Multiple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('1.04.+Real-life+example.csv')\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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>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",
       "</table>\n",
       "</div>"
      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining the variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>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",
       "    </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>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",
       "    </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",
       "    </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",
       "    </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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.drop(['Model'],axis=1)\n",
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_mv = data.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_mv.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the PDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = data_no_mv['Price'].quantile(0.99)\n",
    "data_1 = data_no_mv[data_no_mv['Price']<q]\n",
    "data_1.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_1['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mileage', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_1['Mileage'].quantile(0.99)\n",
    "data_2 = data_1[data_1['Mileage']<q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mileage', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_2['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EngineV', ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_3 = data_2[data_2['EngineV']<6.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EngineV', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_3['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_no_mv['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_3['Year'].quantile(0.01)\n",
    "data_4 = data_3[data_3['Year']>q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_4['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned = data_4.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the OLS assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3))\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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_cleaned['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relaxing the assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>2.0</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>2.9</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>5.0</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>4.2</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>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.814656</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>3862</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>163</td>\n",
       "      <td>2.5</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>1.6</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>3.0</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>3.5</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>2.0</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      2.0      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",
       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
       "3866     Volkswagen   13500.0        van      124      2.0      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",
       "...           ...   ...        ...  \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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_price = np.log(data_cleaned['Price'])\n",
    "data_cleaned['log_price'] = log_price\n",
    "data_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned = data_cleaned.drop(['Price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
       "       'Registration', 'Year', 'log_price'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif[\"features\"] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
   ]
  },
  {
   "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>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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rearrange a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 39,
   "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",
       "      <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>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",
       "      <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>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",
       "      <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>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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = data_preprocessed['log_price']\n",
    "inputs = data_preprocessed.drop(['log_price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_scaled = scaler.transform(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train, y_hat)\n",
    "plt.xlabel('Targets (y_train)',size=18)\n",
    "plt.ylabel('Predictions (y_hat)',size=18)\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hridayashinde/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residuals PDF')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_train - y_hat)\n",
    "plt.title(\"Residuals PDF\", size=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.744996578792662"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the weights and bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.415239458021299"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
    "reg_summary['Weights'] = reg.coef_\n",
    "reg_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
       "       'Mitsubishi'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned['Brand'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_test = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
    "df_pf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "..            ...     ...\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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = y_test.reset_index(drop=True)\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>770</th>\n",
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       "      <th>771</th>\n",
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       "<p>774 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "       Prediction   Target\n",
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       "2     7553.285218   2500.0\n",
       "3     7463.963017   6400.0\n",
       "4    11353.490075   9150.0\n",
       "..            ...      ...\n",
       "769  29651.726363  29500.0\n",
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       "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": 58,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>770</th>\n",
       "      <td>10732.071179</td>\n",
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       "      <th>771</th>\n",
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       "      <th>772</th>\n",
       "      <td>27487.751303</td>\n",
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       "      <td>41012.248697</td>\n",
       "      <td>59.871896</td>\n",
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       "      <th>773</th>\n",
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       "      <td>24.918176</td>\n",
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       "<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",
       "..            ...      ...           ...          ...\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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <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",
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       "    <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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>698</th>\n",
       "      <td>30480.85</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>19.15</td>\n",
       "      <td>0.06</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 999\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "df_pf.sort_values(by=['Difference%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
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}