65</td>\n",
       "      <td>31.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>14049.76</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-3349.76</td>\n",
       "      <td>31.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>12974.89</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>5925.11</td>\n",
       "      <td>31.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>6858.89</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>3140.11</td>\n",
       "      <td>31.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>15691.29</td>\n",
       "      <td>22900.00</td>\n",
       "      <td>7208.71</td>\n",
       "      <td>31.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2393.34</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1106.66</td>\n",
       "      <td>31.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>6425.00</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>2975.00</td>\n",
       "      <td>31.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>50206.83</td>\n",
       "      <td>73500.00</td>\n",
       "      <td>23293.17</td>\n",
       "      <td>31.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>25685.67</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-6185.67</td>\n",
       "      <td>31.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>25951.31</td>\n",
       "      <td>19692.08</td>\n",
       "      <td>-6259.23</td>\n",
       "      <td>31.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>28910.96</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>13589.04</td>\n",
       "      <td>31.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9919.19</td>\n",
       "      <td>14600.00</td>\n",
       "      <td>4680.81</td>\n",
       "      <td>32.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>9113.05</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-2213.05</td>\n",
       "      <td>32.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>24427.14</td>\n",
       "      <td>36000.00</td>\n",
       "      <td>11572.86</td>\n",
       "      <td>32.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>4738.89</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>2261.11</td>\n",
       "      <td>32.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>25040.42</td>\n",
       "      <td>37000.00</td>\n",
       "      <td>11959.58</td>\n",
       "      <td>32.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>18403.35</td>\n",
       "      <td>13893.75</td>\n",
       "      <td>-4509.60</td>\n",
       "      <td>32.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>14576.33</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>-3576.33</td>\n",
       "      <td>32.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>6610.04</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>3189.96</td>\n",
       "      <td>32.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>10105.70</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>4894.30</td>\n",
       "      <td>32.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>5041.20</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-1241.20</td>\n",
       "      <td>32.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>6503.62</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-1603.62</td>\n",
       "      <td>32.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>13542.79</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-3342.79</td>\n",
       "      <td>32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4364.08</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>2135.92</td>\n",
       "      <td>32.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>12006.66</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>5893.34</td>\n",
       "      <td>32.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>7712.65</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>3787.35</td>\n",
       "      <td>32.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>31251.89</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-7751.89</td>\n",
       "      <td>32.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>7011.74</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3488.26</td>\n",
       "      <td>33.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>5792.10</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>2907.90</td>\n",
       "      <td>33.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>8493.04</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>4306.96</td>\n",
       "      <td>33.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>7950.27</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>4049.73</td>\n",
       "      <td>33.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>8940.39</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>4559.61</td>\n",
       "      <td>33.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>4569.15</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>2330.85</td>\n",
       "      <td>33.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>13041.39</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>6658.61</td>\n",
       "      <td>33.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>20163.48</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>10336.52</td>\n",
       "      <td>33.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2839.03</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>1460.97</td>\n",
       "      <td>33.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>20433.23</td>\n",
       "      <td>30999.00</td>\n",
       "      <td>10565.77</td>\n",
       "      <td>34.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1746.19</td>\n",
       "      <td>2650.00</td>\n",
       "      <td>903.81</td>\n",
       "      <td>34.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>19855.31</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>-5055.31</td>\n",
       "      <td>34.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>19741.65</td>\n",
       "      <td>29999.00</td>\n",
       "      <td>10257.35</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>65613.31</td>\n",
       "      <td>99999.00</td>\n",
       "      <td>34385.69</td>\n",
       "      <td>34.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>7178.20</td>\n",
       "      <td>10950.00</td>\n",
       "      <td>3771.80</td>\n",
       "      <td>34.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>9143.17</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2343.17</td>\n",
       "      <td>34.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>2285.24</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1214.76</td>\n",
       "      <td>34.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>22835.48</td>\n",
       "      <td>16950.00</td>\n",
       "      <td>-5885.48</td>\n",
       "      <td>34.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>10786.38</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-2786.38</td>\n",
       "      <td>34.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>11477.47</td>\n",
       "      <td>17650.00</td>\n",
       "      <td>6172.53</td>\n",
       "      <td>34.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>23972.56</td>\n",
       "      <td>36900.00</td>\n",
       "      <td>12927.44</td>\n",
       "      <td>35.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3648.96</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-948.96</td>\n",
       "      <td>35.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>11706.93</td>\n",
       "      <td>18100.00</td>\n",
       "      <td>6393.07</td>\n",
       "      <td>35.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7178.20</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>3921.80</td>\n",
       "      <td>35.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>22213.53</td>\n",
       "      <td>34500.00</td>\n",
       "      <td>12286.47</td>\n",
       "      <td>35.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>22213.53</td>\n",
       "      <td>34500.00</td>\n",
       "      <td>12286.47</td>\n",
       "      <td>35.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>12897.92</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-3397.92</td>\n",
       "      <td>35.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>12904.68</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-3404.68</td>\n",
       "      <td>35.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>9611.80</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5388.20</td>\n",
       "      <td>35.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>20236.85</td>\n",
       "      <td>31600.00</td>\n",
       "      <td>11363.15</td>\n",
       "      <td>35.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>4151.02</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>2348.98</td>\n",
       "      <td>36.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>28951.20</td>\n",
       "      <td>45500.00</td>\n",
       "      <td>16548.80</td>\n",
       "      <td>36.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>25951.31</td>\n",
       "      <td>18988.13</td>\n",
       "      <td>-6963.18</td>\n",
       "      <td>36.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>5570.46</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>3229.54</td>\n",
       "      <td>36.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>22454.74</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>13045.26</td>\n",
       "      <td>36.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>41711.18</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>-11211.18</td>\n",
       "      <td>36.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>3152.62</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>1846.38</td>\n",
       "      <td>36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>8902.52</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-2402.52</td>\n",
       "      <td>36.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>48699.98</td>\n",
       "      <td>77500.00</td>\n",
       "      <td>28800.02</td>\n",
       "      <td>37.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>16821.54</td>\n",
       "      <td>26800.00</td>\n",
       "      <td>9978.46</td>\n",
       "      <td>37.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>5521.28</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>3278.72</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>5268.17</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>3131.83</td>\n",
       "      <td>37.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>65825.25</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>39173.75</td>\n",
       "      <td>37.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>12459.85</td>\n",
       "      <td>19900.00</td>\n",
       "      <td>7440.15</td>\n",
       "      <td>37.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>65723.62</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>39275.38</td>\n",
       "      <td>37.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>9070.23</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>5429.77</td>\n",
       "      <td>37.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>9348.90</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2548.90</td>\n",
       "      <td>37.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>9369.66</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5630.34</td>\n",
       "      <td>37.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>32954.19</td>\n",
       "      <td>52777.00</td>\n",
       "      <td>19822.81</td>\n",
       "      <td>37.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>17195.57</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-4695.57</td>\n",
       "      <td>37.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>3425.23</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>2074.77</td>\n",
       "      <td>37.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>3173.48</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>1926.52</td>\n",
       "      <td>37.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>10337.11</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-2837.11</td>\n",
       "      <td>37.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>65742.57</td>\n",
       "      <td>105999.00</td>\n",
       "      <td>40256.43</td>\n",
       "      <td>37.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>8661.95</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>5337.05</td>\n",
       "      <td>38.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26062.23</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>16437.77</td>\n",
       "      <td>38.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>7630.64</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-2130.64</td>\n",
       "      <td>38.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>11378.43</td>\n",
       "      <td>18600.00</td>\n",
       "      <td>7221.57</td>\n",
       "      <td>38.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>66032.76</td>\n",
       "      <td>107999.00</td>\n",
       "      <td>41966.24</td>\n",
       "      <td>38.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>7279.97</td>\n",
       "      <td>11950.00</td>\n",
       "      <td>4670.03</td>\n",
       "      <td>39.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>18577.88</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>11922.12</td>\n",
       "      <td>39.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4043.74</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>-1143.74</td>\n",
       "      <td>39.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2178.94</td>\n",
       "      <td>3600.00</td>\n",
       "      <td>1421.06</td>\n",
       "      <td>39.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23286.27</td>\n",
       "      <td>38500.00</td>\n",
       "      <td>15213.73</td>\n",
       "      <td>39.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>12558.90</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-3559.90</td>\n",
       "      <td>39.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>18432.63</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>12067.37</td>\n",
       "      <td>39.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>9057.77</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>5941.23</td>\n",
       "      <td>39.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>13476.90</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>9023.10</td>\n",
       "      <td>40.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>37704.71</td>\n",
       "      <td>26900.00</td>\n",
       "      <td>-10804.71</td>\n",
       "      <td>40.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>32945.80</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-9445.80</td>\n",
       "      <td>40.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>7712.65</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-2212.65</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>65742.57</td>\n",
       "      <td>109999.00</td>\n",
       "      <td>44256.43</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>36764.38</td>\n",
       "      <td>62000.00</td>\n",
       "      <td>25235.62</td>\n",
       "      <td>40.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>77403.06</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>-22403.06</td>\n",
       "      <td>40.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>8530.54</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>5969.46</td>\n",
       "      <td>41.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>15259.81</td>\n",
       "      <td>26000.00</td>\n",
       "      <td>10740.19</td>\n",
       "      <td>41.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>16253.48</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-4753.48</td>\n",
       "      <td>41.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1815.28</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>1284.72</td>\n",
       "      <td>41.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>44193.38</td>\n",
       "      <td>75500.00</td>\n",
       "      <td>31306.62</td>\n",
       "      <td>41.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45272.25</td>\n",
       "      <td>31990.00</td>\n",
       "      <td>-13282.25</td>\n",
       "      <td>41.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>9198.08</td>\n",
       "      <td>15800.00</td>\n",
       "      <td>6601.92</td>\n",
       "      <td>41.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>19854.79</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>-5854.79</td>\n",
       "      <td>41.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>10031.96</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>7268.04</td>\n",
       "      <td>42.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>9661.78</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2861.78</td>\n",
       "      <td>42.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2539.57</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>1860.43</td>\n",
       "      <td>42.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>11474.22</td>\n",
       "      <td>19999.00</td>\n",
       "      <td>8524.78</td>\n",
       "      <td>42.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>65887.51</td>\n",
       "      <td>114999.00</td>\n",
       "      <td>49111.49</td>\n",
       "      <td>42.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2784.46</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>-834.46</td>\n",
       "      <td>42.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>15015.22</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-4515.22</td>\n",
       "      <td>43.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>31216.83</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>23783.17</td>\n",
       "      <td>43.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>5374.74</td>\n",
       "      <td>3750.00</td>\n",
       "      <td>-1624.74</td>\n",
       "      <td>43.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7241.07</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>5558.93</td>\n",
       "      <td>43.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>3450.69</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>2649.31</td>\n",
       "      <td>43.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>16724.45</td>\n",
       "      <td>29600.00</td>\n",
       "      <td>12875.55</td>\n",
       "      <td>43.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>65210.35</td>\n",
       "      <td>115800.00</td>\n",
       "      <td>50589.65</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>10740.09</td>\n",
       "      <td>19100.00</td>\n",
       "      <td>8359.91</td>\n",
       "      <td>43.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>12234.68</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-3734.68</td>\n",
       "      <td>43.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>14402.34</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-4402.34</td>\n",
       "      <td>44.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>10807.78</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-3307.78</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>39074.36</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>30924.64</td>\n",
       "      <td>44.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>14512.61</td>\n",
       "      <td>25999.00</td>\n",
       "      <td>11486.39</td>\n",
       "      <td>44.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3316.44</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-1016.44</td>\n",
       "      <td>44.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>12255.45</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>9744.55</td>\n",
       "      <td>44.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>6781.86</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>-2081.86</td>\n",
       "      <td>44.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>3752.54</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-1152.54</td>\n",
       "      <td>44.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2389.14</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>-739.14</td>\n",
       "      <td>44.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>34761.74</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-10761.74</td>\n",
       "      <td>44.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>11881.05</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>-3681.05</td>\n",
       "      <td>44.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>1873.03</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>1526.97</td>\n",
       "      <td>44.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13212.52</td>\n",
       "      <td>23999.00</td>\n",
       "      <td>10786.48</td>\n",
       "      <td>44.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>17282.06</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>-5382.06</td>\n",
       "      <td>45.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>17011.43</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-5311.43</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2020.25</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>1679.75</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>18942.34</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>-5943.34</td>\n",
       "      <td>45.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>11337.51</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>9562.49</td>\n",
       "      <td>45.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>10547.82</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>8952.18</td>\n",
       "      <td>45.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>6421.18</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-2021.18</td>\n",
       "      <td>45.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>12414.72</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>10585.28</td>\n",
       "      <td>46.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>24796.09</td>\n",
       "      <td>46000.00</td>\n",
       "      <td>21203.91</td>\n",
       "      <td>46.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>4172.84</td>\n",
       "      <td>7750.00</td>\n",
       "      <td>3577.16</td>\n",
       "      <td>46.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>15956.95</td>\n",
       "      <td>29999.00</td>\n",
       "      <td>14042.05</td>\n",
       "      <td>46.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>1858.34</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1641.66</td>\n",
       "      <td>46.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>8522.39</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>-2722.39</td>\n",
       "      <td>46.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>13235.55</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-4236.55</td>\n",
       "      <td>47.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>13600.39</td>\n",
       "      <td>25800.00</td>\n",
       "      <td>12199.61</td>\n",
       "      <td>47.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>4032.10</td>\n",
       "      <td>7650.00</td>\n",
       "      <td>3617.90</td>\n",
       "      <td>47.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>12414.41</td>\n",
       "      <td>8420.00</td>\n",
       "      <td>-3994.41</td>\n",
       "      <td>47.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2573.48</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>2326.52</td>\n",
       "      <td>47.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>14572.70</td>\n",
       "      <td>27900.00</td>\n",
       "      <td>13327.30</td>\n",
       "      <td>47.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>15144.28</td>\n",
       "      <td>29000.00</td>\n",
       "      <td>13855.72</td>\n",
       "      <td>47.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20159.19</td>\n",
       "      <td>38888.00</td>\n",
       "      <td>18728.81</td>\n",
       "      <td>48.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7241.07</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>6758.93</td>\n",
       "      <td>48.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>25682.58</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>-8382.58</td>\n",
       "      <td>48.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>10563.39</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>9936.61</td>\n",
       "      <td>48.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>6546.81</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-2146.81</td>\n",
       "      <td>48.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>38260.36</td>\n",
       "      <td>75555.00</td>\n",
       "      <td>37294.64</td>\n",
       "      <td>49.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>38260.36</td>\n",
       "      <td>75555.00</td>\n",
       "      <td>37294.64</td>\n",
       "      <td>49.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>13443.32</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-4443.32</td>\n",
       "      <td>49.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>14933.54</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>14566.46</td>\n",
       "      <td>49.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>13884.26</td>\n",
       "      <td>27800.00</td>\n",
       "      <td>13915.74</td>\n",
       "      <td>50.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>31373.47</td>\n",
       "      <td>20859.15</td>\n",
       "      <td>-10514.32</td>\n",
       "      <td>50.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>47014.52</td>\n",
       "      <td>95000.00</td>\n",
       "      <td>47985.48</td>\n",
       "      <td>50.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>41075.73</td>\n",
       "      <td>27200.00</td>\n",
       "      <td>-13875.73</td>\n",
       "      <td>51.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>10125.27</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-3425.27</td>\n",
       "      <td>51.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>60603.06</td>\n",
       "      <td>124000.00</td>\n",
       "      <td>63396.94</td>\n",
       "      <td>51.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>14363.76</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-4863.76</td>\n",
       "      <td>51.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>25302.43</td>\n",
       "      <td>52000.00</td>\n",
       "      <td>26697.57</td>\n",
       "      <td>51.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>7391.67</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>7808.33</td>\n",
       "      <td>51.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>9753.44</td>\n",
       "      <td>20400.00</td>\n",
       "      <td>10646.56</td>\n",
       "      <td>52.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>32925.60</td>\n",
       "      <td>69500.00</td>\n",
       "      <td>36574.40</td>\n",
       "      <td>52.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>22941.80</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-7941.80</td>\n",
       "      <td>52.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>6199.59</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>7000.41</td>\n",
       "      <td>53.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>5597.06</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>6401.94</td>\n",
       "      <td>53.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>13409.47</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-4709.47</td>\n",
       "      <td>54.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>6635.73</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-2335.73</td>\n",
       "      <td>54.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>33707.27</td>\n",
       "      <td>73900.00</td>\n",
       "      <td>40192.73</td>\n",
       "      <td>54.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>19309.90</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-6809.90</td>\n",
       "      <td>54.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>19624.73</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>-6924.73</td>\n",
       "      <td>54.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>13757.58</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-4857.58</td>\n",
       "      <td>54.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>12354.92</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>15145.08</td>\n",
       "      <td>55.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>17714.62</td>\n",
       "      <td>11400.00</td>\n",
       "      <td>-6314.62</td>\n",
       "      <td>55.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>11555.80</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>-4155.80</td>\n",
       "      <td>56.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>22011.47</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>-8012.47</td>\n",
       "      <td>57.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>15457.91</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-5657.91</td>\n",
       "      <td>57.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>11099.09</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-4100.09</td>\n",
       "      <td>58.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>35039.40</td>\n",
       "      <td>85555.00</td>\n",
       "      <td>50515.60</td>\n",
       "      <td>59.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>34376.16</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>50623.84</td>\n",
       "      <td>59.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>16773.62</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-6273.62</td>\n",
       "      <td>59.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.75</td>\n",
       "      <td>68500.00</td>\n",
       "      <td>41012.25</td>\n",
       "      <td>59.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>7996.00</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-2997.00</td>\n",
       "      <td>59.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>5284.06</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-1984.06</td>\n",
       "      <td>60.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>34863.53</td>\n",
       "      <td>87777.00</td>\n",
       "      <td>52913.47</td>\n",
       "      <td>60.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>19701.70</td>\n",
       "      <td>49999.00</td>\n",
       "      <td>30297.30</td>\n",
       "      <td>60.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>30409.09</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-11509.09</td>\n",
       "      <td>60.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>14486.38</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-5486.38</td>\n",
       "      <td>60.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>11601.33</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-4401.33</td>\n",
       "      <td>61.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>10824.89</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-4124.89</td>\n",
       "      <td>61.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>65448.37</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>-24948.37</td>\n",
       "      <td>61.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>5351.86</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-2051.86</td>\n",
       "      <td>62.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>25864.65</td>\n",
       "      <td>69990.00</td>\n",
       "      <td>44125.35</td>\n",
       "      <td>63.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>15682.82</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-6082.82</td>\n",
       "      <td>63.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>40765.33</td>\n",
       "      <td>112000.00</td>\n",
       "      <td>71234.67</td>\n",
       "      <td>63.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>1320.56</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>2379.44</td>\n",
       "      <td>64.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3795.06</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-1495.06</td>\n",
       "      <td>65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>19335.34</td>\n",
       "      <td>55555.00</td>\n",
       "      <td>36219.66</td>\n",
       "      <td>65.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>26736.21</td>\n",
       "      <td>16100.00</td>\n",
       "      <td>-10636.21</td>\n",
       "      <td>66.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>26441.03</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>-10541.03</td>\n",
       "      <td>66.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>5002.39</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>-2003.39</td>\n",
       "      <td>66.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>18317.20</td>\n",
       "      <td>10974.21</td>\n",
       "      <td>-7342.99</td>\n",
       "      <td>66.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>40893.84</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>85106.16</td>\n",
       "      <td>67.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>38473.71</td>\n",
       "      <td>119000.00</td>\n",
       "      <td>80526.29</td>\n",
       "      <td>67.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>34863.53</td>\n",
       "      <td>109999.00</td>\n",
       "      <td>75135.47</td>\n",
       "      <td>68.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>10946.44</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-4446.44</td>\n",
       "      <td>68.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>3205.38</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>-1305.38</td>\n",
       "      <td>68.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>16924.74</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-6925.74</td>\n",
       "      <td>69.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9356.43</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-3856.43</td>\n",
       "      <td>70.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>15374.08</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-6474.08</td>\n",
       "      <td>72.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>11056.52</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-4656.52</td>\n",
       "      <td>72.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2440.90</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>-1040.90</td>\n",
       "      <td>74.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>3939.91</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>11560.09</td>\n",
       "      <td>74.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>15380.04</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-6580.04</td>\n",
       "      <td>74.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>35093.27</td>\n",
       "      <td>19990.00</td>\n",
       "      <td>-15103.27</td>\n",
       "      <td>75.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>8270.38</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>-3570.38</td>\n",
       "      <td>75.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>6916.13</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>-3016.13</td>\n",
       "      <td>77.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>6746.48</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-2947.48</td>\n",
       "      <td>77.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2555.02</td>\n",
       "      <td>11600.00</td>\n",
       "      <td>9044.98</td>\n",
       "      <td>77.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>13355.11</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-5855.11</td>\n",
       "      <td>78.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>3948.74</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-1748.74</td>\n",
       "      <td>79.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>9024.05</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-4124.05</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>15292.10</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-6992.10</td>\n",
       "      <td>84.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>7621.25</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-3521.25</td>\n",
       "      <td>85.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>2975.40</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>-1375.40</td>\n",
       "      <td>85.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>14915.98</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-7015.98</td>\n",
       "      <td>88.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>5716.14</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-2716.14</td>\n",
       "      <td>90.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>15819.50</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-7519.50</td>\n",
       "      <td>90.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>13420.15</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-6421.15</td>\n",
       "      <td>91.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>23576.99</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>-11576.99</td>\n",
       "      <td>96.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>8648.80</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-4248.80</td>\n",
       "      <td>96.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15559.43</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-7659.43</td>\n",
       "      <td>96.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>4004.65</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-2004.65</td>\n",
       "      <td>100.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>27225.34</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>-13725.34</td>\n",
       "      <td>101.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>5462.52</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-2762.52</td>\n",
       "      <td>102.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>8131.92</td>\n",
       "      <td>3999.00</td>\n",
       "      <td>-4132.92</td>\n",
       "      <td>103.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>10810.62</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-5810.62</td>\n",
       "      <td>116.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>7973.87</td>\n",
       "      <td>3650.00</td>\n",
       "      <td>-4323.87</td>\n",
       "      <td>118.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5948.04</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-3348.04</td>\n",
       "      <td>128.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>6891.99</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-3891.99</td>\n",
       "      <td>129.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>26331.41</td>\n",
       "      <td>11200.00</td>\n",
       "      <td>-15131.41</td>\n",
       "      <td>135.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>21493.44</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-12493.44</td>\n",
       "      <td>138.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>6537.82</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-3837.82</td>\n",
       "      <td>142.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>12891.96</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>-7591.96</td>\n",
       "      <td>143.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>8340.94</td>\n",
       "      <td>3350.00</td>\n",
       "      <td>-4990.94</td>\n",
       "      <td>148.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>3040.77</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>-1840.77</td>\n",
       "      <td>153.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>9631.08</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-5832.08</td>\n",
       "      <td>153.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13967.55</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-8467.55</td>\n",
       "      <td>153.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>3818.71</td>\n",
       "      <td>1450.00</td>\n",
       "      <td>-2368.71</td>\n",
       "      <td>163.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>7809.13</td>\n",
       "      <td>2899.00</td>\n",
       "      <td>-4910.13</td>\n",
       "      <td>169.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>4590.49</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>-2890.49</td>\n",
       "      <td>170.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>7320.42</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-4720.42</td>\n",
       "      <td>181.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>12261.19</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-8161.19</td>\n",
       "      <td>199.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.29</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-5053.29</td>\n",
       "      <td>202.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>9817.06</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-6617.06</td>\n",
       "      <td>206.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>7161.67</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-4961.67</td>\n",
       "      <td>225.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>7918.89</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-5518.89</td>\n",
       "      <td>229.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>9984.87</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-6984.87</td>\n",
       "      <td>232.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>10381.62</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-7381.62</td>\n",
       "      <td>246.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>8843.22</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-6343.22</td>\n",
       "      <td>253.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>10123.04</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-7323.04</td>\n",
       "      <td>261.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>16165.79</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-11765.79</td>\n",
       "      <td>267.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>17937.36</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-13137.36</td>\n",
       "      <td>273.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>7319.77</td>\n",
       "      <td>1850.00</td>\n",
       "      <td>-5469.77</td>\n",
       "      <td>295.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>16095.32</td>\n",
       "      <td>3600.00</td>\n",
       "      <td>-12495.32</td>\n",
       "      <td>347.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.50</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-8385.50</td>\n",
       "      <td>364.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>9664.46</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>-7764.46</td>\n",
       "      <td>408.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>32481.05</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-26481.05</td>\n",
       "      <td>441.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>9954.42</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>-8154.42</td>\n",
       "      <td>453.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>35956.50</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-29456.50</td>\n",
       "      <td>453.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>10019.90</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>-8219.90</td>\n",
       "      <td>456.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>30628.28</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-25629.28</td>\n",
       "      <td>512.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Prediction    Target  Residual  Difference%\n",
       "698    30480.85  30500.00     19.15         0.06\n",
       "742    16960.31  16999.00     38.69         0.23\n",
       "60     12469.21  12500.00     30.79         0.25\n",
       "110    25614.14  25500.00   -114.14         0.45\n",
       "367    42703.68  42500.00   -203.68         0.48\n",
       "369     3084.69   3100.00     15.31         0.49\n",
       "769    29651.73  29500.00   -151.73         0.51\n",
       "272     9749.53   9800.00     50.47         0.52\n",
       "714    23118.07  22999.00   -119.07         0.52\n",
       "630     8734.58   8800.00     65.42         0.74\n",
       "380     3473.79   3500.00     26.21         0.75\n",
       "648    21174.10  21335.00    160.90         0.75\n",
       "308     8967.74   8900.00    -67.74         0.76\n",
       "665    17858.02  18000.00    141.98         0.79\n",
       "379    17654.84  17800.00    145.16         0.82\n",
       "719    11391.95  11500.00    108.05         0.94\n",
       "102    28625.56  28900.00    274.44         0.95\n",
       "94      7724.17   7800.00     75.83         0.97\n",
       "561     6429.03   6500.00     70.97         1.09\n",
       "242     7597.39   7500.00    -97.39         1.30\n",
       "528    18555.09  18800.00    244.91         1.30\n",
       "61      7396.87   7300.00    -96.87         1.33\n",
       "19     16178.14  16400.00    221.86         1.35\n",
       "280    12327.10  12499.00    171.90         1.38\n",
       "311    51287.19  52055.25    768.06         1.48\n",
       "723     6009.63   6100.00     90.37         1.48\n",
       "49      4973.17   4900.00    -73.17         1.49\n",
       "114    27716.14  27300.00   -416.14         1.52\n",
       "636    28498.91  28950.00    451.09         1.56\n",
       "612     2953.17   3000.00     46.83         1.56\n",
       "47     26425.14  25999.00   -426.14         1.64\n",
       "23     13111.91  12900.00   -211.91         1.64\n",
       "31     12858.08  12650.00   -208.08         1.64\n",
       "91     13421.16  13200.00   -221.16         1.68\n",
       "329     7327.18   7200.00   -127.18         1.77\n",
       "549     3816.33   3750.00    -66.33         1.77\n",
       "252     9721.50   9900.00    178.50         1.80\n",
       "387    44173.72  44999.00    825.28         1.83\n",
       "267    40753.58  40000.00   -753.58         1.88\n",
       "467    22262.80  22711.65    448.85         1.98\n",
       "556    18231.44  18600.00    368.56         1.98\n",
       "165     9596.94   9400.00   -196.94         2.10\n",
       "259     6067.79   6200.00    132.21         2.13\n",
       "601    35371.16  34600.00   -771.16         2.23\n",
       "708    11967.39  11700.00   -267.39         2.29\n",
       "593    17908.00  17500.00   -408.00         2.33\n",
       "398     8707.13   8500.00   -207.13         2.44\n",
       "526    29049.27  28350.00   -699.27         2.47\n",
       "603    14513.46  14900.00    386.54         2.59\n",
       "53     20453.89  21000.00    546.11         2.60\n",
       "632    15383.35  14990.00   -393.35         2.62\n",
       "533    24642.50  24000.00   -642.50         2.68\n",
       "497    50099.92  51500.00   1400.08         2.72\n",
       "212    16133.86  15700.00   -433.86         2.76\n",
       "130    17489.92  18000.00    510.08         2.83\n",
       "290     1894.40   1950.00     55.60         2.85\n",
       "78     30810.25  29900.00   -910.25         3.04\n",
       "642     8721.97   8999.00    277.03         3.08\n",
       "437    18866.50  18300.00   -566.50         3.10\n",
       "101     5958.63   6150.00    191.37         3.11\n",
       "314     5811.74   6000.00    188.26         3.14\n",
       "150     9800.43   9500.00   -300.43         3.16\n",
       "565     7324.63   7100.00   -224.63         3.16\n",
       "574    12583.52  13000.00    416.48         3.20\n",
       "591    10115.13   9800.00   -315.13         3.22\n",
       "172    11156.38  10800.00   -356.38         3.30\n",
       "133     9279.28   9600.00    320.72         3.34\n",
       "480    31369.37  32500.00   1130.63         3.48\n",
       "87      2315.71   2400.00     84.29         3.51\n",
       "11      5175.77   5000.00   -175.77         3.52\n",
       "43     21611.83  22400.00    788.17         3.52\n",
       "96      7976.26   7700.00   -276.26         3.59\n",
       "406    24874.86  23999.00   -875.86         3.65\n",
       "173    36516.35  37900.00   1383.65         3.65\n",
       "540     4666.05   4500.00   -166.05         3.69\n",
       "40     18672.68  18000.00   -672.68         3.74\n",
       "340    14815.83  15400.00    584.17         3.79\n",
       "239    10581.62  10999.00    417.38         3.79\n",
       "109    12663.54  12200.00   -463.54         3.80\n",
       "256     1825.44   1900.00     74.56         3.92\n",
       "317    12247.90  12750.00    502.10         3.94\n",
       "77      5930.73   5700.00   -230.73         4.05\n",
       "301     9782.47  10200.00    417.53         4.09\n",
       "333    12452.22  11960.00   -492.22         4.12\n",
       "570    23163.87  24171.42   1007.55         4.17\n",
       "581     3246.94   3390.00    143.06         4.22\n",
       "693    12354.16  12900.00    545.84         4.23\n",
       "381    33499.44  35000.00   1500.56         4.29\n",
       "438    16257.03  16999.00    741.97         4.36\n",
       "368     8415.81   8800.00    384.19         4.37\n",
       "273    12318.39  12900.00    581.61         4.51\n",
       "235     2765.14   2900.00    134.86         4.65\n",
       "168    11420.95  11999.00    578.05         4.82\n",
       "707     2725.40   2600.00   -125.40         4.82\n",
       "72     23624.69  22500.00  -1124.69         5.00\n",
       "559    11377.84  11999.00    621.16         5.18\n",
       "134    12096.18  11500.00   -596.18         5.18\n",
       "446     9363.27   8900.00   -463.27         5.21\n",
       "127    12311.47  11700.00   -611.47         5.23\n",
       "450    14218.94  13500.00   -718.94         5.33\n",
       "293     8431.89   7999.00   -432.89         5.41\n",
       "18     37600.15  39900.00   2299.85         5.76\n",
       "452    39882.67  37700.00  -2182.67         5.79\n",
       "195    20588.57  21900.00   1311.43         5.99\n",
       "676     6861.84   7300.00    438.16         6.00\n",
       "766    18982.15  17900.00  -1082.15         6.05\n",
       "328    15067.85  14200.00   -867.85         6.11\n",
       "762    12201.29  12999.00    797.71         6.14\n",
       "649    11147.91  10500.00   -647.91         6.17\n",
       "299    20183.30  18999.00  -1184.30         6.23\n",
       "641    17334.11  18500.00   1165.89         6.30\n",
       "5      21289.80  20000.00  -1289.80         6.45\n",
       "545     6826.78   7300.00    473.22         6.48\n",
       "622     4472.47   4200.00   -272.47         6.49\n",
       "422    53294.61  57000.00   3705.39         6.50\n",
       "740     6658.73   6250.00   -408.73         6.54\n",
       "85     19001.29  17800.00  -1201.29         6.75\n",
       "315     4590.49   4300.00   -290.49         6.76\n",
       "462    43236.92  40500.00  -2736.92         6.76\n",
       "394     9076.42   8500.00   -576.42         6.78\n",
       "188    10159.18  10900.00    740.82         6.80\n",
       "731    16027.02  15000.00  -1027.02         6.85\n",
       "448    13502.35  14500.00    997.65         6.88\n",
       "254     6413.26   5999.00   -414.26         6.91\n",
       "667    49221.63  52999.00   3777.37         7.13\n",
       "600    21816.43  23500.00   1683.57         7.16\n",
       "144    11969.66  12900.00    930.34         7.21\n",
       "270     8263.95   7700.00   -563.95         7.32\n",
       "8      11581.54  12500.00    918.46         7.35\n",
       "433     6977.90   6500.00   -477.90         7.35\n",
       "251     6978.91   6500.00   -478.91         7.37\n",
       "553    16392.22  17700.00   1307.78         7.39\n",
       "725    15742.41  16999.00   1256.59         7.39\n",
       "615     7869.47   8500.00    630.53         7.42\n",
       "518    21297.65  19800.00  -1497.65         7.56\n",
       "268     6893.62   6400.00   -493.62         7.71\n",
       "71     15627.03  14500.00  -1127.03         7.77\n",
       "579     3411.53   3700.00    288.47         7.80\n",
       "330    11336.71  12300.00    963.29         7.83\n",
       "59      8195.34   7600.00   -595.34         7.83\n",
       "354    16717.08  15500.00  -1217.08         7.85\n",
       "147     7772.57   7200.00   -572.57         7.95\n",
       "508    16197.42  15000.00  -1197.42         7.98\n",
       "22      6102.36   5650.00   -452.36         8.01\n",
       "103     4488.43   4150.00   -338.43         8.16\n",
       "755    15885.66  17300.00   1414.34         8.18\n",
       "198    17983.10  19600.00   1616.90         8.25\n",
       "470     7045.72   6500.00   -545.72         8.40\n",
       "710     3795.06   3500.00   -295.06         8.43\n",
       "185     6130.96   6700.00    569.04         8.49\n",
       "542     5947.51   6500.00    552.49         8.50\n",
       "412     7609.28   7000.00   -609.28         8.70\n",
       "415     8117.44   8900.00    782.56         8.79\n",
       "200    11098.18  10200.00   -898.18         8.81\n",
       "754    17028.45  18700.00   1671.55         8.94\n",
       "65     19847.25  18200.00  -1647.25         9.05\n",
       "682     5454.34   5000.00   -454.34         9.09\n",
       "222    20662.46  18900.00  -1762.46         9.33\n",
       "89     11696.25  12900.00   1203.75         9.33\n",
       "52     10716.84   9800.00   -916.84         9.36\n",
       "126    13459.92  12300.00  -1159.92         9.43\n",
       "598     8767.54   9700.00    932.46         9.61\n",
       "504    23272.83  25749.75   2476.92         9.62\n",
       "493     5484.97   4999.00   -485.97         9.72\n",
       "449     9495.62   8650.00   -845.62         9.78\n",
       "516    11858.48  10800.00  -1058.48         9.80\n",
       "261    53312.81  48535.50  -4777.31         9.84\n",
       "531    47047.70  52300.00   5252.30        10.04\n",
       "68      6717.17   6100.00   -617.17        10.12\n",
       "376     5391.77   6000.00    608.23        10.14\n",
       "538    17591.09  19600.00   2008.91        10.25\n",
       "419    17591.09  19600.00   2008.91        10.25\n",
       "595    25576.58  28500.00   2923.42        10.26\n",
       "472    20367.25  22700.00   2332.75        10.28\n",
       "386     7267.36   8100.00    832.64        10.28\n",
       "473     1792.36   1999.00    206.64        10.34\n",
       "14      8248.67   9200.00    951.33        10.34\n",
       "123     5643.45   6300.00    656.55        10.42\n",
       "142     6839.57   7650.00    810.43        10.59\n",
       "257    41503.90  37500.00  -4003.90        10.68\n",
       "194     9310.69   8400.00   -910.69        10.84\n",
       "637    35662.26  40000.00   4337.74        10.84\n",
       "410    16407.29  14800.00  -1607.29        10.86\n",
       "395    21303.57  23900.00   2596.43        10.86\n",
       "213    15872.91  14300.00  -1572.91        11.00\n",
       "653    10332.54   9300.00  -1032.54        11.10\n",
       "54     18575.43  20900.00   2324.57        11.12\n",
       "105     8882.65  10000.00   1117.35        11.17\n",
       "597     7797.18   6999.00   -798.18        11.40\n",
       "15     10621.84  11999.00   1377.16        11.48\n",
       "770    10732.07   9600.00  -1132.07        11.79\n",
       "674     2381.01   2700.00    318.99        11.81\n",
       "132    11200.20   9999.00  -1201.20        12.01\n",
       "525     2373.66   2700.00    326.34        12.09\n",
       "730    10696.56  12179.00   1482.44        12.17\n",
       "506     3590.97   3200.00   -390.97        12.22\n",
       "278    53063.22  60500.00   7436.78        12.29\n",
       "108     7300.05   6500.00   -800.05        12.31\n",
       "503     6392.70   7300.00    907.30        12.43\n",
       "536     6746.43   6000.00   -746.43        12.44\n",
       "156     9192.06  10500.00   1307.94        12.46\n",
       "765    11034.66   9800.00  -1234.66        12.60\n",
       "534    16939.86  19400.00   2460.14        12.68\n",
       "50      2479.43   2200.00   -279.43        12.70\n",
       "562     9593.52  11000.00   1406.48        12.79\n",
       "131    23967.81  27500.00   3532.19        12.84\n",
       "157     7491.01   8600.00   1108.99        12.90\n",
       "638     8242.62   7300.00   -942.62        12.91\n",
       "543     9489.56  10900.00   1410.44        12.94\n",
       "724    44312.98  50900.00   6587.02        12.94\n",
       "527     9829.53   8700.00  -1129.53        12.98\n",
       "610     9269.68   8200.00  -1069.68        13.04\n",
       "359     4524.77   3999.00   -525.77        13.15\n",
       "24     23650.15  20900.00  -2750.15        13.16\n",
       "402     8850.21  10200.00   1349.79        13.23\n",
       "360     4770.52   5500.00    729.48        13.26\n",
       "184    29479.97  34000.00   4520.03        13.29\n",
       "324     8316.92   9600.00   1283.08        13.37\n",
       "307    24974.57  28900.00   3925.43        13.58\n",
       "118    14943.75  17300.00   2356.25        13.62\n",
       "607    28498.91  33000.00   4501.09        13.64\n",
       "332    14591.35  16900.00   2308.65        13.66\n",
       "140    10358.58  12000.00   1641.42        13.68\n",
       "627    13074.74  11500.00  -1574.74        13.69\n",
       "761     9097.13   8000.00  -1097.13        13.71\n",
       "207    15010.51  13200.00  -1810.51        13.72\n",
       "685    12593.00  14600.00   2007.00        13.75\n",
       "350    20246.04  23500.00   3253.96        13.85\n",
       "143     8768.29   7700.00  -1068.29        13.87\n",
       "318    39744.80  34900.00  -4844.80        13.88\n",
       "377     9909.62   8700.00  -1209.62        13.90\n",
       "152    44680.77  51900.00   7219.23        13.91\n",
       "57     21942.05  25500.00   3557.95        13.95\n",
       "277    12900.31  15000.00   2099.69        14.00\n",
       "253    18128.32  15900.00  -2228.32        14.01\n",
       "384     4296.77   5000.00    703.23        14.06\n",
       "113     6006.27   6999.00    992.73        14.18\n",
       "287     1972.28   2300.00    327.72        14.25\n",
       "650     8236.36   7200.00  -1036.36        14.39\n",
       "319    10612.51  12400.00   1787.49        14.42\n",
       "430    14197.26  12400.00  -1797.26        14.49\n",
       "95     47013.28  55000.00   7986.72        14.52\n",
       "757    12028.91  10500.00  -1528.91        14.56\n",
       "153     3666.31   3200.00   -466.31        14.57\n",
       "720     2860.97   3350.00    489.03        14.60\n",
       "631     7256.86   8500.00   1243.14        14.63\n",
       "36     10499.60  12300.00   1800.40        14.64\n",
       "763    12383.35  10800.00  -1583.35        14.66\n",
       "344    11937.31  13995.00   2057.69        14.70\n",
       "389     2810.71   2450.00   -360.71        14.72\n",
       "204     7664.32   9000.00   1335.68        14.84\n",
       "718     8041.62   6999.00  -1042.62        14.90\n",
       "484    13334.19  11600.00  -1734.19        14.95\n",
       "258    13680.48  11900.00  -1780.48        14.96\n",
       "616    13500.69  15900.00   2399.31        15.09\n",
       "281    11638.89  13708.50   2069.61        15.10\n",
       "476    15700.53  18500.00   2799.47        15.13\n",
       "716    11425.01  13500.00   2074.99        15.37\n",
       "529    53312.81  63000.00   9687.19        15.38\n",
       "481     6599.62   7800.00   1200.38        15.39\n",
       "706    61639.73  72900.00  11260.27        15.45\n",
       "558     7098.53   8400.00   1301.47        15.49\n",
       "151    10889.39  12900.00   2010.61        15.59\n",
       "663     2613.97   3099.00    485.03        15.65\n",
       "16     23721.58  20500.00  -3221.58        15.72\n",
       "356    10133.68   8750.00  -1383.68        15.81\n",
       "297    22261.50  19200.00  -3061.50        15.95\n",
       "119    10448.50   8999.00  -1449.50        16.11\n",
       "390    24271.90  20900.00  -3371.90        16.13\n",
       "439    14926.69  17800.00   2873.31        16.14\n",
       "441    11619.16  10000.00  -1619.16        16.19\n",
       "548    46465.02  39900.00  -6565.02        16.45\n",
       "460    50954.68  61000.00  10045.32        16.47\n",
       "211     6848.88   8200.00   1351.12        16.48\n",
       "106    16547.03  14200.00  -2347.03        16.53\n",
       "599     4590.16   5500.00    909.84        16.54\n",
       "220    13348.91  15999.00   2650.09        16.56\n",
       "226    11191.05   9600.00  -1591.05        16.57\n",
       "197     2126.49   2550.00    423.51        16.61\n",
       "3       7463.96   6400.00  -1063.96        16.62\n",
       "378     2749.08   3300.00    550.92        16.69\n",
       "507    18282.75  21950.00   3667.25        16.71\n",
       "186    19071.43  22900.00   3828.57        16.72\n",
       "100    32886.86  39500.00   6613.14        16.74\n",
       "620    25752.19  30990.00   5237.81        16.90\n",
       "229     7719.69   9300.00   1580.31        16.99\n",
       "400    25986.31  31310.00   5323.69        17.00\n",
       "336     8250.47   9950.00   1699.53        17.08\n",
       "483    28937.50  34900.00   5962.50        17.08\n",
       "171     8392.42   7150.00  -1242.42        17.38\n",
       "421     8033.61   9750.00   1716.39        17.60\n",
       "643     8156.11   9900.00   1743.89        17.62\n",
       "696     2470.61   3000.00    529.39        17.65\n",
       "286    19659.97  16700.00  -2959.97        17.72\n",
       "496    17540.72  14899.00  -2641.72        17.73\n",
       "655     7814.16   9500.00   1685.84        17.75\n",
       "407    21097.66  17900.00  -3197.66        17.86\n",
       "464     4266.27   5200.00    933.73        17.96\n",
       "758     9380.46   7950.00  -1430.46        17.99\n",
       "670    33614.62  41000.00   7385.38        18.01\n",
       "9      33614.62  41000.00   7385.38        18.01\n",
       "263     9089.35   7700.00  -1389.35        18.04\n",
       "521    27740.81  23500.00  -4240.81        18.05\n",
       "46      3542.72   3000.00   -542.72        18.09\n",
       "237    25037.87  21200.00  -3837.87        18.10\n",
       "28     35991.51  43999.00   8007.49        18.20\n",
       "125    13733.33  16800.00   3066.67        18.25\n",
       "721    11431.35  13999.00   2567.65        18.34\n",
       "749     6082.85   7450.00   1367.15        18.35\n",
       "55     12674.57  10700.00  -1974.57        18.45\n",
       "466    25975.12  21900.00  -4075.12        18.61\n",
       "651     3026.25   2550.00   -476.25        18.68\n",
       "560    33172.20  40800.00   7627.80        18.70\n",
       "247    13159.16  16200.00   3040.84        18.77\n",
       "738    20216.67  24900.00   4683.33        18.81\n",
       "715    10220.74   8600.00  -1620.74        18.85\n",
       "608    29127.72  24500.00  -4627.72        18.89\n",
       "509     5839.08   7200.00   1360.92        18.90\n",
       "435    53063.22  65500.00  12436.78        18.99\n",
       "187    28256.79  34900.00   6643.21        19.03\n",
       "689     4524.77   3800.00   -724.77        19.07\n",
       "51      2543.57   3149.25    605.68        19.23\n",
       "501    60493.51  75000.00  14506.49        19.34\n",
       "292     8962.29   7500.00  -1462.29        19.50\n",
       "193    14832.32  12400.00  -2432.32        19.62\n",
       "711    19676.51  24500.00   4823.49        19.69\n",
       "7      20349.62  16999.00  -3350.62        19.71\n",
       "569     1926.60   2400.00    473.40        19.72\n",
       "453     8381.60   6999.00  -1382.60        19.75\n",
       "444    38334.67  32000.00  -6334.67        19.80\n",
       "454    20972.54  17500.00  -3472.54        19.84\n",
       "176     6732.97   8400.00   1667.03        19.85\n",
       "240    10419.20  13000.00   2580.80        19.85\n",
       "688    24572.28  20500.00  -4072.28        19.86\n",
       "313    10014.71  12500.00   2485.29        19.88\n",
       "471     4209.17   3500.00   -709.17        20.26\n",
       "535    34678.95  43500.00   8821.05        20.28\n",
       "79     18893.76  15700.00  -3193.76        20.34\n",
       "409     8121.22  10200.00   2078.78        20.38\n",
       "154    11443.85   9499.00  -1944.85        20.47\n",
       "494    33392.40  42000.00   8607.60        20.49\n",
       "98      6746.48   8500.00   1753.52        20.63\n",
       "227    17251.60  14300.00  -2951.60        20.64\n",
       "115     6981.34   8800.00   1818.66        20.67\n",
       "744     2379.58   3000.00    620.42        20.68\n",
       "474    10932.17  13800.00   2867.83        20.78\n",
       "459     2779.04   2300.00   -479.04        20.83\n",
       "331    11002.01  13900.00   2897.99        20.85\n",
       "76      5144.02   6500.00   1355.98        20.86\n",
       "244     7110.32   8990.00   1879.68        20.91\n",
       "644    53312.81  67431.00  14118.19        20.94\n",
       "174     7745.02   6400.00  -1345.02        21.02\n",
       "358    63921.33  80999.00  17077.67        21.08\n",
       "425     3940.49   4999.00   1058.51        21.17\n",
       "321    23630.85  19500.00  -4130.85        21.18\n",
       "312    14792.29  18800.00   4007.71        21.32\n",
       "17     11770.64   9700.00  -2070.64        21.35\n",
       "517     9424.34  12000.00   2575.66        21.46\n",
       "392     8139.48   6700.00  -1439.48        21.48\n",
       "416    22367.52  28500.00   6132.48        21.52\n",
       "335    13004.34  10700.00  -2304.34        21.54\n",
       "45      8512.03   7000.00  -1512.03        21.60\n",
       "515    32121.20  41000.00   8878.80        21.66\n",
       "557    12523.73  16000.00   3476.27        21.73\n",
       "41     19845.36  16300.00  -3545.36        21.75\n",
       "13     13292.71  16999.00   3706.29        21.80\n",
       "691     8467.17   6950.00  -1517.17        21.83\n",
       "20     11876.82  15200.00   3323.18        21.86\n",
       "275    22547.55  18500.00  -4047.55        21.88\n",
       "120    50728.89  65000.00  14271.11        21.96\n",
       "647     2625.10   2150.00   -475.10        22.10\n",
       "524     5863.41   4800.00  -1063.41        22.15\n",
       "547     9175.06   7499.00  -1676.06        22.35\n",
       "747     8453.28  10900.00   2446.72        22.45\n",
       "295     5350.91   6900.00   1549.09        22.45\n",
       "80      6819.44   8800.00   1980.56        22.51\n",
       "661    10303.21  13300.00   2996.79        22.53\n",
       "537     9139.04  11800.00   2660.96        22.55\n",
       "626    18621.12  24100.00   5478.88        22.73\n",
       "576    15954.72  12999.00  -2955.72        22.74\n",
       "499    12270.01  15900.00   3629.99        22.83\n",
       "218    11490.44  14900.00   3409.56        22.88\n",
       "458    14136.01  11500.00  -2636.01        22.92\n",
       "138    11095.57   8999.00  -2096.57        23.30\n",
       "320    10350.25  13500.00   3149.75        23.33\n",
       "606    20680.40  27000.00   6319.60        23.41\n",
       "709    34446.96  27900.00  -6546.96        23.47\n",
       "75      4209.17   5500.00   1290.83        23.47\n",
       "155     3086.38   2499.00   -587.38        23.50\n",
       "602    16427.12  13300.00  -3127.12        23.51\n",
       "736    53312.81  43163.25 -10149.56        23.51\n",
       "495     3747.21   4900.00   1152.79        23.53\n",
       "399    14410.15  18932.55   4522.40        23.89\n",
       "771    13922.45  18300.00   4377.55        23.92\n",
       "662     9232.79   7450.00  -1782.79        23.93\n",
       "634    18165.16  23900.00   5734.84        24.00\n",
       "4      11353.49   9150.00  -2203.49        24.08\n",
       "580     6205.48   5000.00  -1205.48        24.11\n",
       "687    11004.05  14500.00   3495.95        24.11\n",
       "305    10505.98  13900.00   3394.02        24.42\n",
       "206    19920.63  16000.00  -3920.63        24.50\n",
       "659     3621.08   4799.00   1177.92        24.55\n",
       "178    17316.59  13900.00  -3416.59        24.58\n",
       "692     8471.55   6799.00  -1672.55        24.60\n",
       "479    36883.77  49000.00  12116.23        24.73\n",
       "209     8242.62   6600.00  -1642.62        24.89\n",
       "773    13491.16  10800.00  -2691.16        24.92\n",
       "594     3001.37   4000.00    998.63        24.97\n",
       "1       3499.26   2800.00   -699.26        24.97\n",
       "625    23485.45  18777.00  -4708.45        25.08\n",
       "370    10835.95  14500.00   3664.05        25.27\n",
       "83      6013.05   4800.00  -1213.05        25.27\n",
       "443     3735.04   4999.00   1263.96        25.28\n",
       "158    15623.39  20999.00   5375.61        25.60\n",
       "519     8667.12   6900.00  -1767.12        25.61\n",
       "177     8916.19  11999.00   3082.81        25.69\n",
       "361     8688.92  11700.00   3011.08        25.74\n",
       "482    12027.56  16200.00   4172.44        25.76\n",
       "705    12976.15  17500.00   4523.85        25.85\n",
       "135    44407.14  59999.00  15591.86        25.99\n",
       "264    31059.18  42000.00  10940.82        26.05\n",
       "372     8110.06  10990.00   2879.94        26.21\n",
       "672     5679.39   4500.00  -1179.39        26.21\n",
       "699     4255.62   5800.00   1544.38        26.63\n",
       "306     3164.66   2499.00   -665.66        26.64\n",
       "279     3154.48   4300.00   1145.52        26.64\n",
       "145     4307.68   3400.00   -907.68        26.70\n",
       "681     5195.69   4100.00  -1095.69        26.72\n",
       "175    34863.53  47600.00  12736.47        26.76\n",
       "440     8638.63  11800.00   3161.37        26.79\n",
       "683    32771.45  44800.00  12028.55        26.85\n",
       "592    28154.84  38500.00  10345.16        26.87\n",
       "429    67952.07  53500.00 -14452.07        27.01\n",
       "294    13471.84  10600.00  -2871.84        27.09\n",
       "623    23227.15  31900.00   8672.85        27.19\n",
       "107     7131.92   9800.00   2668.08        27.23\n",
       "86      8071.70  11100.00   3028.30        27.28\n",
       "37     26478.15  20800.00  -5678.15        27.30\n",
       "92      7116.68   9800.00   2683.32        27.38\n",
       "231     9312.21   7300.00  -2012.21        27.56\n",
       "62     10848.85   8500.00  -2348.85        27.63\n",
       "572    10597.72   8300.00  -2297.72        27.68\n",
       "568     7940.44  11000.00   3059.56        27.81\n",
       "202     5040.46   7000.00   1959.54        27.99\n",
       "327    10716.00  14900.00   4184.00        28.08\n",
       "310     9205.10  12800.00   3594.90        28.09\n",
       "461    31656.01  24700.00  -6956.01        28.16\n",
       "613    13853.36  10800.00  -3053.36        28.27\n",
       "323     7098.53   9900.00   2801.47        28.30\n",
       "752     8591.66  12000.00   3408.34        28.40\n",
       "348    12201.29   9500.00  -2701.29        28.43\n",
       "424    50074.56  69990.00  19915.44        28.45\n",
       "584    11432.63   8900.00  -2532.63        28.46\n",
       "99      6437.21   8999.00   2561.79        28.47\n",
       "149     8351.34   6500.00  -1851.34        28.48\n",
       "58     35336.33  49500.00  14163.67        28.61\n",
       "423    74232.55 103999.00  29766.45        28.62\n",
       "726    45664.45  35500.00 -10164.45        28.63\n",
       "614    19298.61  14999.00  -4299.61        28.67\n",
       "129    11580.54   9000.00  -2580.54        28.67\n",
       "303    23496.36  33000.00   9503.64        28.80\n",
       "21     31557.80  24500.00  -7057.80        28.81\n",
       "767    24323.48  18800.00  -5523.48        29.38\n",
       "192    18285.07  25900.00   7614.93        29.40\n",
       "700    25640.67  19800.00  -5840.67        29.50\n",
       "552     5983.44   8500.00   2516.56        29.61\n",
       "500     8973.16  12750.00   3776.84        29.62\n",
       "734    23336.06  33200.00   9863.94        29.71\n",
       "233     6110.94   8700.00   2589.06        29.76\n",
       "582     7724.17  11000.00   3275.83        29.78\n",
       "201    10094.58   7777.00  -2317.58        29.80\n",
       "296    21690.99  30900.00   9209.01        29.80\n",
       "523    10809.48  15400.00   4590.52        29.81\n",
       "357    11018.79  15700.00   4681.21        29.82\n",
       "190    13377.99  10300.00  -3077.99        29.88\n",
       "234    12731.26   9800.00  -2931.26        29.91\n",
       "695    11178.46   8600.00  -2578.46        29.98\n",
       "364    21681.00  30999.00   9318.00        30.06\n",
       "362    27293.13  39040.00  11746.87        30.09\n",
       "309     6510.80   5000.00  -1510.80        30.22\n",
       "146     8298.32  11900.00   3601.68        30.27\n",
       "373    10821.19   8299.00  -2522.19        30.39\n",
       "513     5346.97   4100.00  -1246.97        30.41\n",
       "12      5484.02   7900.00   2415.98        30.58\n",
       "352     5238.05   4000.00  -1238.05        30.95\n",
       "181    17597.10  25500.00   7902.90        30.99\n",
       "128     7727.53  11200.00   3472.47        31.00\n",
       "753     6358.55   4850.00  -1508.55        31.10\n",
       "671     2886.35   4200.00   1313.65        31.28\n",
       "764    14049.76  10700.00  -3349.76        31.31\n",
       "216    12974.89  18900.00   5925.11        31.35\n",
       "260     6858.89   9999.00   3140.11        31.40\n",
       "124    15691.29  22900.00   7208.71        31.48\n",
       "214     2393.34   3500.00   1106.66        31.62\n",
       "405     6425.00   9400.00   2975.00        31.65\n",
       "70     50206.83  73500.00  23293.17        31.69\n",
       "596    25685.67  19500.00  -6185.67        31.72\n",
       "351    25951.31  19692.08  -6259.23        31.79\n",
       "489    28910.96  42500.00  13589.04        31.97\n",
       "38      9919.19  14600.00   4680.81        32.06\n",
       "274     9113.05   6900.00  -2213.05        32.07\n",
       "374    24427.14  36000.00  11572.86        32.15\n",
       "302     4738.89   7000.00   2261.11        32.30\n",
       "148    25040.42  37000.00  11959.58        32.32\n",
       "164    18403.35  13893.75  -4509.60        32.46\n",
       "420    14576.33  11000.00  -3576.33        32.51\n",
       "555     6610.04   9800.00   3189.96        32.55\n",
       "505    10105.70  15000.00   4894.30        32.63\n",
       "250     5041.20   3800.00  -1241.20        32.66\n",
       "491     6503.62   4900.00  -1603.62        32.73\n",
       "426    13542.79  10200.00  -3342.79        32.77\n",
       "84      4364.08   6500.00   2135.92        32.86\n",
       "136    12006.66  17900.00   5893.34        32.92\n",
       "139     7712.65  11500.00   3787.35        32.93\n",
       "618    31251.89  23500.00  -7751.89        32.99\n",
       "702     7011.74  10500.00   3488.26        33.22\n",
       "728     5792.10   8700.00   2907.90        33.42\n",
       "751     8493.04  12800.00   4306.96        33.65\n",
       "498     7950.27  12000.00   4049.73        33.75\n",
       "605     8940.39  13500.00   4559.61        33.77\n",
       "678     4569.15   6900.00   2330.85        33.78\n",
       "205    13041.39  19700.00   6658.61        33.80\n",
       "265    20163.48  30500.00  10336.52        33.89\n",
       "403     2839.03   4300.00   1460.97        33.98\n",
       "291    20433.23  30999.00  10565.77        34.08\n",
       "74      1746.19   2650.00    903.81        34.11\n",
       "741    19855.31  14800.00  -5055.31        34.16\n",
       "170    19741.65  29999.00  10257.35        34.19\n",
       "203    65613.31  99999.00  34385.69        34.39\n",
       "289     7178.20  10950.00   3771.80        34.45\n",
       "90      9143.17   6800.00  -2343.17        34.46\n",
       "563     2285.24   3500.00   1214.76        34.71\n",
       "112    22835.48  16950.00  -5885.48        34.72\n",
       "189    10786.38   8000.00  -2786.38        34.83\n",
       "669    11477.47  17650.00   6172.53        34.97\n",
       "163    23972.56  36900.00  12927.44        35.03\n",
       "97      3648.96   2700.00   -948.96        35.15\n",
       "690    11706.93  18100.00   6393.07        35.32\n",
       "117     7178.20  11100.00   3921.80        35.33\n",
       "673    22213.53  34500.00  12286.47        35.61\n",
       "739    22213.53  34500.00  12286.47        35.61\n",
       "341    12897.92   9500.00  -3397.92        35.77\n",
       "159    12904.68   9500.00  -3404.68        35.84\n",
       "64      9611.80  15000.00   5388.20        35.92\n",
       "300    20236.85  31600.00  11363.15        35.96\n",
       "269     4151.02   6500.00   2348.98        36.14\n",
       "345    28951.20  45500.00  16548.80        36.37\n",
       "442    25951.31  18988.13  -6963.18        36.67\n",
       "475     5570.46   8800.00   3229.54        36.70\n",
       "355    22454.74  35500.00  13045.26        36.75\n",
       "246    41711.18  30500.00 -11211.18        36.76\n",
       "111     3152.62   4999.00   1846.38        36.93\n",
       "490     8902.52   6500.00  -2402.52        36.96\n",
       "748    48699.98  77500.00  28800.02        37.16\n",
       "457    16821.54  26800.00   9978.46        37.23\n",
       "104     5521.28   8800.00   3278.72        37.26\n",
       "743     5268.17   8400.00   3131.83        37.28\n",
       "469    65825.25 104999.00  39173.75        37.31\n",
       "32     12459.85  19900.00   7440.15        37.39\n",
       "666    65723.62 104999.00  39275.38        37.41\n",
       "401     9070.23  14500.00   5429.77        37.45\n",
       "586     9348.90   6800.00  -2548.90        37.48\n",
       "541     9369.66  15000.00   5630.34        37.54\n",
       "160    32954.19  52777.00  19822.81        37.56\n",
       "652    17195.57  12500.00  -4695.57        37.56\n",
       "583     3425.23   5500.00   2074.77        37.72\n",
       "589     3173.48   5100.00   1926.52        37.77\n",
       "735    10337.11   7500.00  -2837.11        37.83\n",
       "375    65742.57 105999.00  40256.43        37.98\n",
       "217     8661.95  13999.00   5337.05        38.12\n",
       "29     26062.23  42500.00  16437.77        38.68\n",
       "283     7630.64   5500.00  -2130.64        38.74\n",
       "418    11378.43  18600.00   7221.57        38.83\n",
       "137    66032.76 107999.00  41966.24        38.86\n",
       "567     7279.97  11950.00   4670.03        39.08\n",
       "73     18577.88  30500.00  11922.12        39.09\n",
       "488     4043.74   2900.00  -1143.74        39.44\n",
       "26      2178.94   3600.00   1421.06        39.47\n",
       "42     23286.27  38500.00  15213.73        39.52\n",
       "468    12558.90   8999.00  -3559.90        39.56\n",
       "241    18432.63  30500.00  12067.37        39.57\n",
       "325     9057.77  14999.00   5941.23        39.61\n",
       "675    13476.90  22500.00   9023.10        40.10\n",
       "737    37704.71  26900.00 -10804.71        40.17\n",
       "346    32945.80  23500.00  -9445.80        40.19\n",
       "445     7712.65   5500.00  -2212.65        40.23\n",
       "266    65742.57 109999.00  44256.43        40.23\n",
       "478    36764.38  62000.00  25235.62        40.70\n",
       "48     77403.06  55000.00 -22403.06        40.73\n",
       "712     8530.54  14500.00   5969.46        41.17\n",
       "587    15259.81  26000.00  10740.19        41.31\n",
       "431    16253.48  11500.00  -4753.48        41.33\n",
       "141     1815.28   3100.00   1284.72        41.44\n",
       "363    44193.38  75500.00  31306.62        41.47\n",
       "25     45272.25  31990.00 -13282.25        41.52\n",
       "230     9198.08  15800.00   6601.92        41.78\n",
       "34     19854.79  14000.00  -5854.79        41.82\n",
       "697    10031.96  17300.00   7268.04        42.01\n",
       "371     9661.78   6800.00  -2861.78        42.09\n",
       "628     2539.57   4400.00   1860.43        42.28\n",
       "388    11474.22  19999.00   8524.78        42.63\n",
       "316    65887.51 114999.00  49111.49        42.71\n",
       "199     2784.46   1950.00   -834.46        42.79\n",
       "514    15015.22  10500.00  -4515.22        43.00\n",
       "210    31216.83  55000.00  23783.17        43.24\n",
       "337     5374.74   3750.00  -1624.74        43.33\n",
       "10      7241.07  12800.00   5558.93        43.43\n",
       "646     3450.69   6100.00   2649.31        43.43\n",
       "39     16724.45  29600.00  12875.55        43.50\n",
       "322    65210.35 115800.00  50589.65        43.69\n",
       "645    10740.09  19100.00   8359.91        43.77\n",
       "413    12234.68   8500.00  -3734.68        43.94\n",
       "640    14402.34  10000.00  -4402.34        44.02\n",
       "88     10807.78   7500.00  -3307.78        44.10\n",
       "585    39074.36  69999.00  30924.64        44.18\n",
       "590    14512.61  25999.00  11486.39        44.18\n",
       "66      3316.44   2300.00  -1016.44        44.19\n",
       "588    12255.45  22000.00   9744.55        44.29\n",
       "539     6781.86   4700.00  -2081.86        44.29\n",
       "756     3752.54   2600.00  -1152.54        44.33\n",
       "428     2389.14   1650.00   -739.14        44.80\n",
       "285    34761.74  24000.00 -10761.74        44.84\n",
       "566    11881.05   8200.00  -3681.05        44.89\n",
       "510     1873.03   3400.00   1526.97        44.91\n",
       "167    13212.52  23999.00  10786.48        44.95\n",
       "511    17282.06  11900.00  -5382.06        45.23\n",
       "522    17011.43  11700.00  -5311.43        45.40\n",
       "349     2020.25   3700.00   1679.75        45.40\n",
       "684    18942.34  12999.00  -5943.34        45.72\n",
       "35     11337.51  20900.00   9562.49        45.75\n",
       "365    10547.82  19500.00   8952.18        45.91\n",
       "745     6421.18   4400.00  -2021.18        45.94\n",
       "334    12414.72  23000.00  10585.28        46.02\n",
       "255    24796.09  46000.00  21203.91        46.10\n",
       "717     4172.84   7750.00   3577.16        46.16\n",
       "191    15956.95  29999.00  14042.05        46.81\n",
       "575     1858.34   3500.00   1641.66        46.90\n",
       "169     8522.39   5800.00  -2722.39        46.94\n",
       "385    13235.55   8999.00  -4236.55        47.08\n",
       "393    13600.39  25800.00  12199.61        47.29\n",
       "654     4032.10   7650.00   3617.90        47.29\n",
       "262    12414.41   8420.00  -3994.41        47.44\n",
       "617     2573.48   4900.00   2326.52        47.48\n",
       "342    14572.70  27900.00  13327.30        47.77\n",
       "512    15144.28  29000.00  13855.72        47.78\n",
       "6      20159.19  38888.00  18728.81        48.16\n",
       "44      7241.07  14000.00   6758.93        48.28\n",
       "564    25682.58  17300.00  -8382.58        48.45\n",
       "658    10563.39  20500.00   9936.61        48.47\n",
       "304     6546.81   4400.00  -2146.81        48.79\n",
       "768    38260.36  75555.00  37294.64        49.36\n",
       "338    38260.36  75555.00  37294.64        49.36\n",
       "760    13443.32   9000.00  -4443.32        49.37\n",
       "179    14933.54  29500.00  14566.46        49.38\n",
       "486    13884.26  27800.00  13915.74        50.06\n",
       "604    31373.47  20859.15 -10514.32        50.41\n",
       "183    47014.52  95000.00  47985.48        50.51\n",
       "121    41075.73  27200.00 -13875.73        51.01\n",
       "759    10125.27   6700.00  -3425.27        51.12\n",
       "343    60603.06 124000.00  63396.94        51.13\n",
       "414    14363.76   9500.00  -4863.76        51.20\n",
       "391    25302.43  52000.00  26697.57        51.34\n",
       "249     7391.67  15200.00   7808.33        51.37\n",
       "353     9753.44  20400.00  10646.56        52.19\n",
       "243    32925.60  69500.00  36574.40        52.63\n",
       "33     22941.80  15000.00  -7941.80        52.95\n",
       "223     6199.59  13200.00   7000.41        53.03\n",
       "703     5597.06  11999.00   6401.94        53.35\n",
       "411    13409.47   8700.00  -4709.47        54.13\n",
       "288     6635.73   4300.00  -2335.73        54.32\n",
       "573    33707.27  73900.00  40192.73        54.39\n",
       "732    19309.90  12500.00  -6809.90        54.48\n",
       "456    19624.73  12700.00  -6924.73        54.53\n",
       "383    13757.58   8900.00  -4857.58        54.58\n",
       "544    12354.92  27500.00  15145.08        55.07\n",
       "530    17714.62  11400.00  -6314.62        55.39\n",
       "551    11555.80   7400.00  -4155.80        56.16\n",
       "248    22011.47  13999.00  -8012.47        57.24\n",
       "245    15457.91   9800.00  -5657.91        57.73\n",
       "339    11099.09   6999.00  -4100.09        58.58\n",
       "656    35039.40  85555.00  50515.60        59.04\n",
       "219    34376.16  85000.00  50623.84        59.56\n",
       "520    16773.62  10500.00  -6273.62        59.75\n",
       "772    27487.75  68500.00  41012.25        59.87\n",
       "701     7996.00   4999.00  -2997.00        59.95\n",
       "215     5284.06   3300.00  -1984.06        60.12\n",
       "447    34863.53  87777.00  52913.47        60.28\n",
       "180    19701.70  49999.00  30297.30        60.60\n",
       "722    30409.09  18900.00 -11509.09        60.89\n",
       "577    14486.38   9000.00  -5486.38        60.96\n",
       "733    11601.33   7200.00  -4401.33        61.13\n",
       "487    10824.89   6700.00  -4124.89        61.57\n",
       "408    65448.37  40500.00 -24948.37        61.60\n",
       "63      5351.86   3300.00  -2051.86        62.18\n",
       "679    25864.65  69990.00  44125.35        63.05\n",
       "228    15682.82   9600.00  -6082.82        63.36\n",
       "284    40765.33 112000.00  71234.67        63.60\n",
       "232     1320.56   3700.00   2379.44        64.31\n",
       "417     3795.06   2300.00  -1495.06        65.00\n",
       "122    19335.34  55555.00  36219.66        65.20\n",
       "238    26736.21  16100.00 -10636.21        66.06\n",
       "347    26441.03  15900.00 -10541.03        66.30\n",
       "276     5002.39   2999.00  -2003.39        66.80\n",
       "93     18317.20  10974.21  -7342.99        66.91\n",
       "436    40893.84 126000.00  85106.16        67.54\n",
       "621    38473.71 119000.00  80526.29        67.67\n",
       "221    34863.53 109999.00  75135.47        68.31\n",
       "455    10946.44   6500.00  -4446.44        68.41\n",
       "578     3205.38   1900.00  -1305.38        68.70\n",
       "624    16924.74   9999.00  -6925.74        69.26\n",
       "326     9356.43   5500.00  -3856.43        70.12\n",
       "477    15374.08   8900.00  -6474.08        72.74\n",
       "633    11056.52   6400.00  -4656.52        72.76\n",
       "69      2440.90   1400.00  -1040.90        74.35\n",
       "224     3939.91  15500.00  11560.09        74.58\n",
       "56     15380.04   8800.00  -6580.04        74.77\n",
       "463    35093.27  19990.00 -15103.27        75.55\n",
       "668     8270.38   4700.00  -3570.38        75.97\n",
       "225     6916.13   3900.00  -3016.13        77.34\n",
       "546     6746.48   3799.00  -2947.48        77.59\n",
       "27      2555.02  11600.00   9044.98        77.97\n",
       "746    13355.11   7500.00  -5855.11        78.07\n",
       "271     3948.74   2200.00  -1748.74        79.49\n",
       "236     9024.05   4900.00  -4124.05        84.16\n",
       "166    15292.10   8300.00  -6992.10        84.24\n",
       "67      7621.25   4100.00  -3521.25        85.88\n",
       "432     2975.40   1600.00  -1375.40        85.96\n",
       "550    14915.98   7900.00  -7015.98        88.81\n",
       "208     5716.14   3000.00  -2716.14        90.54\n",
       "116    15819.50   8300.00  -7519.50        90.60\n",
       "366    13420.15   6999.00  -6421.15        91.74\n",
       "686    23576.99  12000.00 -11576.99        96.47\n",
       "404     8648.80   4400.00  -4248.80        96.56\n",
       "30     15559.43   7900.00  -7659.43        96.95\n",
       "713     4004.65   2000.00  -2004.65       100.23\n",
       "611    27225.34  13500.00 -13725.34       101.67\n",
       "427     5462.52   2700.00  -2762.52       102.32\n",
       "694     8131.92   3999.00  -4132.92       103.35\n",
       "727    10810.62   5000.00  -5810.62       116.21\n",
       "397     7973.87   3650.00  -4323.87       118.46\n",
       "196     5948.04   2600.00  -3348.04       128.77\n",
       "704     6891.99   3000.00  -3891.99       129.73\n",
       "571    26331.41  11200.00 -15131.41       135.10\n",
       "680    21493.44   9000.00 -12493.44       138.82\n",
       "465     6537.82   2700.00  -3837.82       142.14\n",
       "81     12891.96   5300.00  -7591.96       143.24\n",
       "554     8340.94   3350.00  -4990.94       148.98\n",
       "609     3040.77   1200.00  -1840.77       153.40\n",
       "677     9631.08   3799.00  -5832.08       153.52\n",
       "161    13967.55   5500.00  -8467.55       153.96\n",
       "635     3818.71   1450.00  -2368.71       163.36\n",
       "660     7809.13   2899.00  -4910.13       169.37\n",
       "664     4590.49   1700.00  -2890.49       170.03\n",
       "82      7320.42   2600.00  -4720.42       181.55\n",
       "282    12261.19   4100.00  -8161.19       199.05\n",
       "2       7553.29   2500.00  -5053.29       202.13\n",
       "729     9817.06   3200.00  -6617.06       206.78\n",
       "492     7161.67   2200.00  -4961.67       225.53\n",
       "382     7918.89   2400.00  -5518.89       229.95\n",
       "502     9984.87   3000.00  -6984.87       232.83\n",
       "750    10381.62   3000.00  -7381.62       246.05\n",
       "434     8843.22   2500.00  -6343.22       253.73\n",
       "182    10123.04   2800.00  -7323.04       261.54\n",
       "396    16165.79   4400.00 -11765.79       267.40\n",
       "298    17937.36   4800.00 -13137.36       273.69\n",
       "629     7319.77   1850.00  -5469.77       295.66\n",
       "619    16095.32   3600.00 -12495.32       347.09\n",
       "0      10685.50   2300.00  -8385.50       364.59\n",
       "485     9664.46   1900.00  -7764.46       408.66\n",
       "657    32481.05   6000.00 -26481.05       441.35\n",
       "162     9954.42   1800.00  -8154.42       453.02\n",
       "451    35956.50   6500.00 -29456.50       453.18\n",
       "532    10019.90   1800.00  -8219.90       456.66\n",
       "639    30628.28   4999.00 -25629.28       512.69"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sometimes it is useful to check these outputs manually\n",
    "# To see all rows, we use the relevant pandas syntax\n",
    "pd.options.display.max_rows = 999\n",
    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "# Finally, we sort by difference in % and manually check the model\n",
    "df_pf.sort_values(by=['Difference%'])"
   ]
  }
 ],
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