Linear Regression car sales( Practical).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1991</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>Sprinter 212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>2003</td>\n",
       "      <td>S 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>Q7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>Rav 4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
       "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
       "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
       "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
       "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
       "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
       "\n",
       "  Registration  Year         Model  \n",
       "0          yes  1991           320  \n",
       "1          yes  1999  Sprinter 212  \n",
       "2          yes  2003         S 500  \n",
       "3          yes  2007            Q7  \n",
       "4          yes  2011         Rav 4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data=pd.read_csv('C:/Users/rjvbb/Desktop/RJv Folder/Folder/Intern docs/Data science/Part_5_Advanced_Statistical_Methods_(Machine_Learning)/S35_L225/1.04. Real-life example.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4345</td>\n",
       "      <td>4173.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4195.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>3947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19418.746935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.237284</td>\n",
       "      <td>2.790734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.550058</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25584.242620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.705797</td>\n",
       "      <td>5.066437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.719097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## exploring describtive statistics of variables\n",
    "raw_data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the variable of interest"
   ]
  },
  {
   "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",
       "    </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## axis=0- row, axis=1 - columns\n",
    "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": 7,
   "metadata": {},
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## data with no missing value\n",
    "data_no_mv=data.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the pdfs (probability distribution function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 11,
     "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": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##removing top 1 percent in other words keeping 99 percentile\n",
    "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 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_no_mv['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=data_no_mv['Mileage'].quantile(0.99)\n",
    "data_2=data_no_mv[data_no_mv['Mileage']<q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 16,
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 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_no_mv['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_3=data_2[data_2['EngineV']<6.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 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_no_mv['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=data_3['Year'].quantile(0.01)\n",
    "data_4=data_3[data_3['Year']>q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 22,
     "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": 23,
   "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>3907</td>\n",
       "      <td>3907.000000</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907.000000</td>\n",
       "      <td>3907.000000</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907</td>\n",
       "      <td>3907.000000</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>1472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1815</td>\n",
       "      <td>3545</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19960.692119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.929358</td>\n",
       "      <td>2.474771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.798055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26048.838086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.242696</td>\n",
       "      <td>0.978819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.127291</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>7274.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.500000</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>11800.000000</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>22000.000000</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>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433.000000</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         3907    3907.000000   3907  3907.000000  3907.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           848            NaN   1472          NaN          NaN   \n",
       "mean           NaN   19960.692119    NaN   158.929358     2.474771   \n",
       "std            NaN   26048.838086    NaN    96.242696     0.978819   \n",
       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    7274.500000    NaN    89.500000     1.800000   \n",
       "50%            NaN   11800.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   22000.000000    NaN   225.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   433.000000     6.300000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3907         3907  3907.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1815         3545          NaN  \n",
       "mean           NaN          NaN  2006.798055  \n",
       "std            NaN          NaN     6.127291  \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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned=data_4.reset_index(drop=True)\n",
    "data_cleaned.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking OLS assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we decided to use some matplotlib code, without explaining it\n",
    "#  can simply use plt.scatter() for each of them (with your current knowledge)\n",
    "# But since Price is the 'y' axis of all the plots, it made sense to plot them side-by-side (so we can compare them)\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
    "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
    "ax1.set_title('Price and Year')\n",
    "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
    "ax2.set_title('Price and EngineV')\n",
    "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
    "ax3.set_title('Price and Mileage')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 25,
     "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": [
    "## realxing the assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "      <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>3902</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>3903</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>3904</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>3905</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>3906</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>3907 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",
       "3902     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
       "3903         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
       "3904  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
       "3905            BMW    6500.0      sedan        1      3.5      Petrol   \n",
       "3906     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",
       "3902          yes  2008   9.350102  \n",
       "3903          yes  2014   9.792556  \n",
       "3904          yes  2014  11.736069  \n",
       "3905          yes  1999   8.779557  \n",
       "3906          yes  2013   9.510445  \n",
       "\n",
       "[3907 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## since price is exponential increasing it doesnt form linear relation. Hence transforming to log function\n",
    "\n",
    "log_price= np.log(data_cleaned['Price'])\n",
    "data_cleaned['log_price']= log_price\n",
    "data_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hb1SuTxCEfdgtT+yWDzx5SBinvKIGf39tR0hBA4DG5g48v7aKGRNWMLQv97xGxk2s5zGCSARISSOIKDN32vCIBYg8m1l+XhZunpAT2nHM6JGMmyfkRC0rFis2QU/MAkEQ7kIuTwRYlyd2yweePCSMs7q0GgEVs1hHQGTGhM0Yl4PRw/rCI3T+7RE6fwez81Cs5zGCSATI3ZEgYgBvAZKfl6U5mTnpbsRyedEbs0AQhLuQ5ElmZhpqaxssncsJ+SDJQz39I1dLbcorajTjvrSODeqXjt3VdaHPVB04joweybil8FxTyhVvHiMIQhtS0ggizpDcjSQkdyMAtixWBAEQVRZcgmD51ARBEKZxWvbFO1Laey1YMWHSd6WMjJLSLaXOB0AKF0FEGXJ3JIg4w2l3RL9XXRtjtRME0XVI9nsNtdsJuWJro5b2Xo7PKzBjwrS+S6nzCSI2kCWNIOIMp90R2zrUT8RqJ4h4pLyiBqtLq1FX34qMHskoKsh2laXArW59re0BQ+12Qq7Y2mi5MiqzOyrHPy81PqXOJ4joQ0oaQcQZAgC1NQnZuQhCH0rXLre5dOlx64uVEhdLd2hWmngPCT8uSgVNOf55UOp8gog+5O5IEHFGkl99RcJqJwgiHDXXLje5dPHc+mJZJkNNQdNqtxNWmng96eO7OvKxzXOLVEKp8wkiNpAljSDijNZ29dUQq50gEgXJReu7+lb0suCiyLIcuMWli+fWt4WhxG3ZecgVLpFOId2bG91A3Y58bGuNc8n1UbJautEVmCC6CqSkEUScwYofkLuj8OJt7FrsEkS0KK+owbKSSgRkWeeWlVQCMO6iqOcdiiU8t75YWrNizYxxOaSUmUA+trXG/6I5I6PZLYIgNNDl7tjY2IjJkyfj4MGDAIDXX38dkydPRmFhIX7/+9+jra3N0U4SBHGKooJsJPnCX125O4oUbyBNwlK8TXlFTej4s8WVqKtvhXjy+LPFlaHjBOFGVmzcF1LQJAJiZ7tReO9QrGG57/l9XsxauCnKvSESgbr6VsxdUobyihrXj3+CIDrhKmm7du3CtGnTsH//fgDAl19+ieeeew6vvfYa3nnnHQSDQaxYscLpfhIEcZL8vCzcPCEntDOa0SMZN0/ICVkTePE2L66rikg8Ip5sJwi30tSinj2Q1a4F7x2KNYP6pasm4ohGBkUicZEnyHHz+CcIohOuu+Mbb7yBBQsW4J577gEAJCUlYcGCBUhNTQUAnHPOOTh0iGqUEEQ0yc/LYk6ovHgbSrFPENrvUKxZXVrtWtdFyrAY30gbdovmjHTt+CcIohOukvbggw+G/X3GGWfgjDPOAAB89913eOWVV/DXv/7V8IUzMlINf8csmZlpUbuWGah/1qD+Ka7XMwW1x5pV23l9sXo8ESDZdAq390/Cbf202p/vTCYwseMd5zH+4oFYW35Atd2O38Ftv6XbsEM+1dW34pcLN+GHPVNw04RcXD6ivw09c55EGhuJdC8A3Y9TmE4ccuTIEcyePRvXXnstLrroIsPfr6trRDAKFSgzM9NQW9vg+HXMQv2zRlftn1ZikPPO7InNKkraeWf2RG1tg2adI15fWcc9HiGqyo2TkGzqxG39S/Z7Vd39kv1eV/XT7HOT1z0zQ5LPg6tHnaV5bau/aXlFDf6tEruaOzAd1xVkW/4dnBpzJJ8iEQHUHmvGP97YifqGlqhb1eRzWPduXgiCgMbmDmY2SbfJIysk0r0AdD9W4MkmU0padXU1Zs+ejRkzZmDWrFmmO0cQXRVe9kXed7UK8X5UdUT1ex9VHcGMcTm4fGjfsEK5EpefTFZAxbIJNyKK6nWdWO3xhLJ4tVGikSZdKXfkVH9Tj/KKGnKfizEs2a2F5P5o9rczM5cpx5I8rtRtheUJIpYYVtIaGxvxy1/+Ev/7v/+Lq6++2oEuEURiw1OyeGglBsnPy7KcYIE1ybs0RIboIiRyLCWr7pleWto6NI/bUXJDqwCy1YU+YQ9m3wRWHLOeUi5m5jJeMW0aTwTRia4U/HLefPNNHD16FM8//zymTp2KqVOn4vHHH3eibwSRkPCyL/KwWoiXtWNvZSefIJwkHspDlFfUYO6SMky5a00o1blerCYJaWoJhJXZUPZLKskhldxgfVYLnnxxSyFwwhzKMcsr5QKYn8v0jBUaTwRhwJK2aVNnbZaZM2di5syZTvWHIBIeq0qW2wvxEoTd6N3AiBVWreN2wLI+8CzvemHJHflxIn6pq2/F0uJKLC2uRPduXgBQHTcrNu4LjRuzcxlvLEmfIYiujmFLGkEQ1mBNPnonpaKCbPi84RFiPq9AhUiJhMXtu+pWreNJPnsiPtWek9VNIQm1AsgSVAg5sWhqCWi6zd+2eLNmUXXeXKY1lgAaTwQhYTq7I0EQ5igqyI4IwDc6KYmK7F7KvwnCCeQZCD0CUDC0L2aMy3H8uqkpPjQ2a8ddGcVK8h4lVhWhmyfk4tniSstxn5IFRI5dlnfp2UjPTKqXFo2kJYS70IoD1TOXKceSnuyOBNEVISWNIKKMcoIyOimtLq1GQDFHBkRQoDXhKMoMhEHxVByj04pakwMKmp3uiVYVITWZEAgEcbyp3VA/BCHSImfHppC8n/l5WWEKLkFIGJnL3FxMniDcAilpBBEDrExQdrkvEYmJnRYiOVoJZ5xW0uy2E9sVpyVhhyIklwmLXv0UVQeOG+6HmrUxPy8LXxw8HmYBHXm+efnjhvg7wp0smjMy1l0giISClDSCcACrKa+1FtpJPkHV3cSuuBYifnHbAtophdEqdm90yC1hVtLcS5hR0AB1y115RQ3K9tSEimQHRaBsTw0G9UsP9c+IG6vdCi6RGFCiD4KwH1LSCMJmrC6Ued9vV/o6nkRqZ8XvpKbQ657ouGkB7TaFUY4TGVIlS1hmZhpqaxusdM8ULMsdb0wYdWMlSz6hhBJXEYQz0KqNIGxGz0JZy8LA+z6rppLUPu3Kc/D82ip0yJQ5n1fAtCvPAUBKXCLj5AI62e9Fa3tkxrdkf2SyCsBdCqMSO+O07MJMnJee5B28MVHKKKRdulPdjZVKgCQe3bt5mdkcgc53g1V8OjXFh2lXnhMRq+gmyzlBxCu0KiMIm+EtingWBt73pYWZEs9Jb0e1GJRLh5wemixZSRjsTs5ARB8nF9CXnNdHNS7tkvP6qH7eToXR7uyOVpP32I1SJuglKALL5o3R/AxvTLASw7LaiwqysaykMix5kVcAWVLiGC0FDQivl8Zyh3Wz5Zwg4hWqk0YQNsOrg8arqcT7fsHQvqrHpfbyihqU7jwcFoNSuvMwyitqALCTMFAS//hHrf6QXRaij6qOGGq3Wg9QjmQFtpP8vCwsmjMSy+aNwaI5I2O6kFSTCXrQ8yx5Y8LDCGVltQOAoDio/JtIXKSYRmk+kWDNay+uq8Lshzdh1sJNmP3wJizfsDea3SWIuIaUNIKwGd6iiGdh4H1/UL90eBSptj2CgEH90gEAL63fh6DCJzIoinhp/T5zN0TEDfl5Wbh5Qk5o8Z7RIxk3T8ixRQHRKm6rhpMKY6Jhxrqo91nKx4SAyDHB2/RRsrq0OsyVGgA6AqLuwt1E/KNWqJ01hts6xLANw807DpGiRhA6IXdHokvipO88L9Mbz/2IlzJ7dWm1qhImxfqoxQ0BYLYTiYVb6g/Z6VJICkA4RtPoayU1kdzW9GZ3jHXiEIp7cgd19a2Yu6Qs9DsYcUlmxTsSBBEOKWlElyMavvNaiyJe0oLyihp8cHLBBHTuPn6w81AoZXasF0lE18TnFSIsKFI7C7sURhrb4ail0bfCjHE5uhfNsUwcUl5Rg+dKqkKbVHX1rXiupAoAxT3FAmkcGH0/WfGOBEGEQ0oa0eWwmn1Rz3GtukM8S9mKjfugXAsHxM72/Lwsw1n2CMIIrLGrpqABYLZrncsoLMWgK2MkU6bVuo1yigqyVbPHWnFjVRbvzh2YjrnThkd8TsuVm5S0+GL2w5sweEA6vj3WrGuezeyZgqtHnUW/M9GlICWN6HJYzb7IO86rO8QrLsuL/SF3RsIptMaunecyqqgVFWRjaXGlqX6wSAS3OT2KqxOeA6LCFKL82whKBQ3oLOa96NVPIxQ1kn2JQ1AML9rOm2drjzVTtkiiy0GJQ4guh9Xsi7zjWnWH9HyfIKxQXlGDuUvKMGvhJsxdUhaRhU0LlkJmRlHbwngPWO1a2L0okxaAcnetF9ftNfSs3IAeF0O75c3q0mpVS7/Z8ykVNF47kbi0dQSxtLgSc5eUYcXGfZbGrRU5SBBugSxpRJeDFxPGs7TxjvPqDlFMGeEUbqpVxCu6HktiUWjbbsud3uyOdssbkl+E02iNJa1jrILwPDnIejcTwdpOxDe6lbTGxkbccMMNePrpp9GvXz8AwD333IOLL74YRUVFjnWQIOyGl3WOFxjPymKVmtL5OvGKTccy8J5IbGKhfMQj0VY07FaeM3okY0h2BlaXVmNpcaXmAtJuecOTf4Q7EQR3bJDYwfINe7G7ui5s/gagWRBe6Q0jfXdIdgbK9tREvJtfHDyu2g6QuyURPXRJ1V27dmH+/PnYv38/AODIkSNYsGABysvLcfHFFzvZP4JwBK2sc0OyM1Tdu4ZkZwAARMZMJ7UXDO2r+n2p7hDv/ARhFieVD69HQEBl98Ebh4WMndgo0UrOYbfy3NoewJYdh0IF6OvqW7GspDNmT3k+nueAUdoY8V+sdh65A9NVXRtzB6ZHtNEGl3kSRUEDwt2vJeXJ7xO4BeGlz8oVL7W5uK0jGEp4pGynDS8imuiKSXvjjTewYMEC9O7dGwBQXFyMK664AhMmTHC0cwQRC3ZX12m28xJ7DOqXDuWyVTjZruf8BGEWljXDDiuHmoKm1W6mL9GKI2FtiJjdKJHHuImIjHHjKc9Gf5/G5g4on7qUAVYJr5i1Udo61H9vVjuPudOGo29GSlhb34wU1eyOdv9uRGLQ1hFkzstyhJOf1QMrbIHceoloomtmePDBB8P+nj17NgBg+/btpi+ckZFq+rtGycxMi9q1zED9s4bd/dNaUGVmpiHtND8aTrRHHE87zY/MzDS8vbU8YgElAnh765eYcvnZ3PNr4fbjiUA8yyZBULdqCYLg6G+ndu5brxmCx1/fEZGq/dZrhqh+fsv2r/HS+n2hTH119a14af0+9EjrZur6Wvxn/zFmu5nn9PbWclVLmfTOZ/ZMQe2x5ojvZfZMQWZmmuqzMkNTS0C1/1MuT8OUy8+2dG49mHl2W7Z/jbr6trC2uvo2VHx1HJeP6B/WbvfvFo9EUz4lGkbeLo9HQFBFU5PeWask2nil+3GGmDmR19U1qr4AdqNWTNhNUP+sweqfUwG/tbUNmu6OtbUNqosxoDOFMO9Zxutxj0dImMVDPMsmtc0Dqd3J91jt3HkD0vGLibkR72HegHTVz79QUhGRSr21PaDaruf6mp+38I6aOd95Z/bEZpXPnHdmT9TWNkQ8Kyto9d9peW7m3Fq/e96A9PDzm/zdSD4RRvAKwGUXnB4WkwZ0uglfPeosy++Qne+hG5KbuH2daJRo3g9PNlGkL5Fw6AnS11s8VQ21oHl5O8VNELHCbWNPK/ZTSTSTeST7BbS2Ry50k/3m4ut4z92oi7PZJA+sgvZ2FrO2GyO/OyUtIayS5PNwXR5TuvkwY1wOBvVLj7kCpIWbsvkSzkB10oiEg1cXSKt4qh5YeRKk9qKCbCT5wl8tK4H6BKGXooJs+LzhA9TnFWwZewxPSma7UXj1C+2kTUVB02rnwYuV4ikiyrptZpM8+FR0tPKKGiwrqQyLl1tWUumaulFGfnde0iaC4HHzhBx076a+mSEhbQTk52Vh0ZyRWDZvDBbNGclM3x+remxUczXxoe0nIuHgLYisFk/l1UHLz8vC1t2Hws6XfUYP2tkiokJAEdek/NsseuqeWXG90cpCuGLjPl2JAfTCeiJmn9THe79lts8Yl8P9vtpiywxqz2jFxn2qxadXbNxnSiZ5BUScT2o3g5Hsk7ykTQShh3ZOkhuthLVyGZea4kNzS0fofYi2JYtqFiY+hpS0TZs2hf29cOFCWztDEHZg1eUr2e9VjYGRXIl451++Ya+qpW75hr26FmwEoYWWIrRi4z7VpDXyBblTMQxWXW94mxtLiyst99EpeC7QPPQsqgQBgKitSKrJOLsVm5Ru6i6HKd3M7fnKyxTw3DHd5s5LxB8vrd/H3RBhbcQqZZzaexDNNP30PiQ+ZEkjEg6rdYFYSQqkdl6dM7VjUjspaYQVeIoQb0HuZAwDy/XmuZJKbsFlgL+54WYlzWn0KGjRcqm2qpCqIcUuqgXsKy0XSgSAXMlNwtqQTGT03m95RU2ErNJr8Y6WJcvuGoiE+6CYNCLhyM/Lwsjzs0IuCx4BGHn+qQQGakVS5e28GAkt1yaCcBKrMQhOxjCwFiZBhSsQK2ajdKf65kbpzkOuiZ+KFSJDQZNknFbtMydr5zmNPJ4OUFcERQBfHDwe3Y4lCPEwBmLFcypxm0aUr2jILHkNRMB6DUTCfdAbSjhCLNPCllfUoGxPTWhxGBSBsj01GNQvHfl5WRg1pK9q/NmoIX0BAMl+9b0Lqd2JnWQiPon2OOfFIPBcda3EMJw05qi2A/oyEmq5AmnFelIgvDpBEVg2b4zmZy7M6a1q3b8wp7dT3cLyDXtRuvMQgmKnIlkwtK8pLwK1eDo1yEvBHBS7xCYoIsLLgJVdVI0X11WFWYBFUURTSwAZPZIxc3JeRHkJNfTML0Yy6BLxB1nSCNtRZirj7aDbDc9awFrwSe2H6tRr8bDaia5JLMY5L7NoR4e6Kw+r3Qi8ZBt6E+yZWRjavZjkWdPjBY8Abla5j6qOGGq3yvINe7F5x6GwTbLNOw5h+Ya9hs9FCUGIWNLWEcTS4srQ+2Uki2hbhxhmAZbGcl19K55cuYs7T8R6HUW4A7KkEabQ2uHRUpL07vjwdpC06v7wrAWUEYmwAzvGuVF4mUVZVgebEjzagpmgdlaAvBK9lk2eNT1eULqSApGxhdHOiGgmJtfNddwIoq6+1daY2Nb2AHee0LPZzJNzbih0TViDlDTCMLzkA1aVIN75ecd5GY8oIxJhB7FQ9lljV7KouB1lULt8EaEFK1mPHCNJUZ5fW6V6jufXVjmyiEnyCWhTSfud5DOXt94jRCrs0cwqZydav5sR9zKCiCd4Mk9rftEj56jQdWJA7o6EYXg7PFaL0urZQdI6zismzSs8SxB6iGbxZYnePVNU21kWNjuxWsxaGdReXlGD59dW6VJqd1fXcT9jJClKB8O0yGq3SpJfvXguq52FgM4YNNbvHY/eAFq/27Qrz4kozq7G6GHxZQElCI+gnVyENY94BOiSc1ToOjEgJY0wDM+CwFOSgE7hNHdJmWo8hVV3RV7GI9aCT89CkCAk9Ixzu9n31XHHzs1DTzFrLerqW7F19ylr2KvvfaZbKdKjfNhl2eTFeJnBrmRDSX4vZi3cxIxNjEdvAK3fLT8vC7+YmBsmy3MHpodl7h09zFxSEoKIJVJiEpacUds0TvJ5dG/QUFhHYkDujibpyr6+PHfB/LwsfHHweFh2L3kKfCmtckAWT7GspDL0XafdFUl4EXYgj8GMlhwwazGLRqptNRc8JVUHjmPRq59i7rThhhQUvTFpdqHmGmRF5tvhYu0RhFDmTtZzjkdvAD3zidKN69tjzairb0XPtGQM6pcera4ShK2wXJSlDNVKRp6fhd3VdbpkiZY8VqsBR7gTsqSZoKtn3eFZEFgp8KXno5ZWOSB2tus5v57ra/0+yQwXI1Y7QbDIz8vCojkjsWzeGCyaM9K1E5+RrGRm0atAqiXs4BGL4qxy1yCrMt+K1XXZvDHI6JGMoI7fMBreAFbdXpUYeTZdfe4lEg9pLMu9i54rqVQtmv1R1RHm5pZyg0ZLHpPLY/xASpoJurqvL8+dkPd8eNnGeOfnFavmXV+tjpRWO0HEO9FIZe6Uq133bt6YKb/SAsqqzLdadFavFbGuvjXCVTO9u1/1s6x2Hjy31+7d1De7WO3yZyNA+9l09bmXSDwyeiRj+Ya9WFp8qmg7S8Fqagkw1ynyDRrepgV5DcUP5O5oAnKX0y6gaMfz0Tp/eUUNPtx9OMxS9+Huw6Fi1fT7EET0KSrIDssmZgdeAZh+1WDbzmcUSamKtUwx4u5ZV9+KZ4tPuY9/39Su+jlWuxZ6LFbTrxoc5s4O8H9HSd5nZqahtraB+blY/w4EYSdJPo+uzLV6kFvkJFdtFvEYu9pVISXNBJTCXRs7no9W/IdawoGOgIhX3/tMV0wbQSQiy+aNiWkafmWMnh2cMyA9ZlY0ucudVZnCS4fdvZtX1dopWZ+MKsAigBfXdZYT4BUh14uexR/gbKwmK86GlUiFINxMW0fQFgUNOCWL1KzNSlhlULpafoV4gNwdTRCLrG7RRvKPnnLXGsOZznjPh5XEQGqXEovI4w6WlVSG+sDLlFZUkA2PIkDCIwgJ9fsQhBuRx+jZQdWB41i+ga8Y2I3S5c6qzOe56U2/ajBYesashZuwurQaI8/PCi3E9MTPqtVls4KexZ/T8Iq5E0Siwov5lGLS9GyQrS6tRnlFDcV4xgFkSTOBnp3CeN6dsFoEkfd8Lszprbp7dGFObwDaiUX0XP+Lg8cjguyDoogvDh6Pm9+AINTIHZiumngjd2A6AMDvBdRCFuI5J07pzkNRTbGupmBatQ7pcdPzeoUIDwHJulZX34qyPTUhxfGXMbCY6rWOOllEl7wkiK6ERxBCaxle3iApG6SeLLvSO5nk9zA3j2it5A5ISTMJL2Yqniu9a+366u2/1vPh1SnjJRYRoO6qI200sdwHNu+I7mKPIOxm7rThWPTqp2GKWu7AdMydNhyAuoKm1W43yr5poTfOKtpWEsllVP5cAW2ZZpXVpdXcmnFyGWz3I9Gzqaj397Jj/mCh5vaZaF4sBCGhJ6OrhFHXybaOINMyTjGe7kGXu2NjYyMmT56MgwcPAgC2bduGwsJCjB07Fo899pijHYxH4j0DldPB2VbPb1eMBUHEI6OG9A3LEjhqSN8Y96gTIwoaACT79XnbxyreSKrpFg2MJAWxG70uT2oun0b6aEffrWbJJIiuhhn5STGe7oFrSdu1axfmz5+P/fv3AwBaWlrwhz/8AcuXL8fpp5+OW2+9FaWlpSgoKHC6r3FDvGegctqlhHf+1BSfatyZFLNGLi9EIqNl1SivqMHSk5n7gE6ZsrS4Eq++95mh4tBOYLT+2aG6Zl2fKxgaOyXUTE03M+i1Up2KSRPQ2q5vW4qXbEOv5Uv6t3z8sfpop3xWvg/xWLCbIGJFUASSfIKhGFWK8XQPXCXtjTfewIIFC3DPPfcAAHbv3o2BAweif//+AIDCwkKsX7+elDQZ8a5EFBVkq6ZQNuJSorXQLCrIxrPFlWGWLwGnzs+LWSsqyMazJZVhPtqCwf4RhBuRkuZI756UNAfoXCRL/1YSawXNKXIHpncJF2U92RvlMvJHfX+gW4HkJdswsqmYn5fFVdLMuCRK88V39a3oJZsv1EIH5HNDvIUSEEQsYClogqAe6yatVeXruO7dvBAEAY3NHXGXZyGe4SppDz74YNjf3377LTIzM0N/9+7dG0eOHDF84YyMVMPfMUtmZlrUrgUAMyfn4cmVu8KKDib7vZg5OU+1L9HuH48eaccheATItTTBI6BHWjddfd2y/Ws8v7YqFGNRV9+K59dWoUdaN1w+oj++OVod4ZooAvjmaBMyM9Pwn/3HVM/7n/3HkJmZho+qdkUIFlEEPqo6gimXn63ZN17/6bi144lALGXTa+9/qJo057X3P8eUy8+OOGblWkaP2/ldvZ//76EGVHx1HJeP6G/pfNG8N6Pfz8xMw5TL09AjrRteWleFo8eakZzkRUtbeCChXEbu/eq47msn+z1obY9U/pL9HmRmpiGzZwpqj0VaNTN7pqj2nbWwEwT1e/lhzxTcNCGX+Rtu2f41lv2rCoHgqfli2b8654u3t37JzSjZ1hHE21u/5Mr+RCGa8olIbESxc22qtlat+Oo4Xlq/L3RMniugrr4VL63fF1rTAYm3NnDL/RhOHBIMBiHIcoGKohj2t17q6hoRjIJNlVcc0wnyBqTjpvGDIyxJeQPSI/riVP+sZJd8oaRCtQ7ZCyUVyBuQzj3//3trt+r3/99bu5E3IB3r/78Dqtdd//8dwHUF2aoLBgCoPdaM2toG7PpCPfHIri/quM+Sjjtz3OMREmbxEEvZ1HBCvcBww4l2y3LC6m9v53f1fr61PRAmd8yez+y9pXf3h747f2l5mItm34wUPHBLPvccevv2aVUN6o43QwQiFDQJSUbqzSdQW9ugqqABQGt7ELW1Dbh61Fmqlq+rR52l3neNoGDp83kD0vHwreHPhvUcnl69K6SgSQSCIp5evYuZREqJNDewIPlEEJF4BOCS8/pgd3VdxFp17pKyMOVNiVw289ax8ZbtPJp6A082GVbSsrKyUFtbG/q7trYWvXv3Nte7BMbJTGA89GSX1HppeO4vvPPz6pjx3G+0dmoJguhaxDKW9/umTqVZqaABnTF185eW61LUeCzfsFdXZjYn1uZGywvYnbhJK5uv0Vg9QhtWvDfRNQmKnVmvRw/rG+ZWXl5Ro+u90/MZNRf+pcWV+OLg8S7hym4Vw0raBRdcgC+//BIHDhxAv379UFJSgmuvvdaJvhEm4QWC85QsXkydkymWAXY9EAPZaAkiLmEtopL9AuYuKYtBj2JPLBfgkshhJTnRm/yER+lO/amzZzlQIy2Wm4pa6InVoxT8+kn2e9G/d2rUEuIQ8cHmHYcwqF962PpQD0rZrLb5r1b3VnlNgo1hJS05ORkLFy7EHXfcgdbWVhQUFGD8+PFO9M3VuNl8y7OE8ZQsXuA37/zdu3lVd0e7d4vjiroEEQVYC6i2DjFussPaiRsW4HqUIq9HiHDZk9r14ISFLMkXH64HWtl81ax8Q7IzItyz3DL3up26+tYuKUcIPtL6T219qIZSNrM2/7XOtbS4EqtLq6P2Drt53c5Ct5K2adOpiSo/Px/vvPOOIx2KB9xerJpnCeMpWfKJUZltS8/5p181WDU75PSrBpu/KcI24j37aCLD2uHuilbkeJlEAagqaFrt0UDK6MZKv21WieOl9DfKtCvPCUs0BQA+r4BpV54DwL1WvniE9dsRhLxOIgtpQ0VNNrM2//VcNxrrZ7ev21kYtqQRzrv7WYVnCePVIQNOTYxqAZRDsjNU4yek+jVGYxyI6GImRTZBaGHUBc8Jl71EZdm8Maael6Q0sWJ5zcb4Fgztqyr/zdaz420KEvbgDU/YTBARaLnUCwCe+M1lzONWLLTRWD+7fd3OgpQ0E9hRrJpVF8bo99WUIJ6SJDK25eXtyzfsRenOQwiKnZN9wdBTgaW7q9WzK8rbaffTvdCiiIgHpABzwN07ndFAbwINOZLFhFX0Wm8xbCXSPMCaH8ygtSlI2MOsyeeG1gQEoYbW2BDRubmWmuKDKIpoagmEaqc1NXcwrbQC9CUVcnpc6lm3W12XOwEpaSaw6i5m1eyq5/taSpJWNi0gMtOYlAEI6Jyg9Qz2Ra9+Gua6lTswHXOnDefeGxEdaFFEKHGrdevZksqYT5Qscgem23IenguhngQaSpyMAZ4xLocys8UZ0jtkdBwRhBy5F5Z8LclM+KbzvEbDLYzGl/HW7W51h/TE7MpxTFFBNpJ84Y/OiLuYltk1Gt9nxQ5I7axMY1I762WS2pUKGtAZa7Po1U919Y9wnvKKGsxdUoYpd63B3CVlKK+oiXWXCEIVUXSnAmnnxtNgRh04qT0/Lwsjz88yFPdlpn4pkbjMXVKGpcWVSPJ7kOznj43u3by4pfBceGkYEQaQZBRLViX7BcPrZ2m9MmvhJsxdUoblG/bixXV7w+LoXly3V3Mdw1u3W11XOwVZ0kxgNebKqruk1e/z6pTxjvNi0ljJDyjtrztw644RQcQDy+aNsf2c3x5TT+UvtZdX1KBsT42hpA9UD4uQI60PGps7Ti5WtQeTpOT7fF4ENIoaE4ScoAjcUnhuyFVdSWu7iNHDTtedoVVtvaK2/uTFl/HW7XaEMTkBKWkmsRJzZdVdMtbf/6jqCLOdXGDcT7wG0CYKSr93eUpxJ3GjNSrecCqtvZmyKTwoWyvBQs9YamzuiMi6SRB6WFairqBJ7K6uw6I5I3Wdy4jsszKHujXrNSlpMcBqdr2igmzVFPdGvm/l+ryYNsLduHXHqCugd1eQcCftMVqwGn034y1bqxsD9rs6HgGkoBGm4A0bI/LMyGflGcqV8DyIzKyLo1F3jZS0GGBHdj3BE55PVzAQrMAz+7Ky8ZBremKgpwQD4QxmLCKEe3CiXp0eC6eR7I7xVvKE3K9jhyCwxzTVUyOcwoh1yojsY2UuL6+owXMllRFjWu5BZHRdHi25RauyGGElu97q0uqIHa6OgGibuxpLNpPMTgz0lGAgnIGslfGN2YLNrI0RvejJ7pjk8+DmCTlxp9iQ+3XsYIn83IHp+PZYM8krwhGk/AV6MJLZtqklgPKKmjC5ISlTrE0H+Rg3si6PltwiJc2laNUpM1LvQc1SRjuXXRtyV40dWjvXhPthFWxO9nvRqpJcIdnfmQa/vcP8uzVr4SZk9EjGyPOzwoLt5bGM8WY9k0Pu1+7j22PNKCrIppg0whHK9tRgUL90XfJK6fnF2/CS1rLy72hhNuYsWnKLlDQXwqtTZrXeA+1cdm14NZkI5yAFzTgZPZKxaM7IqCc+kSvUvILNagqavN1s4WiJuvpWlO2piUtLGeEOknweQwkYpHH20vp9zPFNEGYwut5UJuqbv7Qch+rUM+K2dQTx6nufoa09yB3vVmJ3o5VohJQ0F6JVp2zGuBxugCNPCaOdy8SGF1PIK7FAEG4hVgkwjFqmojFh00YaYYWbJ+TosiwA4eNW6Qaf5PNAEETLGw9E14Y3DuXeYKkpPoiiiKaWADJ6JOO7Bu3v6nEt9wiwtOllNQGfXkhJcyG8RXR+Xha+OHg8zB1y5Pmndhp4SphbU40S9kAxhYQay+aNias0/JJL3+rSambNHScwUwctWhN2Im6kkWU/OkjWCD0yoK6+FbMf3gS/T0BbR/iP09YRRPdu6u69BGEEtfgxtY0EudJlhwxkxe8ayTJrtV6yXkhJcyG8Sau8ogZbdhwKLbqDIrBlxyHdPr69e6aoDvTePVMs9JpIFCj7o3NoPdsnfnNZXClRThNP5Ql4G2fdu3lVYz5ZVm8WibiRNnhAOqoOHFdtJ+xDWoDqJSiy3XQpfpmwA7lngDJMxyxJPg/8PoE5RlnKlJlcDVbqJeuFVl0xQktjLxjaV3VxIgWtv7iuKmJiF0+26xkwahOiVjvRtaDsj84x7cpzIoLxfV4B0648J4a9ItQwUgOnvKIGZXtqQptrQTE8OH76VYPxbHFlmNwWAMwuPFe3dQMwlhVNIneguhKUOzDd8Lmc4Ntj6rElrHbCHHYsgAnCTurqWzF3SRnq6luZxgkj+LwCbp7QGTes5tkgWc/KK2pC15Vku1tzNXhiduUujKSx19W3QsQpjb28ogZAZ3IQ5QSaOzA9FLSudD+QYLUThByWRUxqp+yPzpGfl4VLh5wesop7BODSIadTnJHLkMtoIFJGK9Ga4CW83nD/PRHAsyWVhqynu6vrdH82XqAY6ehAChrhRqT33I6YeKkUFdAZbyZ5HmT0SA5T0JaVVIbJdvnfrP7FCktK2jPPPINx48ahsLAQTz31lF19Snh4E3p5RQ2qv6kPO179TT1zgUAQRph25TnwKRaMcmsOy6UqEV2tok15RQ0+3H04zOLy4e7D9G67DD1KlxzeBK9W2xIwnu3TzILB7Z4TJG/sI8lHgXxE16auvhVLiyvxxcHjWDRnJJbNG4NFc0aGNkJXbNwHpSjWqjIRazlk2t1x27ZtKC4uxqpVq5CSkoLbb78d7777LsaOHWtn/+IWLVcZPRO6G82uRGIgD3hVc7eNVhKErsir732mWoj+1fc+o3fbRRjdVeUlY7JrNzbWCwYnIHljH0l+L9o6ImNeqT4j0dXYvOMQPqo6gqaWQMiVMqNHsqZHkLJMhV45ZMQ13iimlbTKykqMGjUKqampAIBLL70U7733Hilp4Acgmp3QY212JRIHKeA1MzMNtbUNEccA57MWdUVYqYH1pAwmoofRDLhDsjNU44ilGDI74i0SVXHhbRoR+mHJEVE0VicN6Byzfh9lcSTiF0khk2Qvbw098vws7K6uw3f1reh+Mu3/0uJKrC6tZsokMwlHjGBaScvLy8NDDz2EW2+9FSkpKdi0aZOhxAIZGalmL22YzMy0qF0LAN7eWq5qCXt765eYcvnZmDk5D0+u3BUm/JL9XsycnMftKx2n43YfV2ubcnkaplx+tua5EpVoyiY5TsupaMtBLYxmNeRh9b1QftaojP7P/mOq5/rP/mPIzEyzpKAJAH7YMwU3TcjF5SP6mz+RCm4ZE11Z3hhFSz5l9kxBrUrClcyT4+dvKz7lnv+u6cND42zL9q91fYcgEoH/7D+GFxaMx5btX4fJ/7r6Vry0fh96pHWLkMG89b5VTCtp+fn5KCoqwowZM5Ceno78/Hzs2rVL9/fr6hoRjEL1XDVLgdOoCUmpvba2AXkD0vGjvmlhMQE/6puGvAHp3L7ScTpu53G73g+PR4iZcmM30ZJNSpyWU4V3rXH0/Eaw++nW1jZoFnE38mwlGX3JeX3CUupfcl4fpozmyXyWZU4Pz8nqttk9RqI9N/Jwar7uKvLphz2SVcfisfoW1De0cMfh6GF9w8Z4HpVBILoQtceaMfPP61Xfkdb2AF4oqYh4J3iynwdPNplOHNLY2IixY8eiuLgYy5cvR1JSEvr3t3eXz0mkFJyzFm7C3CVltgbud+/m1WxfvmFvRNB21YHjWL5hr219IAiC6ErYWcSdlVKfNU/wkl/E0k2RVRSaikUnHnu/Oq7a3hEQ8eK6vRiSnRGRNAroXJvcUnhuKIO0nNHD+trdTYJwLVqbGPJjkg7Bwq74YdNK2sGDBzFnzhx0dHSgoaEBb775JiZMmGBLp5zGaHplowiC+uwntbMKtMZL4VbC3bAmVZps3U8iJoaIR4xmdywqyEaSL3w6lceQbd1tTrbboUhJ9TX1thPxi1bESVtHEB/v/RaiwgrnFYDpVw1mxs/MGJeDZL/6xjNBdCWkMkVKHUKJnfHDppW0nJwcjB07FlOmTMF1112Hm2++GSNGjLClU05jdAI2CiUHIGLJjHE5GD2sb1gtrtHD+qrukhLuoqggW3WnO9FZJnPpcwNGkzfl52WF6vIICK/LA5hPd2+H1y2v7ibRdWhs7lBNP85b+9w0fnDEJgQL2mgiEhUp78aKjfuYSXiUst8qpmPSAOD222/H7bffbktHjGIl5aXT2RNTU3yqChmriDARX2jFvriFGeNyaBHmQvRkDVTudHcFjBR0jgZGszsC2hlTrTBr4SZLGVa16m5SBkUC4K99pHHyXEml5sZBRo9kLJozErMf3mTLBgNBuImmlgDKK2o00/gvmjPS1mtaKmYdK6y6K7KUJbuUqPYO9R+Q1U7EF7zYF6djQJS74rx2wj2oWcp8XiHkGrG6tFqzsCYRHXjui9HGiku+054jRPyjx/qVn5eFX04+V9OiJq3JSEEjEpGMHsmactMJK7JrTTtaljKrxZ5ZpQLk7TxLndbx1nb187Paifiiezev6k6KlBimYGhf1fhCu2JA5k4bjkWvfhrmQpU7MB1zpw0P/e1kcUXCGkpLmfxvqoXoDtxYK9DIHCeH6m4SWsg3H3jzBs+iJi1SjWYz7d7NC0EQKCSEcC3Se7K0uJL5GSc28VyppPGKw1mddFimSqm9vKIGy0oqQzvadfWtWFZSGbo+7ziR2PASwwzql66qpA3qlw6gU6FSi1ExYgmTK2RKnC6uSJhHzVImxYRoFbq3g2XzxrjOrTBWJPsF1U2zZP+pd1tyX7QDLffJRXNG6v5dzIwNM66bRNdArojpnTekf8s/C4Qre0UF2RHHlXgFYNbkc8POTfKpayAIgN8roK1Dn+HCIwA905ybG/Uw8vzO+UDaxFAieeLNXVJm68aeK90dee4ZvHTHPHjfX7Fxn+pCasXGfbqOE4kNLzHMq+99pnpcap87bTj6ZqSEHeubkRJSvPSMb60SEuTe5F54G0xqbnaE/fh96tnqWO1W4blPWp27rFybSByMZPCVNgh4HkpLiysj5hh5ohzpXDdP6IyBnrukDEuLK+H3CaGFa0aPZIwe1jcspKRbcuS7xhvfgtCp3BHxjSgipKBJe94ZPZKZG9WDB6TH3PK/u7oOAFueXpjT25Gs8a5cDZhZyBiZdJL96rcttfMsbbzjRGLDU6J4Slx5RQ2Ofh8+xo9+3xp6mXnjmxeTSe5N7oVXQ1G5+CGcIdoZeFmLWmmBrEc5N6tY8TJPEomPR8X7Y0h2RtjfvPpQygVnfl4WFs0ZiWXzxoSSJcjnpaaWANrag7il8FwsmjMSg/qlo639lBLY1BIIO+fyDXvxXYP2HCWKwGVD+7oqSRdhDVE8FZc9d9rwCEWtb0YKPmPU/7OC2juhhTSuWbJ8d3WdI5vjrnR35Lln6IkXkHyrv6tvRS/F8UN16hXCWe0EIUfNlcPIAooXU8kb37zvk3uTe+G5ygLhbna//ttmimV1gFi8I1ruk2rv/JDsDOyurrPFdcapzJOEuyjdqV6PL6gSh1+2pwaD+qWHxhTP1ZoXE8mbl7SOf3HwuO46sR/uPqzrc0T80BEQsbS4Eis27sOJ1nBjh1PrcrV3Qgv53KAmy1mxalY3x12ppOlZBGtNeOUVNXh+bRU6Tvok1tW34vm1VaHvEYQWgqBeFFRaR/OUKF5iET2WLq3xrcfSbEWJJJzDqAWHFDRncOIdWb5hL0p3HkJQ7IyhKBhqrDahnTFwRNfESFZFpdI1JDuDqyjxrG1a7VrHWcqlGh2U/jZhcbM3Wmt7QLNsiVMbf65U0qxm1nr1vc8iXuSOgIhX3/uMJsEEICXJg+a2yIDklCR7vHdZGyzydq0F1fSrBocllgE6/einXzUYgL6XWSvLlh2WZiI2eAT1hRSrPANL4SesYfc7snzD3rAFblBE6G+qV0i4FWkeKa+oQdkefuyM1oKTNy9pHSdX/MQmEbJ3NjZ3aCZgc2pz3JVKGgB8cfA4jp30Tz7W0IovDh7XPYFGO96AiC4t7eoZo1jt0Ua+AFRzt+W9zLwsW1YtzUTsYO10y9vlCrpBt3kiRrAsAaU7DzGVNCqTQcQaebI0rUyMEvI4NuX4HZKdgbI9NYYyPkrHny2pZG6OEvHP9KsGq2ZGjzfaOoJYsXGfqpx2anPclUqanl1Jq64lhHn6ZqSo+gkrMxY6Bc/S5fMKqi4RyiLCLHjuinrQigGxGnOWn5eFLw4eDxv/UnpYwt3wdpuVCjotXDqxu3yA3WUq9CjfTl6fIHgk+TyqClJ5RY1ua72U4U5t/JbtqcHI87OYcZSseQ8AwHhPcgemo/qbel0KpJQ5Um0znuXBQESHpcWVodp6qSk+tLUHdKffdxtNLQEs37BXVd9wYnPclUoab1eSp8Ql+dTrLyT5aFvaDtyeeMXrATpU5hyvTm9InruiHViJOZNcU6RJJyhGBoETzmLWCtK7Z4rq79u7Z+cGh5qCTuirn8SLJZXD2whxmlhfn+h63DwhR1VmzV1Spvsckuxijd/d1XWhTI9qqM17c5eUqepoST4Bc6cNj1jvqeEVgGlXngNAvX6bVB5AqxAx4SzSeqWxuQNJPg9GDzsdpTsPG07g4QY27zgUtfWWK5U03q4kT4lL8nvR1hG5m5Lkd6YGDuEuWMkW9CZhiHVMF8/aQgu82GLFCrKPkUpYaqfYDPN4AKjZA9T2ZuwuU5Hs96K1PfLqyYw5h8pkENFGTUEqr6gxNOakOcjO8cv6TluHiPKKGnygI6lIQOycF4sKspHkP2Ux7N7NG3K1M6KM8pC8bSiezhxtHUFs2Xkorj1FniupxNLiSsfXh65U0njB9TwljmLSCC1SU3yqY0FeaDOWMV28mDNa4MUWK0oyT3Y5Oenb6S7oRlhxDmrtemSAEdQUNK12KpNBOEGyX2BuRioz00mbTXqRalkB9o5frXOt2LhPd/yStFkml83tMo8qO+XqP/63IPTvRJerTuEmBc2MO6z0eadd1V1ZzHrwgHRD7UZhBeNTkH58wMqEJ7WzfkapfdqV50TEp/m8QshdItbwCt/yimkTzmJFSeaNXT1FjQnriIwVAqudh9F3klewniDM8KO+P2AeUxajNupaLcpWsVbGb3lFDeYuKcOshZswd0kZhmRnMM9lJLOtR4BmMWHWu+gRgFsKz9U9fyb7hbD7IOIfq/GKUkIR+biWv2tWcOVq4Ksj6sU2We2GYf0gLtLsCTY9TvNrtvN+3vy8LPxiYm6YEvSLibmuchXMz8vCojkjsWzeGCyaMzKsb7TAiy1WlOSCoX0125UKOqEfI5tvrMWf2XIHRQXZqhs/rHeStxFDEGZguVMD4QoLYNyyJLkUAubHr2S9k9dOkxKOWHkXknwe5kJbXj9Ubd785eRzQ1mTeRtkggDcND434j4IoqklEDaulZsiZnGluyNvAjVaa0hJrHU0r0dAQOUGvHpvoItzvKndULsa8ZyiPtYxc10dK/VQpIxQlJnWfvTUN5Rwwt1QVMh05d9K4lkGEe6EZxGoq2/F3CVlKCrINuVaLf+8mfFrNuEIDy2LoLSs0po3pURQbR3B0PpSKisgZauU2leXVqO1PUAJnuIcvTVIWeOAh115AiwpaWvWrMEzzzwDALjssstw7733WuqMXoymO3YbsyblqmYZmjUp15bz25FCnnA3tMCLHVaV5BnjcjBjXI5qeQZlUpJExe6U+oAxxcvuwqOrS6sjYmckywO9p4SbqKtvxdLiSuQOTEfDiXZDssaqhd+oqzgrdtQI8nUhK3mKXBYExVOyQFLglImiiPimswwAf9zLx4GEkTnajrFi2t2xubkZDz74IJYvX441a9bgk08+wbZt2yx3CDjl88tqZykb8aKE5OdlYfSwvqEdHo8AjB7W17bJvCOgPnhY7W4j3n9fIvHRcke1AqXgjyTZL2DZvDHczxlxA7bb3dDtyXyk+Jkpd62xNV6CcBesbKJqVB04HuZm2L2bV7OWqB0u9UZdxdXix+26poRWIijWcSK+aWzu0PWbKl2EAfW5g5VwyiPAsqw1bUkLBAIIBoNobm7Gaaedho6ODiQn2xNHoVbjSt5+gmGiZLW7DSmtrLzO1Qc7T9Vd4O0I89wlraagjzUCI7hEas8dmI6qA8cjjucOTAdAmdOI+MUti/pooHeXvLVd1GV1M2rhtNMa7WaZQ4Wzuw6sbKIslG6G8vqP3bt5IQgCGps7bHOpN2rBVr7TRlE7t7LGpdYGi9ESBYS7sKOIudrvrxyX0gZHh8KdIijCsqw1raSlpqbiN7/5DSZMmICUlBRceOGFGD58uO7vZ2SkMo+pKSBSe2ZmmmZMWWZmmuZ1o3Hc4xEQVLkHj0dAZmYaXnv/Q1XXmNfe/xxTLj8bF+VlYW35gYjvX5SXhczMNO7zsdr/WB9vYizcmpo7kJmZhkfuLMD8p7Zi1xd1oWMXDMrAA78eBQCYOTkPj7++I+yF8XkFzJycx72+E8TimkZwe/+ijZZssoMt27/GS+uqcPRYM37YMwU3TcjF5SP6AwAye6ag9pg7isI7SWZmGm69ZkjEe2qGbkne0Biecnkaplx+th1dNMTMyXl4cuWusEVyst8bM5kj5+2t5aqWgre3fhmTZ8Uj1s/L7WjJJ1ZBdxbf1beGPW+n358pl6ehR1o3pvwD1OXjCwvGY8v2ryPeMS08HgF3/HRo5LnX7wudQ0sB65bkxfNrq5jH004mKms4ER4Lz9pEJ+xHWmsLAgAxPK9Est+LK37cD+9/ctDw5oWcZL8nQiYpx1FTSwBej6D6/lmVtaaVtL1792LVqlXYvHkz0tLScPfdd+O5557D7NmzdX2/rq5RVZHhoYzhcOK41q5obW0DsxZJsl9AbW0D876CQRG1tQ0RL7VEw4l21NY24L2Pv1I9/t7HX+E6jrtBNJ6P08d7MZ5/r5PPHwB+c90FABAW1yP9v76hRTWIv76hJfQZ5W6aU4k31OKO3IRd/fN4BMeVm2hhVjbpQWnVqD3WjH+8sRP1DS3Iz8vC1aPO6hIxabW1DcgbkI5fTMw1vUsu0dIWiPp7rSRvQDpuGj8Yq0ur8V19K3qdvHbegPSYv/8spb/2WHPM+6bEKXnZVeST0QoS8jlVD3a8X3kD0vHwrflhbfL3lyUfe6R1g98noFVHfrAknwc3T8iJeP9eKKnQvWBvadP+XMOJdnTv5oVHEBCUPXhBsCeWLtHpm5GCQ3XWNiSl90AUAY8ghJVQEcUgzvhh95BcNjvHtLYHMfPP68PGuto40lLMtWQtTzaZjknbunUr8vPzkZGRgaSkJBQVFeGjjz4ye7qoMnqYehpsqX1Idobqcan9pvG5EWmdpdSs0r/V0FuHra1D/cdmtScavOfPQyuIH1BPA2xXulSC0IIX/9DVUvDLY/usEuv3WrqXd/421dY4RatQXUWChZEYs2i8Xyz5uGLjPjy5cpcuxUcrvtRu18WmlkCYggYAHQERoihSvUsOVhU0Jcrfoa1DxLKSzgR9i+aMtCTvlGPd6Diycm3ToygnJwfbtm3DiRMnIIoiNm3ahPPPP990R6LJoH7pmu27q+tUj0vt+XlZmD353LDAwdkna20A/FTQXb2YNi8xDO/58+AF8fMWygThFHoSTNipuHQl6L1Wh+oqEiykNYuywLSa4hWN94slH5taArosYLxETtHamGhqCSD7jB5hyeGI6BMQESoybVVBl491VhK71BSf7bLWtLvjqFGjUFlZiaKiIvj9fpx//vn41a9+ZbojYZ1SCcCT2u3gxXXqfsYvrqtCfl6W7oWU2Z1SI/V8EhFeYhOrmdJ4Qfxuz8RGJC5GE0wk+7263XOcSGsvx0xdpWhC77U68iB3uSumWyx9ROyQlDE9iWWi8X45LWPUEpf4vALEoBjmfZPk80AQREvJ1uTJzShEzRx6a5lp0dQS0HUOPfGcUjKZVpX0/V6hMxspYG8NW0t10n71q1/ZppjJ+cXEXDxbXBkWBCicbLcDnjuh1UxdvDplVs+f6MWwrT4fXgYpN2diIxIbo9nN9Cpo0bDCFxVk47mSqgi3EruwukCj95qNtKno9hhZIrpIlgGWhUy+uNR6P6UC2U5lf0zye3S5Os5auElzYczKACtvS03xQRRFNLUkdlyw2xk9rC9mjMtxZONR+t3VMpnyvre6tFrViJTSzRcaX3ZugCWk0ywv5oyHVfeQn+T20WznnZ+la4XaE9wUZzUmjVcDidx/iFghH5sC+PW59CoY0Xj1vzh43DEFDVB/L61+n95rgmBTV9+q20Km9X7aFZ/GmrunXXmO7hpwvL6o1biU2m4pPBdt7UHVTfbUFB+c2gvT4ySW5POEygwlMqkpPtxSeC5mjMtx7Bp19a1hv3l7h8hV0KS5hPW+OJUoxpIlzSlWbNwXkWZfPNmuR0OVftzSk7XIPAJQMLSv7h/daL0dJXpi2qTzq7mfsEzjUjsrY7XFTNauwWpMGqDtjmr19yUIKxixahQVZGNZSSX33ZbHPjjlWlO685AzJz6J8r3Uey+ShwK910RXZ/Swvti8Q/97qhUCoNwg4tUsU7O+mYE1d/dI64YXSipQV9/KdQM32xdW4WrpWTi1EJ81+VzNDISpKT5cmNMbHzgsg2OFV+h8BlbHjtxlUcs6Jh/beoqVy+cS1u/klMeGK5U0lv+oEd/UGeNyMGNcjmn3DisxZxQbYY1oPD87C9kShJMIHoG7AyMpM07GPvDObYeCKH8v9bi5eAVg+lWDVb9PEF0N5Qa1RGqKD80tHRFxV5KVWa8LtvR+sd5NJ9Y4Utp/aUP7lsLwxbydfXHr2i3Z78VHVUdMb8Srxd25he7dvJh+1WCm3DYyr4giwhJuKUs6AJFjW89vKy/4bjRkwSquVNJ4sGpQpKboux3Wj25XSBcvJk05cFiBuokK7/lTbAlBdMLyf1cS63fDiaQlWpOzAFACDIJQgbVBzatxZsQCHa05Ws9ayUpflM/EjkQVEsl+AakpSaFzN5xoU82HkOwXuLUxjSiJyufhETr/a3OZgpbRIzlM+WFhZONPuYbX411hNBY62h4bcamkiYy4CFa7koKh6i4BBUP1xazxEBhR/FK7VirbRFhw8JRU3vNXc/HyCsZquhBEIqBn8ojGu6Gn8Kjdmdm0Jud3/jaVEmAQhApKy5O0gOSFABhZe0TLmqBnraTWF6BTdmolNFFTAH1eAV6+40IEST5PxLO4aXx4rPGv/7ZZ9budGSS1L6hXtkoKg/y+gqI7a+xKmRJ5yo6ReUVtzuCNbdb40SKaHhuuTByS5FNXcqR2q+6QM8blYPSwvmFxHFImGQk9dUNYsPyWpXanTeosg2C0cj9Ov2pwRCCs3C1pxriciADY3IHpYc9fUGyJKP8miK6AHu+AaLiwTLrkLG5CI6uJP5RQEWaCMIa84LQI5wq685Jz2YXeckjyvig/x7p/NQWwIyAipZvPkIzxCJ2Ko9wTSO1ZmE3nLym/vLlA+tyKjfsMKRyxons3r67i6EbmFTNzgzR+WPNbrOcbV1rSkvxetHVEKjpJJ7P72OGuKLkEqGHVHZFnfnfaVeB0xq736RkptpyftdMkKWY8c3B5RQ2qv6kP+271N/Uor6gJBWYqXbw6AmLCWBoJQi96vQNeWl/l6Lvx4roqrgWcl1jAKNH2/SeIeCeaXjrRsCboXStJfVErWsy6f60sfU/85jIA0FUEWR4PLMknu56Lcu30/NoqVfd3eSkBvcYKpfUvmnTWoRPQ1hHeV7XfSm09OSQ7A2V7amybG6RruHG+caWSxrNE8bIfAmyTv/K4mhJhVdAVFWRHvEw+rxD6sXv3TFF98Xv3tEeJOsxwS2K1G2XW5HOxtLhStV1CS4Dznq9bg3cJItronXD17tIK4DnWqNPWIerKmit/73/9t1LVDGx6U2mzJufVpdV4triSYtIIQkGizZ1GN2qM3L+WAihfH7JQMxa0dQSxYuM+W+KVlN5drOy38mvc+fgHus8/8vwsfLz3W0cyVkrPVq0OndSmtoYEOn8rZb07tfXkoH7ptsaF8bKuxwpXKmm83ROriTl4x+0QdMpi0/K/9311XPU7rHYlvPtnLcL0Ls6S/YLqoi/Z32kqsxo4yXu+lDiEIDqxM5DdjuQeWh4ISm4aPxjPllSG1XAThM52vcgn566ecIkgeFhNquY2jC6cjawdWArgkOwMzRglXpHlppZASGbLZZRRPqo6gt3VdaprLDU5+MXB44YUri07DzlWX1MtIYjyN+MpwTz57oQl10h5nGjhypg0XjFjK4k59ByXlB0lrHYlr773WcTgF8XOdoBvCWQVNpTaeTFfPFgCW2r3+9TvU96uVhBSL7xYEypKSxCdsGQdC17cWLRRTjC8CUcrFpgntwmiq2M1qVq8Y2TtwIqr211dp+kG+PHeb0Of14Mko/R6EEg0tQRU47VYctBoHUujQ2L0sL667lnvhoCeWDOS7y61pEkvgVr7jHE5lhNz8I53BNRfUHm7lrskr388fD4vAipuQj6fPUVbeYLcav958FwYqCgtQXSi952TrNyDB6Sj6sDxiOODB6Tb2Ct9rC6tjohdDYhguo1Hw8OBIBIZO2rMugmj1nOjawc1awzLDU9CkslGsgJ2FuC2tlMmKSwseWd3jUzJnVLu1q5Wd0yOzytg2pXn6Dq/3hjmri7fXamk8ZQEq4k5eMdZ8R1Su9NuN2pxHMp2K6ZeniB32t1QjyClorQEwXZfUiLJpm+PqcedstqdxKhSxYtVJTdogtAm0d4RM/kBrK4d9KZ8V1vHtLYHVOU175w+r6CrHqZ0nWgoLs/eOyaiTXnP3bt5IQgCmpo7TMVwyX8rVpIW1tjVk77fKLxcFrHAlUoajyHZGapZxiR3SJ6lxmrWMJ7gSPIJqnUppBICTgtS3vV52TGjkVWNlDCC4KPXTUmSHU5Zm4y66kjfMZI4hNd3yvZIENok2jsSC+s5z0ImD3tRrmPULE3S89ey0CX7PegI8K2danXQnEBrLaq2drMjhsvI2HXCUOLWmGdXxqTxYsJ2V9epHpfa5b7GAiLrVvBqfPDqjPEERxJjESK1Ox1zdfOE3Ih7EE62A/yYON7zIwgiOuhxU5LLDidqi3kEwVCyDwk9HgFyeH0nuUQQ2iTaOxKLWonSM1SLreLF/mutLbXWtUbkvFZdODuIlVJvpPaeE/HJbo15dqUlbfpVg/FscWVYNkIBp14OvQUOtbK0aFlyeNkReZYwnrum06k+ee6EdtSZIwjCeXiuLR6hM5Wy9G6r7UZ6hU7FyGhmRwEwLJv0pK5moWcn1Y3ZtwjCTSTSOxIry6D0DM241LHWltOvGoxlJZVhcbqS0seSmWpp9uXXsJqtV4lRt0G73QP1elg5YWF1a8yzK5U0APAqfHS9snSGTrsL8s7PExy8FPmAtiAdPUy9aOzoYX1134PWYOdZ0txq9iWIrgbPtSUoAmV7ajCoX3rYOy+PGWhtD5pK+vPO36YaWuTxgsolWJtBlDCIIAg5sa5dZWdYBu9e1NaUPCsoq1ySGnpqZKqlzmcRy3WiEzqAW+M5TStpK1euxMsvvxz6++DBg5g6dSr+9Kc/We7U6tLqiCDKjoAYivlyenfFavZBXokAHnqKxlqBZ0mzWsybIAh7UMoaVgFV+bupDMZuajG+E2gw83+oj3riJAqGsjebKFaVIAg5iWQZZN2L2Q2qNp0KGgB0T/Eh2e9lWoaMKiOxXCc6oQO4NZ7TtJJ2/fXX4/rrrwcAfP7557j99tvxP//zP7Z0imd2dHrH1Wr2QTtS2BspGmsUniXNrWZfguiKyGUNy73FzDurVdzaTFklnnywe7OJIAgiUTCzQWVETDc2d+CJ31ymmdzECLFcJzqhA8TaasvCFnfH++67D7/97W/Rq1cvO06ny+zo9I6rlfO71WwqYbVEAUEQscHou2n2XfcIwJS71hiaqLSuZcSNhiAIguDD8opSQ56ACbCu4MR6neiEDmDFautESQAAEESLpei3bduGv/3tb1i1apXlzkg89eZOrC0/ENE+MX8gfn3dUNuu4xRbtn+NJ1fuCstiluz34n+uvwCXj+gfw551wuuf2/tPEF0Vo++mmXddid53n+QGQRBE9GCtlZXxZ07IYZL3p3DyWVhW0u68806MHTsWkydPNvS9urpGBBlbAFpF7YzuyMbKj1mvVu3W/rmxqJ8abvdT7yr983gEZGSk2tCj2KMlm+zE7LM3umOn911nxbwB+mWvU7uJStz8XlHfzONU/7qifHL7b22URLofO+9l+Ya9EfkLBvVLj4ocjpd1olGM/j5WdBaebLLk7tjW1oaPP/4YCxcutHKaCBIhJsrtAfC8/iVSsC5BJBJGZYvedx0wHvNmtW8EQRCEeVj5C6Ihh2md2ImTOoulYtb79u3DmWeeidNOO81yR+TEooAhQRBEV4dkL0EQBEHox8l505KS9vXXXyMry35tvaggG0m+8K65IRUmQRBEIkOylyAIgiD04+S8acndceLEiZg4caLlTiihoqZ8ohX7QRBEYqOUJSPPz8Lu6rqEizMgCIKIJYkaw9XVcVJnsSUFvxNQbAObWFZ6JwgicVCTJWV7anDzhBxMufzsLh1nQBAEYRe0bktsnNJZLLk7ErFBq9I7QRCEXkiWEARBOA/JWsIMpKTFIYmQ/ZIgiNhDsoQgCMJ5SNYSZiAlLQ6hDGwEQdgByRKCIAjnIVlLmIGUtDiEMrARBGEHJEsIgiCch2QtYQbXJg4h2FD2S4Ig7IBkCUEQhPPIZS1ldyT0QkpanELZLwmCsAOSJQRBEM4jydrMzDTKnEvogtwdCYIgCIIgCIIgXETMLGkej5CQ1zID9c8a1D9r2NE/t9+jEUg2ncLN/aO+mcPNfQOc6Z/b79kIRu4lke4bSKz7SaR7Aeh+nLqOIIqiGJWeEARBEARBEARBEFzI3ZEgCIIgCIIgCMJFkJJGEARBEARBEAThIkhJIwiCIAiCIAiCcBGkpBEEQRAEQRAEQbgIUtIIgiAIgiAIgiBcBClpBEEQBEEQBEEQLoKUNIIgCIIgCIIgCBdBShpBEARBEARBEISLICWNIAiCIAiCIAjCRZCSRhAEQRAEQRAE4SLiVklrbGzE5MmTcfDgQQDA6tWrMXHiRBQWFuKBBx5AR0cHAKC0tBSFhYUoLCzEXXfdhaamJgDAoUOHcOONN2L8+PH49a9/HWp3S//eeustjBo1ClOnTsXUqVPx2GOP2da3J598EpMmTcKkSZPwyCOPAAC2bduGwsJCjB07NuxaVVVVKCoqwrhx4/DHP/4x1G8nn58d/XPL85O45557sHr16tDfbnl+rP45+fyISPngJtTGj1t4/PHHMXHiREyaNAnPP/98rLujysMPP4x58+bFuhsRzJgxA5MmTQq907t27Yp1l0Js2rQJRUVFmDBhAh544IFYdyeuKS4uxsSJEzF27Fi88sorse6OIZRy0ei87xbsWMO4CTW5G8/3IyGX1a69HzEO2blzpzh58mQxLy9P/Prrr8Xq6mrx0ksvFY8cOSKKoiguWLBAXLZsmfj999+LF198sfj555+LoiiKzzzzjPiXv/xFFEVR/NWvfiWWlJSIoiiKTz75pPjII4+4qn/333+/WFxcbFufJMrKysSf/exnYmtrq9jW1ibedNNNYnFxsVhQUCB+9dVXYnt7uzhr1ixxy5YtoiiK4qRJk8QdO3aIoiiKv//978VXXnlFFEXnnp9d/XPL86upqRFvvfVWcciQIeKqVatC53HL82P1z6nnR0TKBzehNn7efffdWHdLFEVR/Pe//y3ecMMNYnt7u9jc3CyOHj1arK6ujnW3wti2bZt40UUXiffee2+suxJGMBgUR40aJba3t8e6KxF89dVX4qhRo8TDhw+LbW1t4rRp00LyiTBGTU2NOHr0aPHYsWNiU1OTWFhYGFpfuB2lXGxubjY877sBu9YwbkFN7lZVVcXt/UjIZbWbx1pcWtLeeOMNLFiwAL179wYA7Nu3D0OHDg39PXr0aLz33nvYv38/+vbti0GDBoW1t7e34+OPP8a4ceMAAEVFRVi/fr1r+gcAe/bswVtvvYXCwkLcfffd+P77723pW2ZmJubNm4ekpCT4/X5kZ2dj//79GDhwIPr37w+fz4fCwkKsX78e33zzDVpaWjB06FAAp56Tk8/Pjv4B7nh+QOeu5hVXXIEJEyaEzuGW58fqH+Dc8yMi5YObUBs/hw4dinW3AAA/+clP8NJLL8Hn86Gurg6BQACnnXZarLsV4vjx43jsscdw2223xborEfz3v/8FAMyaNQtTpkzByy+/HOMenWLjxo2YOHEisrKy4Pf78dhjj+GCCy6Idbfikm3btuHiiy9Geno6TjvtNIwbN87WtY2TKOXi7t27Dc/7bsCuNYxbUJO79fX1cXs/QKSsdvNYi0sl7cEHH8SPf/zj0N85OTnYtWsXDh8+jEAggPXr1+Po0aM488wzUVNTg7179wIA1q1bh6NHj+LYsWNITU2Fz+cD0PlSHTlyxDX9k/o0Z84cvPPOOzj99NNx//3329K3s88+OzTg9u/fj3Xr1kEQBGRmZoY+07t3bxw5cgTffvttWLv0nJx8fnb0T/p3rJ8fAMyePRvXX3992Dnc8vxY/ZP65MTzIyLlg5tQGz8FBQWx7ZQMv9+PJ554ApMmTUJ+fj769OkT6y6F+NOf/oTf/va36NGjR6y7EkF9fT3y8/Pxz3/+Ey+88AJee+01lJWVxbpbAIADBw4gEAjgtttuw9SpU7FixQr84Ac/iHW34hLlnCiX9W5HKRdZ96I177sBu9YwbkIpd+P1t5FQymo3309cKmlKzjrrLNx111349a9/jRtvvBGDBw+G3+9Hjx498PDDD+P//u//cO2116J3797w+/0QRRGCIISdQ/l3LPsHAP/85z8xYsQICIKA2bNn48MPP7S1T59//jlmzZqFe+65B/379w+7f+n5BINB1fZoPD8r/QPc8fxYuOX5aeH08yPcjXz8nHnmmbHuThh33nknysvLcfjwYbzxxhux7g4AYOXKlTj99NORn58f666oMmzYMDzyyCNIS0tDr169cN1116G0tDTW3QIABAIBlJeX46GHHsLrr7+O3bt346233op1t+ISrTkx3mDdS7zco9U1jNuQy939+/fH7f2oyWo3j7WEUNJaW1sxZMgQvP3223jttdfQp08f9O/fH4FAAFlZWVi5ciVWrVqF3Nxc9O/fH7169UJDQwMCgQAAoLa21lHXI6P9a2howAsvvBD6viiK8Hq9tvVn+/btmDlzJu666y5cc801yMrKQm1tbei49DyU7UePHkXv3r0df35W++eW58fCLc+PhdPPj3A3yvHjFqqrq1FVVQUASElJwdixY7Fv374Y96qTtWvXoqysDFOnTsUTTzyBTZs24aGHHop1t0J88sknKC8vD/0timLIkh9rfvjDHyI/Px+9evVCt27dcOWVV2L37t2x7lZcYlTWuxmj876bsLqGcRNqcvff//533N6PmqxeuXKla+8nIZS0EydOYObMmWhsbERbWxtefvllTJw4EYIgYNasWThy5AhEUcQLL7yAiRMnwu/348c//jHWrl0LAHj77bdx2WWXuaZ/p512Gp599tlQ9q2XX34ZV111lS19OXz4MG6//XYsXrwYkyZNAgBccMEF+PLLL0NuJyUlJbjssstwxhlnIDk5Gdu3bwcArFmzBpdddpmjz8+O/rnl+bFwy/Nj4eTzI9yN2vhxCwcPHsT8+fPR1taGtrY2vP/++xgxYkSsuwUAeP7551FSUoI1a9bgzjvvxJgxY/CHP/wh1t0K0dDQgEceeQStra1obGzEW2+95Zp3evTo0di6dSvq6+sRCATw4YcfIi8vL9bdiksuueQSlJeX47vvvkNzczPeffddR9c2TmJ03ncLdqxh3ISa3L3hhhvi9n7UZPWzzz7r2vtxx1aaRXr27Inbb78dP/vZz9DR0YHJkyejsLAQAHD//fdj9uzZaGtrQ35+Pn75y18CABYsWIB58+bhqaeewumnn45HH33UNf3zer34+9//jvvuuw8tLS0488wzbUuF/dxzz6G1tRULFy4Mtd1www1YuHAh7rjjDrS2tqKgoADjx48HACxevBjz589HY2Mj8vLycNNNNwFw7vnZ0T83PT8Wbnl+ajj5/Ah3wxo/06ZNi2GvOikoKMDu3btx9dVXw+v1YuzYsa5TJN3K6NGjsWvXLlx99dUIBoOYPn06hg0bFutuAehcwM6ePRvTp09He3s7Ro4ciWuvvTbW3YpL+vTpg9/+9re46aab0N7ejuuuuw5DhgyJdbdMkZycbHhd4gbsWmO5BZbc7dWrV1zejxpuHmuCKIpiVK9IEARBEARBEARBMEkId0eCIAiCIAiCIIhEgZQ0giAIgiAIgiAIF0FKGkEQBEEQBEEQhIsgJY0gCIIgCIIgCMJFkJJGEARBEARBEAThIkhJIwiCIAiCIAiCcBGkpBEEQRAEQRAEQbiI/x/cAwcK3fYudAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting again\n",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize =(15,3)) #sharey -> share 'Price' as y\n",
    "ax1.scatter(data_cleaned['Year'],data_cleaned['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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## dropping original price in data frame since it is not needed more as we are getting linear relation in log price value\n",
    "data_cleaned= data_cleaned.drop(['Price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi colinearity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
       "       'Registration', 'Year', 'log_price'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking multi colineairy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn does not have a built-in way to check for multicollinearity\n",
    "# one of the main reasons is that this is an issue well covered in statistical frameworks and not in ML ones\n",
    "## Variance inflation factor\n",
    "\n",
    "# Here's the relevant module\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# To make this as easy as possible to use, we declare a variable where we put\n",
    "# all features where we want to check for multicollinearity\n",
    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
    "\n",
    "# we create a new data frame which will include all the VIFs\n",
    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "# Finally, I like to include names so it is easier to explore the result\n",
    "vif[\"Features\"] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.703266</td>\n",
       "      <td>Mileage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.323287</td>\n",
       "      <td>Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.400348</td>\n",
       "      <td>EngineV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF Features\n",
       "0   3.703266  Mileage\n",
       "1  10.323287     Year\n",
       "2   7.400348  EngineV"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since Year has the highest VIF, I will remove it from the model\n",
    "# This will drive the VIF of other variables down!!! \n",
    "# So even if EngineV seems with a high VIF, too, once 'Year' is gone that will no longer be the case\n",
    "data_no_multicollinearity = data_cleaned.drop(['Year'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating dmmy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To include the categorical data in the regression, let's create dummies\n",
    "# There is a very convenient method called: 'get_dummies' which does that seemlessly\n",
    "# It is extremely important that we drop one of the dummies, alternatively we will introduce multicollinearity\n",
    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3906</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3907 rows × 18 columns</p>\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",
       "3902      163      2.5   9.350102          0                    0   \n",
       "3903       35      1.6   9.792556          0                    0   \n",
       "3904        9      3.0  11.736069          0                    1   \n",
       "3905        1      3.5   8.779557          1                    0   \n",
       "3906      124      2.0   9.510445          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",
       "3902                 0              0             0                 1   \n",
       "3903                 0              0             1                 0   \n",
       "3904                 0              0             0                 0   \n",
       "3905                 0              0             0                 0   \n",
       "3906                 0              0             0                 1   \n",
       "\n",
       "      Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  \\\n",
       "0              0           0           1           0         0   \n",
       "1              0           0           0           0         1   \n",
       "2              0           0           1           0         0   \n",
       "3              0           0           0           0         0   \n",
       "4              0           0           0           0         0   \n",
       "...          ...         ...         ...         ...       ...   \n",
       "3902           0           0           0           0         1   \n",
       "3903           0           0           1           0         0   \n",
       "3904           0           0           1           0         0   \n",
       "3905           0           0           1           0         0   \n",
       "3906           0           0           0           0         1   \n",
       "\n",
       "      Engine Type_Gas  Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                   0                  0                   1                 1  \n",
       "1                   0                  0                   0                 1  \n",
       "2                   1                  0                   0                 1  \n",
       "3                   0                  0                   1                 1  \n",
       "4                   0                  0                   1                 1  \n",
       "...               ...                ...                 ...               ...  \n",
       "3902                0                  0                   0                 1  \n",
       "3903                0                  0                   1                 1  \n",
       "3904                0                  0                   0                 1  \n",
       "3905                0                  0                   1                 1  \n",
       "3906                0                  0                   0                 1  \n",
       "\n",
       "[3907 rows x 18 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reaaranging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## manually \n",
    "data_with_dummies.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_preprocessed=data_with_dummies[cols]\n",
    "data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression model\n",
    "\n",
    "# declare inputs and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets=data_preprocessed['log_price']\n",
    "\n",
    "## data pre processed with out log price\n",
    "inputs= data_preprocessed.drop(['log_price'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the scaling module\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler= StandardScaler()\n",
    "scaler.fit(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_scaled= scaler.transform(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the module for the split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the variables with an 80-20 split and some random state\n",
    "# To have the same split as mine, use random_state = 365\n",
    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, targets, test_size=0.2, random_state=365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg= LinearRegression()\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's check the outputs of the regression\n",
    "# store them in y_hat as this is the 'theoretical' name of the predictions\n",
    "y_hat = reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The simplest way to compare the targets (y_train) and the predictions (y_hat) is to plot them on a scatter plot\n",
    "# The closer the points to the 45-degree line, the better the prediction\n",
    "plt.scatter(y_train, y_hat)\n",
    "\n",
    "#  name the axes\n",
    "plt.xlabel('Targets (y_train)',size=18)\n",
    "plt.ylabel('Predictions (y_hat)',size=18)\n",
    "\n",
    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
    "# We want the x-axis and the y-axis to be the same\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjvbb\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 52,
     "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": [
    "# Another useful check of our model is a residual plot\n",
    "# We can plot the PDF of the residuals and check for anomalies\n",
    "sns.distplot(y_train - y_hat)\n",
    "\n",
    "# Include a title\n",
    "plt.title(\"Residuals PDF\", size=18)\n",
    "\n",
    "# In the best case scenario this plot should be normally distributed\n",
    "# In our case we notice that there are many negative residuals (far away from the mean)\n",
    "# Given the definition of the residuals (y_train - y_hat), negative values imply\n",
    "# that y_hat (predictions) are much higher than y_train (the targets)\n",
    "# This is for thought to improve our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7520453281718504"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## finding weights and bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.43753512973232"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the bias (intercept) of the regression\n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.47713375,  0.25279341,  0.02659834,  0.03600588, -0.12726982,\n",
       "       -0.16670307, -0.04848545, -0.07180363, -0.14024984, -0.09622117,\n",
       "       -0.19162974, -0.10890609, -0.15699644, -0.12248327, -0.03067591,\n",
       "       -0.14136631,  0.31071348])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the weights (coefficients) of the regression\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>-0.477134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EngineV</td>\n",
       "      <td>0.252793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brand_BMW</td>\n",
       "      <td>0.026598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brand_Mercedes-Benz</td>\n",
       "      <td>0.036006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brand_Mitsubishi</td>\n",
       "      <td>-0.127270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brand_Renault</td>\n",
       "      <td>-0.166703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brand_Toyota</td>\n",
       "      <td>-0.048485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brand_Volkswagen</td>\n",
       "      <td>-0.071804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Body_hatch</td>\n",
       "      <td>-0.140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Body_other</td>\n",
       "      <td>-0.096221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Body_sedan</td>\n",
       "      <td>-0.191630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body_vagon</td>\n",
       "      <td>-0.108906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Body_van</td>\n",
       "      <td>-0.156996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Engine Type_Gas</td>\n",
       "      <td>-0.122483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Engine Type_Other</td>\n",
       "      <td>-0.030676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Engine Type_Petrol</td>\n",
       "      <td>-0.141366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Registration_yes</td>\n",
       "      <td>0.310713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Features   Weights\n",
       "0               Mileage -0.477134\n",
       "1               EngineV  0.252793\n",
       "2             Brand_BMW  0.026598\n",
       "3   Brand_Mercedes-Benz  0.036006\n",
       "4      Brand_Mitsubishi -0.127270\n",
       "5         Brand_Renault -0.166703\n",
       "6          Brand_Toyota -0.048485\n",
       "7      Brand_Volkswagen -0.071804\n",
       "8            Body_hatch -0.140250\n",
       "9            Body_other -0.096221\n",
       "10           Body_sedan -0.191630\n",
       "11           Body_vagon -0.108906\n",
       "12             Body_van -0.156996\n",
       "13      Engine Type_Gas -0.122483\n",
       "14    Engine Type_Other -0.030676\n",
       "15   Engine Type_Petrol -0.141366\n",
       "16     Registration_yes  0.310713"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a regression summary where we can compare them with one-another\n",
    "reg_summary = pd.DataFrame(inputs.columns.values, columns=['Features'])\n",
    "reg_summary['Weights'] = reg.coef_\n",
    "reg_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
       "       'Mitsubishi'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the different categories in the 'Brand' variable\n",
    "data_cleaned['Brand'].unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once we have trained and fine-tuned our model, we can proceed to testing it\n",
    "# Testing is done on a dataset that the algorithm has never seen\n",
    "# Luckily we have prepared such a dataset\n",
    "# Our test inputs are 'x_test', while the outputs: 'y_test' \n",
    "# We SHOULD NOT TRAIN THE MODEL ON THEM, we just feed them and find the predictions\n",
    "# If the predictions are far off, we will know that our model overfitted\n",
    "y_hat_test = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot with the test targets and the test predictions\n",
    "# You can include the argument 'alpha' which will introduce opacity to the graph\n",
    "plt.scatter(y_test, y_hat_test, alpha=0.2)\n",
    "plt.xlabel('Targets (y_test)',size=18)\n",
    "plt.ylabel('Predictions (y_hat_test)',size=18)\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>2497.123713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7456.534893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13496.814274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7573.105796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16502.817902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Prediction\n",
       "0   2497.123713\n",
       "1   7456.534893\n",
       "2  13496.814274\n",
       "3   7573.105796\n",
       "4  16502.817902"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, let's manually check these predictions\n",
    "# To obtain the actual prices, we take the exponential of the log_price\n",
    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns=['Prediction'])\n",
    "df_pf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2497.123713</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7456.534893</td>\n",
       "      <td>7900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13496.814274</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7573.105796</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16502.817902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>1866.980599</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>22010.975542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>7804.297565</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>6923.705483</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>9581.790221</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>782 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction  Target\n",
       "0     2497.123713     NaN\n",
       "1     7456.534893  7900.0\n",
       "2    13496.814274     NaN\n",
       "3     7573.105796     NaN\n",
       "4    16502.817902     NaN\n",
       "..            ...     ...\n",
       "777   1866.980599     NaN\n",
       "778  22010.975542     NaN\n",
       "779   7804.297565     NaN\n",
       "780   6923.705483     NaN\n",
       "781   9581.790221     NaN\n",
       "\n",
       "[782 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also include the test targets in that data frame (so we can manually compare them)\n",
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf\n",
    "\n",
    "## lot of missing vaues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.495542\n",
       "1    9.287301\n",
       "2    9.609116\n",
       "3    9.305651\n",
       "4    9.740910\n",
       "Name: log_price, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After displaying y_test, we find what the issue is\n",
    "# The old indexes are preserved (recall earlier in that code we made a note on that)\n",
    "# The code was: data_cleaned = data_4.reset_index(drop=True)\n",
    "\n",
    "# Therefore, to get a proper result, we must reset the index and drop the old indexing\n",
    "y_test = y_test.reset_index(drop=True)\n",
    "\n",
    "# Check the result\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2497.123713</td>\n",
       "      <td>1800.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7456.534893</td>\n",
       "      <td>10800.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13496.814274</td>\n",
       "      <td>14900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7573.105796</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16502.817902</td>\n",
       "      <td>16999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>1866.980599</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>22010.975542</td>\n",
       "      <td>21335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>7804.297565</td>\n",
       "      <td>13500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>6923.705483</td>\n",
       "      <td>6200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>9581.790221</td>\n",
       "      <td>13300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>782 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction   Target\n",
       "0     2497.123713   1800.0\n",
       "1     7456.534893  10800.0\n",
       "2    13496.814274  14900.0\n",
       "3     7573.105796  11000.0\n",
       "4    16502.817902  16999.0\n",
       "..            ...      ...\n",
       "777   1866.980599   1950.0\n",
       "778  22010.975542  21335.0\n",
       "779   7804.297565  13500.0\n",
       "780   6923.705483   6200.0\n",
       "781   9581.790221  13300.0\n",
       "\n",
       "[782 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# again putting values after resetiing indexes\n",
    "df_pf['Target'] = np.exp(y_test)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additionally, we can calculate the difference between the targets and the predictions\n",
    "# Note that this is actually the residual (we already plotted the residuals)\n",
    "df_pf['Residual'] = df_pf['Target'] - df_pf['Prediction']\n",
    "\n",
    "# Since OLS is basically an algorithm which minimizes the total sum of squared errors (residuals),\n",
    "# this comparison makes a lot of sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2497.123713</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>-697.123713</td>\n",
       "      <td>38.729095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7456.534893</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>3343.465107</td>\n",
       "      <td>30.958010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13496.814274</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>1403.185726</td>\n",
       "      <td>9.417354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7573.105796</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>3426.894204</td>\n",
       "      <td>31.153584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16502.817902</td>\n",
       "      <td>16999.0</td>\n",
       "      <td>496.182098</td>\n",
       "      <td>2.918890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>1866.980599</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>83.019401</td>\n",
       "      <td>4.257405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>22010.975542</td>\n",
       "      <td>21335.0</td>\n",
       "      <td>-675.975542</td>\n",
       "      <td>3.168388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>7804.297565</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>5695.702435</td>\n",
       "      <td>42.190388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>6923.705483</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>-723.705483</td>\n",
       "      <td>11.672669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>9581.790221</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>3718.209779</td>\n",
       "      <td>27.956465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction   Target     Residual  Difference%\n",
       "0     2497.123713   1800.0  -697.123713    38.729095\n",
       "1     7456.534893  10800.0  3343.465107    30.958010\n",
       "2    13496.814274  14900.0  1403.185726     9.417354\n",
       "3     7573.105796  11000.0  3426.894204    31.153584\n",
       "4    16502.817902  16999.0   496.182098     2.918890\n",
       "..            ...      ...          ...          ...\n",
       "777   1866.980599   1950.0    83.019401     4.257405\n",
       "778  22010.975542  21335.0  -675.975542     3.168388\n",
       "779   7804.297565  13500.0  5695.702435    42.190388\n",
       "780   6923.705483   6200.0  -723.705483    11.672669\n",
       "781   9581.790221  13300.0  3718.209779    27.956465\n",
       "\n",
       "[782 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, it makes sense to see how far off we are from the result percentage-wise\n",
    "# Here, we take the absolute difference in %, so we can easily order the data frame\n",
    "df_pf['Difference%'] = np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>782.000000</td>\n",
       "      <td>782.000000</td>\n",
       "      <td>782.000000</td>\n",
       "      <td>782.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17668.626792</td>\n",
       "      <td>20717.216803</td>\n",
       "      <td>3048.590012</td>\n",
       "      <td>35.024687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15260.172350</td>\n",
       "      <td>25654.851301</td>\n",
       "      <td>15487.661137</td>\n",
       "      <td>79.023527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1195.372625</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>-44009.355675</td>\n",
       "      <td>0.044431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7642.370756</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>-2106.719386</td>\n",
       "      <td>10.244297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12647.391012</td>\n",
       "      <td>12500.000000</td>\n",
       "      <td>453.938710</td>\n",
       "      <td>23.326711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22630.721407</td>\n",
       "      <td>23324.750000</td>\n",
       "      <td>3734.118831</td>\n",
       "      <td>38.876238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99473.692128</td>\n",
       "      <td>249999.000000</td>\n",
       "      <td>179168.433265</td>\n",
       "      <td>1833.723153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Prediction         Target       Residual  Difference%\n",
       "count    782.000000     782.000000     782.000000   782.000000\n",
       "mean   17668.626792   20717.216803    3048.590012    35.024687\n",
       "std    15260.172350   25654.851301   15487.661137    79.023527\n",
       "min     1195.372625     800.000000  -44009.355675     0.044431\n",
       "25%     7642.370756    7500.000000   -2106.719386    10.244297\n",
       "50%    12647.391012   12500.000000     453.938710    23.326711\n",
       "75%    22630.721407   23324.750000    3734.118831    38.876238\n",
       "max    99473.692128  249999.000000  179168.433265  1833.723153"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>32385.60</td>\n",
       "      <td>32400.00</td>\n",
       "      <td>14.40</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>46574.25</td>\n",
       "      <td>46500.00</td>\n",
       "      <td>-74.25</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>17600.14</td>\n",
       "      <td>17639.47</td>\n",
       "      <td>39.33</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>54866.55</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>133.45</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>8278.72</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>21.28</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>8323.24</td>\n",
       "      <td>8350.00</td>\n",
       "      <td>26.76</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>12944.41</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>-44.41</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>18930.10</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>68.90</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>32359.77</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>140.23</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>12839.50</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>60.50</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>2981.97</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>18.03</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>8957.99</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-57.99</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>44665.52</td>\n",
       "      <td>44999.00</td>\n",
       "      <td>333.48</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>21157.30</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>-157.30</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>29131.76</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>-231.76</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>5045.80</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-46.80</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>11590.02</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>109.98</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>10096.43</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-96.43</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>7221.34</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>78.66</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>12796.88</td>\n",
       "      <td>12650.00</td>\n",
       "      <td>-146.88</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>39515.25</td>\n",
       "      <td>39999.00</td>\n",
       "      <td>483.75</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2864.16</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>35.84</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>8393.47</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>106.53</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8782.09</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>117.91</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>32024.79</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>475.21</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>8070.30</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>129.70</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>11808.93</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>191.07</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>5083.27</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-83.27</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>28182.91</td>\n",
       "      <td>28700.00</td>\n",
       "      <td>517.09</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4584.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-84.00</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>34326.78</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>673.22</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>15084.98</td>\n",
       "      <td>15400.00</td>\n",
       "      <td>315.02</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>3818.32</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>81.68</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>22998.30</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>-498.30</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>21477.39</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>-477.39</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>30783.64</td>\n",
       "      <td>31500.00</td>\n",
       "      <td>716.36</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>30174.93</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>-674.93</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>11585.33</td>\n",
       "      <td>11300.00</td>\n",
       "      <td>-285.33</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>55006.33</td>\n",
       "      <td>56500.00</td>\n",
       "      <td>1493.67</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>30291.88</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>-791.88</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>12014.38</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-314.38</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>11771.17</td>\n",
       "      <td>12100.00</td>\n",
       "      <td>328.83</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>3696.69</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>103.31</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>9662.94</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>-262.94</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>6512.16</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>187.84</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>21283.46</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>616.54</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>1594.71</td>\n",
       "      <td>1550.00</td>\n",
       "      <td>-44.71</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>6585.71</td>\n",
       "      <td>6399.00</td>\n",
       "      <td>-186.71</td>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16502.82</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>496.18</td>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>16500.91</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>498.09</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>12800.16</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>399.84</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>7729.91</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-229.91</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>26282.33</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>-782.33</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>19860.22</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>639.78</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>5664.88</td>\n",
       "      <td>5850.00</td>\n",
       "      <td>185.12</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>22010.98</td>\n",
       "      <td>21335.00</td>\n",
       "      <td>-675.98</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>7641.91</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>-241.91</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>12396.44</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>-396.44</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>2295.70</td>\n",
       "      <td>2222.00</td>\n",
       "      <td>-73.70</td>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>7233.33</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-233.33</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>9199.74</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-299.74</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>24828.01</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-828.01</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>11155.32</td>\n",
       "      <td>10777.00</td>\n",
       "      <td>-378.32</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>12628.30</td>\n",
       "      <td>12200.00</td>\n",
       "      <td>-428.30</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>24881.61</td>\n",
       "      <td>25800.00</td>\n",
       "      <td>918.39</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>12138.51</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-438.51</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>18861.74</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>738.26</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>32714.54</td>\n",
       "      <td>34000.00</td>\n",
       "      <td>1285.46</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>18478.99</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>-678.99</td>\n",
       "      <td>3.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>2547.70</td>\n",
       "      <td>2650.00</td>\n",
       "      <td>102.30</td>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>7679.36</td>\n",
       "      <td>7990.00</td>\n",
       "      <td>310.64</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>28827.18</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>1172.82</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>3546.13</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>153.87</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>1866.98</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>83.02</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>22329.56</td>\n",
       "      <td>23333.00</td>\n",
       "      <td>1003.44</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>5216.28</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-216.28</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>37783.29</td>\n",
       "      <td>39500.00</td>\n",
       "      <td>1716.71</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>17694.00</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>806.00</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>11475.65</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>523.35</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>10413.44</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>486.56</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>81101.31</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>3898.69</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>17936.07</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>863.93</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>8869.55</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>430.45</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18111.21</td>\n",
       "      <td>19000.00</td>\n",
       "      <td>888.79</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>13924.90</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-624.90</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>12514.56</td>\n",
       "      <td>11950.00</td>\n",
       "      <td>-564.56</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>2951.76</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>148.24</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>9384.02</td>\n",
       "      <td>8950.00</td>\n",
       "      <td>-434.02</td>\n",
       "      <td>4.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>16457.54</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>842.46</td>\n",
       "      <td>4.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4353.85</td>\n",
       "      <td>4150.00</td>\n",
       "      <td>-203.85</td>\n",
       "      <td>4.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2098.78</td>\n",
       "      <td>1999.00</td>\n",
       "      <td>-99.78</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>7125.42</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>374.58</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>17667.73</td>\n",
       "      <td>18600.00</td>\n",
       "      <td>932.27</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>9143.26</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-443.26</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>22072.25</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>-1072.25</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>10303.57</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-503.57</td>\n",
       "      <td>5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>21980.88</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-1080.88</td>\n",
       "      <td>5.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>5830.47</td>\n",
       "      <td>6150.00</td>\n",
       "      <td>319.53</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>24408.20</td>\n",
       "      <td>25749.75</td>\n",
       "      <td>1341.55</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>73713.84</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>-3713.84</td>\n",
       "      <td>5.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>27347.60</td>\n",
       "      <td>28950.00</td>\n",
       "      <td>1602.40</td>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>14062.56</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>837.44</td>\n",
       "      <td>5.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>8298.71</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>501.29</td>\n",
       "      <td>5.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8996.80</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-496.80</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>15528.65</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>971.35</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>21257.95</td>\n",
       "      <td>22600.00</td>\n",
       "      <td>1342.05</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>9431.16</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-531.16</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>9356.09</td>\n",
       "      <td>9950.00</td>\n",
       "      <td>593.91</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>10050.47</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>649.53</td>\n",
       "      <td>6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>12210.23</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>789.77</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>13157.71</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>-757.71</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>14363.15</td>\n",
       "      <td>15300.00</td>\n",
       "      <td>936.85</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>17553.78</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>1146.22</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>9198.22</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>601.78</td>\n",
       "      <td>6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>20592.06</td>\n",
       "      <td>19400.00</td>\n",
       "      <td>-1192.06</td>\n",
       "      <td>6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>11257.44</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>741.56</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>12186.89</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>812.11</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>6010.25</td>\n",
       "      <td>5650.00</td>\n",
       "      <td>-360.25</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>15640.82</td>\n",
       "      <td>14700.00</td>\n",
       "      <td>-940.82</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6922.10</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-422.10</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>8313.98</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>-513.98</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>24803.48</td>\n",
       "      <td>26600.00</td>\n",
       "      <td>1796.52</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>8435.19</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-535.19</td>\n",
       "      <td>6.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>14202.70</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-902.70</td>\n",
       "      <td>6.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44260.45</td>\n",
       "      <td>47500.00</td>\n",
       "      <td>3239.55</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>12574.92</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>925.08</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3863.93</td>\n",
       "      <td>4150.00</td>\n",
       "      <td>286.07</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>12720.69</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>-820.69</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8280.07</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>619.93</td>\n",
       "      <td>6.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>16624.33</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>1275.67</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>66841.73</td>\n",
       "      <td>72000.00</td>\n",
       "      <td>5158.27</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>11408.45</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>891.55</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>11797.96</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>-797.96</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>4915.01</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>384.99</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>11957.75</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>942.25</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>3977.13</td>\n",
       "      <td>4299.00</td>\n",
       "      <td>321.87</td>\n",
       "      <td>7.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>12659.37</td>\n",
       "      <td>13700.00</td>\n",
       "      <td>1040.63</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2860.82</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>239.18</td>\n",
       "      <td>7.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>15862.55</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>1337.45</td>\n",
       "      <td>7.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>22869.16</td>\n",
       "      <td>24800.00</td>\n",
       "      <td>1930.84</td>\n",
       "      <td>7.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>16720.90</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>-1220.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>9170.44</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-670.44</td>\n",
       "      <td>7.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>32263.63</td>\n",
       "      <td>29900.00</td>\n",
       "      <td>-2363.63</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>14568.44</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>-1068.44</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>9660.89</td>\n",
       "      <td>10499.00</td>\n",
       "      <td>838.11</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>7883.70</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-583.70</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>15172.35</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>1327.65</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>55660.66</td>\n",
       "      <td>51500.00</td>\n",
       "      <td>-4160.66</td>\n",
       "      <td>8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>10841.92</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>958.08</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>16076.95</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>1423.05</td>\n",
       "      <td>8.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>44090.14</td>\n",
       "      <td>48000.00</td>\n",
       "      <td>3909.86</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2650.79</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-200.79</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>11687.21</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-887.21</td>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>13316.10</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>-1016.10</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>28706.06</td>\n",
       "      <td>26500.00</td>\n",
       "      <td>-2206.06</td>\n",
       "      <td>8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>4164.08</td>\n",
       "      <td>4550.00</td>\n",
       "      <td>385.92</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>8869.60</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>830.40</td>\n",
       "      <td>8.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>49077.89</td>\n",
       "      <td>45200.00</td>\n",
       "      <td>-3877.89</td>\n",
       "      <td>8.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>5668.03</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>531.97</td>\n",
       "      <td>8.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>17177.84</td>\n",
       "      <td>15820.35</td>\n",
       "      <td>-1357.49</td>\n",
       "      <td>8.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>6670.59</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>629.41</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>8406.65</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>793.35</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>11416.96</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>1083.04</td>\n",
       "      <td>8.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>22280.29</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-1780.29</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>15979.55</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>1520.45</td>\n",
       "      <td>8.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>9239.26</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-739.26</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>13235.41</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>1264.59</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>11671.08</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>1128.92</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>5741.60</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>558.40</td>\n",
       "      <td>8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>9419.35</td>\n",
       "      <td>8650.00</td>\n",
       "      <td>-769.35</td>\n",
       "      <td>8.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>7187.35</td>\n",
       "      <td>7899.00</td>\n",
       "      <td>711.65</td>\n",
       "      <td>9.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>12428.92</td>\n",
       "      <td>11400.00</td>\n",
       "      <td>-1028.92</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>12538.66</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-1038.66</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>7968.26</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-668.26</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>35877.31</td>\n",
       "      <td>39500.00</td>\n",
       "      <td>3622.69</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>11349.69</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>1150.31</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>7643.74</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-644.74</td>\n",
       "      <td>9.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2855.46</td>\n",
       "      <td>3150.00</td>\n",
       "      <td>294.54</td>\n",
       "      <td>9.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>6343.22</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>656.78</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>7987.45</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-687.45</td>\n",
       "      <td>9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13496.81</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>1403.19</td>\n",
       "      <td>9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>17747.36</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>1852.64</td>\n",
       "      <td>9.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>14022.07</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>-1222.07</td>\n",
       "      <td>9.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>7235.05</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>-635.05</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>13924.90</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>-1224.90</td>\n",
       "      <td>9.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>11738.51</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>1261.49</td>\n",
       "      <td>9.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>10292.10</td>\n",
       "      <td>11400.00</td>\n",
       "      <td>1107.90</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>39261.24</td>\n",
       "      <td>43500.00</td>\n",
       "      <td>4238.76</td>\n",
       "      <td>9.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>39261.24</td>\n",
       "      <td>43500.00</td>\n",
       "      <td>4238.76</td>\n",
       "      <td>9.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>10990.16</td>\n",
       "      <td>12179.00</td>\n",
       "      <td>1188.84</td>\n",
       "      <td>9.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>11968.39</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>-1068.39</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>12523.66</td>\n",
       "      <td>13899.00</td>\n",
       "      <td>1375.34</td>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>8820.13</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>979.87</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>5352.31</td>\n",
       "      <td>5950.00</td>\n",
       "      <td>597.69</td>\n",
       "      <td>10.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>20484.79</td>\n",
       "      <td>22800.00</td>\n",
       "      <td>2315.21</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>5389.13</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>610.87</td>\n",
       "      <td>10.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>34243.84</td>\n",
       "      <td>38233.00</td>\n",
       "      <td>3989.16</td>\n",
       "      <td>10.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2327.72</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>272.28</td>\n",
       "      <td>10.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>13038.02</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-1238.02</td>\n",
       "      <td>10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>32203.77</td>\n",
       "      <td>35999.00</td>\n",
       "      <td>3795.23</td>\n",
       "      <td>10.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>29321.56</td>\n",
       "      <td>26500.00</td>\n",
       "      <td>-2821.56</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>2767.09</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-267.09</td>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>7943.91</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>956.09</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>13828.59</td>\n",
       "      <td>15499.00</td>\n",
       "      <td>1670.41</td>\n",
       "      <td>10.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>27741.38</td>\n",
       "      <td>31111.00</td>\n",
       "      <td>3369.62</td>\n",
       "      <td>10.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>23348.59</td>\n",
       "      <td>26200.00</td>\n",
       "      <td>2851.41</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>26502.55</td>\n",
       "      <td>23900.00</td>\n",
       "      <td>-2602.55</td>\n",
       "      <td>10.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>12975.50</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-1275.50</td>\n",
       "      <td>10.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>16283.68</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>2016.32</td>\n",
       "      <td>11.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>27400.89</td>\n",
       "      <td>30800.00</td>\n",
       "      <td>3399.11</td>\n",
       "      <td>11.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>7905.44</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>994.56</td>\n",
       "      <td>11.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>7192.45</td>\n",
       "      <td>8100.00</td>\n",
       "      <td>907.55</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>3669.98</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-369.98</td>\n",
       "      <td>11.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>11262.63</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>1437.37</td>\n",
       "      <td>11.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>8030.37</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-830.37</td>\n",
       "      <td>11.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>22310.28</td>\n",
       "      <td>19999.00</td>\n",
       "      <td>-2311.28</td>\n",
       "      <td>11.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>12049.19</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-1249.19</td>\n",
       "      <td>11.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>25467.03</td>\n",
       "      <td>28800.00</td>\n",
       "      <td>3332.97</td>\n",
       "      <td>11.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>11385.55</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-1185.55</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>11043.32</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>1456.68</td>\n",
       "      <td>11.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>6923.71</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>-723.71</td>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>11392.30</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-1192.30</td>\n",
       "      <td>11.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>11951.89</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-1251.89</td>\n",
       "      <td>11.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>6792.13</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>907.87</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>9326.40</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>1273.60</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>7734.70</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-834.70</td>\n",
       "      <td>12.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>8520.67</td>\n",
       "      <td>7600.00</td>\n",
       "      <td>-920.67</td>\n",
       "      <td>12.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>19295.88</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>-2095.88</td>\n",
       "      <td>12.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>14038.09</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-1538.09</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>7491.85</td>\n",
       "      <td>8550.00</td>\n",
       "      <td>1058.15</td>\n",
       "      <td>12.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>15766.21</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>2233.79</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1485.69</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>214.31</td>\n",
       "      <td>12.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>52842.42</td>\n",
       "      <td>60500.00</td>\n",
       "      <td>7657.58</td>\n",
       "      <td>12.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6724.33</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>975.67</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>4453.49</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>646.51</td>\n",
       "      <td>12.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>39466.48</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>-4466.48</td>\n",
       "      <td>12.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>9507.16</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1392.84</td>\n",
       "      <td>12.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>26942.45</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>3957.55</td>\n",
       "      <td>12.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>10453.02</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>1545.98</td>\n",
       "      <td>12.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>25412.16</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>-2912.16</td>\n",
       "      <td>12.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>7214.23</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>1085.77</td>\n",
       "      <td>13.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>17779.77</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>2720.23</td>\n",
       "      <td>13.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>17570.86</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>-2070.86</td>\n",
       "      <td>13.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>43010.52</td>\n",
       "      <td>49700.00</td>\n",
       "      <td>6689.48</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>4142.82</td>\n",
       "      <td>3650.00</td>\n",
       "      <td>-492.82</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4031.17</td>\n",
       "      <td>3550.00</td>\n",
       "      <td>-481.17</td>\n",
       "      <td>13.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>60887.45</td>\n",
       "      <td>70500.00</td>\n",
       "      <td>9612.55</td>\n",
       "      <td>13.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>60426.09</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>9572.91</td>\n",
       "      <td>13.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>16829.58</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>-2029.58</td>\n",
       "      <td>13.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2717.33</td>\n",
       "      <td>3150.00</td>\n",
       "      <td>432.67</td>\n",
       "      <td>13.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>9053.04</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>1446.96</td>\n",
       "      <td>13.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>9684.50</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-1184.50</td>\n",
       "      <td>13.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>26560.07</td>\n",
       "      <td>23300.00</td>\n",
       "      <td>-3260.07</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>9884.07</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>1615.93</td>\n",
       "      <td>14.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>11406.13</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-1407.13</td>\n",
       "      <td>14.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2233.47</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>366.53</td>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>9360.65</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1539.35</td>\n",
       "      <td>14.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>9946.29</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-1246.29</td>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>14760.83</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>-1860.83</td>\n",
       "      <td>14.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>25760.73</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>-3260.73</td>\n",
       "      <td>14.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>5128.13</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>871.87</td>\n",
       "      <td>14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>52842.42</td>\n",
       "      <td>62000.00</td>\n",
       "      <td>9157.58</td>\n",
       "      <td>14.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>4772.50</td>\n",
       "      <td>5600.00</td>\n",
       "      <td>827.50</td>\n",
       "      <td>14.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>17008.34</td>\n",
       "      <td>20007.00</td>\n",
       "      <td>2998.66</td>\n",
       "      <td>14.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>21749.35</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-2849.35</td>\n",
       "      <td>15.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>9216.03</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-1216.03</td>\n",
       "      <td>15.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>36866.69</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>-4866.69</td>\n",
       "      <td>15.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>24297.20</td>\n",
       "      <td>28660.00</td>\n",
       "      <td>4362.80</td>\n",
       "      <td>15.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>19059.83</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>3440.17</td>\n",
       "      <td>15.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5675.47</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>1024.53</td>\n",
       "      <td>15.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>63512.54</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>11487.46</td>\n",
       "      <td>15.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>16837.72</td>\n",
       "      <td>19900.00</td>\n",
       "      <td>3062.28</td>\n",
       "      <td>15.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>12256.26</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>2243.74</td>\n",
       "      <td>15.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>18725.79</td>\n",
       "      <td>16200.00</td>\n",
       "      <td>-2525.79</td>\n",
       "      <td>15.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>24297.20</td>\n",
       "      <td>28793.04</td>\n",
       "      <td>4495.84</td>\n",
       "      <td>15.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>27752.79</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-3752.79</td>\n",
       "      <td>15.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12887.29</td>\n",
       "      <td>15300.00</td>\n",
       "      <td>2412.71</td>\n",
       "      <td>15.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>26024.70</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>4875.30</td>\n",
       "      <td>15.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>4632.83</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-632.83</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>34474.68</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>6525.32</td>\n",
       "      <td>15.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>25526.53</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>-3526.53</td>\n",
       "      <td>16.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>30984.62</td>\n",
       "      <td>36900.00</td>\n",
       "      <td>5915.38</td>\n",
       "      <td>16.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>8712.12</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-1212.12</td>\n",
       "      <td>16.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>4174.43</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>825.57</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>8163.91</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>1636.09</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>4331.20</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>868.80</td>\n",
       "      <td>16.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>27431.12</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-3931.12</td>\n",
       "      <td>16.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20794.97</td>\n",
       "      <td>24996.00</td>\n",
       "      <td>4201.03</td>\n",
       "      <td>16.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>58184.07</td>\n",
       "      <td>69990.00</td>\n",
       "      <td>11805.93</td>\n",
       "      <td>16.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>7850.10</td>\n",
       "      <td>9450.00</td>\n",
       "      <td>1599.90</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>1988.73</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>-288.73</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1618.78</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>331.22</td>\n",
       "      <td>16.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>5621.16</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-821.16</td>\n",
       "      <td>17.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>12008.21</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>2491.79</td>\n",
       "      <td>17.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>9010.37</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1889.63</td>\n",
       "      <td>17.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>7179.84</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>1520.16</td>\n",
       "      <td>17.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>10148.91</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>2151.09</td>\n",
       "      <td>17.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>5875.92</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-875.92</td>\n",
       "      <td>17.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>10402.70</td>\n",
       "      <td>8850.00</td>\n",
       "      <td>-1552.70</td>\n",
       "      <td>17.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>4697.05</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>1002.95</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>16351.92</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>-2451.92</td>\n",
       "      <td>17.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>20531.76</td>\n",
       "      <td>24950.00</td>\n",
       "      <td>4418.24</td>\n",
       "      <td>17.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7405.80</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>1594.20</td>\n",
       "      <td>17.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>17912.85</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>-2712.85</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>15682.71</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-2382.71</td>\n",
       "      <td>17.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12617.30</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-1917.30</td>\n",
       "      <td>17.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>7433.90</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>-1133.90</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>24784.11</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>-3784.11</td>\n",
       "      <td>18.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2699.74</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>600.26</td>\n",
       "      <td>18.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>14431.45</td>\n",
       "      <td>12200.00</td>\n",
       "      <td>-2231.45</td>\n",
       "      <td>18.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>19589.33</td>\n",
       "      <td>23999.00</td>\n",
       "      <td>4409.67</td>\n",
       "      <td>18.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>17596.06</td>\n",
       "      <td>21600.00</td>\n",
       "      <td>4003.94</td>\n",
       "      <td>18.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>5098.66</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-798.66</td>\n",
       "      <td>18.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3558.86</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-558.86</td>\n",
       "      <td>18.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>56894.51</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>13104.49</td>\n",
       "      <td>18.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>16514.09</td>\n",
       "      <td>20350.00</td>\n",
       "      <td>3835.91</td>\n",
       "      <td>18.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14418.61</td>\n",
       "      <td>17777.00</td>\n",
       "      <td>3358.39</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>7619.45</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-1219.45</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14296.36</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>-2296.36</td>\n",
       "      <td>19.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>13702.85</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-2202.85</td>\n",
       "      <td>19.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>25858.70</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>6141.30</td>\n",
       "      <td>19.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>23865.25</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-3865.25</td>\n",
       "      <td>19.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>7865.52</td>\n",
       "      <td>9750.00</td>\n",
       "      <td>1884.48</td>\n",
       "      <td>19.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>5491.62</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>-891.62</td>\n",
       "      <td>19.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2054.19</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>495.81</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>5797.27</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>1402.73</td>\n",
       "      <td>19.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>6599.46</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>1600.54</td>\n",
       "      <td>19.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>5737.80</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-937.80</td>\n",
       "      <td>19.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11435.33</td>\n",
       "      <td>9555.00</td>\n",
       "      <td>-1880.33</td>\n",
       "      <td>19.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>5387.94</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-887.94</td>\n",
       "      <td>19.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>23948.80</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-3948.80</td>\n",
       "      <td>19.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>52165.19</td>\n",
       "      <td>65000.00</td>\n",
       "      <td>12834.81</td>\n",
       "      <td>19.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>22864.11</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>5635.89</td>\n",
       "      <td>19.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>26232.97</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>-4332.97</td>\n",
       "      <td>19.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>3835.81</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-635.81</td>\n",
       "      <td>19.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>23375.89</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-3875.89</td>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>8272.86</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-1372.86</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>33614.79</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>8385.21</td>\n",
       "      <td>19.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>30594.65</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>-5094.65</td>\n",
       "      <td>19.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>9040.04</td>\n",
       "      <td>11300.00</td>\n",
       "      <td>2259.96</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>6799.68</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>1700.32</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>8402.88</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-1403.88</td>\n",
       "      <td>20.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>9988.12</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>2511.88</td>\n",
       "      <td>20.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>13574.21</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>3425.79</td>\n",
       "      <td>20.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>8300.12</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-1400.12</td>\n",
       "      <td>20.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>52842.42</td>\n",
       "      <td>66500.00</td>\n",
       "      <td>13657.58</td>\n",
       "      <td>20.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>7689.64</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>2010.36</td>\n",
       "      <td>20.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>17266.67</td>\n",
       "      <td>14300.00</td>\n",
       "      <td>-2966.67</td>\n",
       "      <td>20.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>29596.84</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-5096.84</td>\n",
       "      <td>20.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>13775.20</td>\n",
       "      <td>17400.00</td>\n",
       "      <td>3624.80</td>\n",
       "      <td>20.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>34818.27</td>\n",
       "      <td>44000.00</td>\n",
       "      <td>9181.73</td>\n",
       "      <td>20.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>22546.67</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>5953.33</td>\n",
       "      <td>20.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>21262.95</td>\n",
       "      <td>26900.00</td>\n",
       "      <td>5637.05</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>56894.51</td>\n",
       "      <td>71999.00</td>\n",
       "      <td>15104.49</td>\n",
       "      <td>20.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>50014.22</td>\n",
       "      <td>63333.00</td>\n",
       "      <td>13318.78</td>\n",
       "      <td>21.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>11876.14</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-2076.14</td>\n",
       "      <td>21.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>37084.67</td>\n",
       "      <td>30600.00</td>\n",
       "      <td>-6484.67</td>\n",
       "      <td>21.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>7325.60</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>1974.40</td>\n",
       "      <td>21.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>33457.59</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>9042.41</td>\n",
       "      <td>21.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>9354.14</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>2545.86</td>\n",
       "      <td>21.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>33012.12</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>8987.88</td>\n",
       "      <td>21.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>9819.82</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>2680.18</td>\n",
       "      <td>21.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>15542.28</td>\n",
       "      <td>19800.00</td>\n",
       "      <td>4257.72</td>\n",
       "      <td>21.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>22658.74</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>6241.26</td>\n",
       "      <td>21.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>81504.43</td>\n",
       "      <td>103999.00</td>\n",
       "      <td>22494.57</td>\n",
       "      <td>21.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>5112.16</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-912.16</td>\n",
       "      <td>21.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>21063.59</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>5936.41</td>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>31575.04</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>8924.96</td>\n",
       "      <td>22.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>8911.60</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-1611.60</td>\n",
       "      <td>22.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>4884.12</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-884.12</td>\n",
       "      <td>22.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9283.26</td>\n",
       "      <td>7600.00</td>\n",
       "      <td>-1683.26</td>\n",
       "      <td>22.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>12952.57</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>-2352.57</td>\n",
       "      <td>22.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>18333.81</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-3333.81</td>\n",
       "      <td>22.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>25895.98</td>\n",
       "      <td>33300.00</td>\n",
       "      <td>7404.02</td>\n",
       "      <td>22.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>5208.39</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>1491.61</td>\n",
       "      <td>22.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>36938.43</td>\n",
       "      <td>47600.00</td>\n",
       "      <td>10661.57</td>\n",
       "      <td>22.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>14354.82</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>4145.18</td>\n",
       "      <td>22.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>13571.82</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>3928.18</td>\n",
       "      <td>22.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>10719.87</td>\n",
       "      <td>8750.00</td>\n",
       "      <td>-1969.87</td>\n",
       "      <td>22.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>18748.54</td>\n",
       "      <td>15300.00</td>\n",
       "      <td>-3448.54</td>\n",
       "      <td>22.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>8748.84</td>\n",
       "      <td>11300.00</td>\n",
       "      <td>2551.16</td>\n",
       "      <td>22.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>14724.19</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>-2724.19</td>\n",
       "      <td>22.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>15837.95</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>-2937.95</td>\n",
       "      <td>22.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>11499.81</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>3400.19</td>\n",
       "      <td>22.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>11429.08</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-2129.08</td>\n",
       "      <td>22.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>9157.19</td>\n",
       "      <td>7450.00</td>\n",
       "      <td>-1707.19</td>\n",
       "      <td>22.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>14627.81</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>4371.19</td>\n",
       "      <td>23.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>2694.73</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>805.27</td>\n",
       "      <td>23.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>11260.33</td>\n",
       "      <td>9150.00</td>\n",
       "      <td>-2110.33</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>9766.11</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>2933.89</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>27961.76</td>\n",
       "      <td>22700.00</td>\n",
       "      <td>-5261.76</td>\n",
       "      <td>23.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>43092.38</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>-8192.38</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>3210.62</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-610.62</td>\n",
       "      <td>23.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>7268.54</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>2231.46</td>\n",
       "      <td>23.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>20782.19</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>-3982.19</td>\n",
       "      <td>23.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>39608.99</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>-7608.99</td>\n",
       "      <td>23.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>22185.01</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-4285.01</td>\n",
       "      <td>23.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>12407.95</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-2408.95</td>\n",
       "      <td>24.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>9695.36</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>3104.64</td>\n",
       "      <td>24.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>6807.64</td>\n",
       "      <td>8990.00</td>\n",
       "      <td>2182.36</td>\n",
       "      <td>24.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>22363.72</td>\n",
       "      <td>17990.00</td>\n",
       "      <td>-4373.72</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>18011.91</td>\n",
       "      <td>14474.00</td>\n",
       "      <td>-3537.91</td>\n",
       "      <td>24.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>30514.90</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-6014.90</td>\n",
       "      <td>24.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6262.09</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>2037.91</td>\n",
       "      <td>24.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>29022.66</td>\n",
       "      <td>38500.00</td>\n",
       "      <td>9477.34</td>\n",
       "      <td>24.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>15789.30</td>\n",
       "      <td>20999.00</td>\n",
       "      <td>5209.70</td>\n",
       "      <td>24.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>3000.61</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>999.39</td>\n",
       "      <td>24.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>8197.00</td>\n",
       "      <td>6557.85</td>\n",
       "      <td>-1639.15</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>52424.48</td>\n",
       "      <td>41900.00</td>\n",
       "      <td>-10524.48</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>52287.36</td>\n",
       "      <td>69900.00</td>\n",
       "      <td>17612.64</td>\n",
       "      <td>25.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>30633.37</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>10366.63</td>\n",
       "      <td>25.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>12691.54</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>4308.46</td>\n",
       "      <td>25.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>6270.06</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>2129.94</td>\n",
       "      <td>25.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>3132.72</td>\n",
       "      <td>2499.00</td>\n",
       "      <td>-633.72</td>\n",
       "      <td>25.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10441.59</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>3558.41</td>\n",
       "      <td>25.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>29730.04</td>\n",
       "      <td>23700.00</td>\n",
       "      <td>-6030.04</td>\n",
       "      <td>25.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>24577.81</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>8422.19</td>\n",
       "      <td>25.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>16748.08</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>5751.92</td>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20601.52</td>\n",
       "      <td>16400.00</td>\n",
       "      <td>-4201.52</td>\n",
       "      <td>25.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5129.05</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>1770.95</td>\n",
       "      <td>25.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>17912.87</td>\n",
       "      <td>14250.00</td>\n",
       "      <td>-3662.87</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>4463.14</td>\n",
       "      <td>3550.00</td>\n",
       "      <td>-913.14</td>\n",
       "      <td>25.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>6607.63</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>2292.37</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>32142.37</td>\n",
       "      <td>25555.00</td>\n",
       "      <td>-6587.37</td>\n",
       "      <td>25.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>16397.35</td>\n",
       "      <td>22100.00</td>\n",
       "      <td>5702.65</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>12225.99</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>4274.01</td>\n",
       "      <td>25.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>8117.41</td>\n",
       "      <td>10990.00</td>\n",
       "      <td>2872.59</td>\n",
       "      <td>26.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>12000.29</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-2500.29</td>\n",
       "      <td>26.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>7879.54</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>2820.46</td>\n",
       "      <td>26.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>16823.57</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-3523.57</td>\n",
       "      <td>26.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>12144.02</td>\n",
       "      <td>9599.00</td>\n",
       "      <td>-2545.02</td>\n",
       "      <td>26.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>2214.64</td>\n",
       "      <td>1750.00</td>\n",
       "      <td>-464.64</td>\n",
       "      <td>26.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>3668.67</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>1331.33</td>\n",
       "      <td>26.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>18122.50</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>6577.50</td>\n",
       "      <td>26.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>4585.23</td>\n",
       "      <td>6250.00</td>\n",
       "      <td>1664.77</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>50566.22</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>-10666.22</td>\n",
       "      <td>26.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>4942.75</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>-1042.75</td>\n",
       "      <td>26.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>5492.72</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>2007.28</td>\n",
       "      <td>26.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>16101.21</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>-3401.21</td>\n",
       "      <td>26.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>6147.58</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>2252.42</td>\n",
       "      <td>26.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>23993.37</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-5093.37</td>\n",
       "      <td>26.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>11466.33</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>4233.67</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>79597.64</td>\n",
       "      <td>109000.00</td>\n",
       "      <td>29402.36</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>8748.44</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>3250.56</td>\n",
       "      <td>27.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>10566.75</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>3933.25</td>\n",
       "      <td>27.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>2404.13</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>895.87</td>\n",
       "      <td>27.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>8650.39</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-1850.39</td>\n",
       "      <td>27.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>9371.55</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>3528.45</td>\n",
       "      <td>27.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>48593.61</td>\n",
       "      <td>66900.00</td>\n",
       "      <td>18306.39</td>\n",
       "      <td>27.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>5735.32</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>2164.68</td>\n",
       "      <td>27.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>35035.92</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>-7535.92</td>\n",
       "      <td>27.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>11357.82</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-2457.82</td>\n",
       "      <td>27.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>12635.41</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>-2735.41</td>\n",
       "      <td>27.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>53461.65</td>\n",
       "      <td>74000.00</td>\n",
       "      <td>20538.35</td>\n",
       "      <td>27.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>27942.98</td>\n",
       "      <td>38764.00</td>\n",
       "      <td>10821.02</td>\n",
       "      <td>27.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>15777.64</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>6122.36</td>\n",
       "      <td>27.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>9581.79</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>3718.21</td>\n",
       "      <td>27.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>22418.13</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-4918.13</td>\n",
       "      <td>28.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>15069.33</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>5930.67</td>\n",
       "      <td>28.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>7004.03</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>2795.97</td>\n",
       "      <td>28.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5993.19</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>2406.81</td>\n",
       "      <td>28.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>13256.48</td>\n",
       "      <td>10300.00</td>\n",
       "      <td>-2956.48</td>\n",
       "      <td>28.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13918.17</td>\n",
       "      <td>10799.00</td>\n",
       "      <td>-3119.17</td>\n",
       "      <td>28.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>10451.57</td>\n",
       "      <td>14700.00</td>\n",
       "      <td>4248.43</td>\n",
       "      <td>28.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>8522.67</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>-1922.67</td>\n",
       "      <td>29.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>11636.05</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-2636.05</td>\n",
       "      <td>29.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19280.80</td>\n",
       "      <td>27300.00</td>\n",
       "      <td>8019.20</td>\n",
       "      <td>29.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>8312.14</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>3487.86</td>\n",
       "      <td>29.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>18204.03</td>\n",
       "      <td>25900.00</td>\n",
       "      <td>7695.97</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>27110.47</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-6210.47</td>\n",
       "      <td>29.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>16003.99</td>\n",
       "      <td>22800.00</td>\n",
       "      <td>6796.01</td>\n",
       "      <td>29.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>8960.83</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-2060.83</td>\n",
       "      <td>29.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>8061.27</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>3438.73</td>\n",
       "      <td>29.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>23262.50</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-5362.50</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>11372.29</td>\n",
       "      <td>8750.00</td>\n",
       "      <td>-2622.29</td>\n",
       "      <td>29.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>13261.97</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-3061.97</td>\n",
       "      <td>30.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>7554.19</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>3245.81</td>\n",
       "      <td>30.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>12378.40</td>\n",
       "      <td>17700.00</td>\n",
       "      <td>5321.60</td>\n",
       "      <td>30.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>16911.70</td>\n",
       "      <td>24193.65</td>\n",
       "      <td>7281.95</td>\n",
       "      <td>30.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3491.61</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>1508.39</td>\n",
       "      <td>30.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>24473.76</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>-5673.76</td>\n",
       "      <td>30.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>69773.42</td>\n",
       "      <td>99999.00</td>\n",
       "      <td>30225.58</td>\n",
       "      <td>30.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>11462.31</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-2662.31</td>\n",
       "      <td>30.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>24759.82</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>10740.18</td>\n",
       "      <td>30.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5431.80</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>2368.20</td>\n",
       "      <td>30.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>20790.25</td>\n",
       "      <td>29900.00</td>\n",
       "      <td>9109.75</td>\n",
       "      <td>30.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>18946.58</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>-4446.58</td>\n",
       "      <td>30.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>8689.90</td>\n",
       "      <td>6650.00</td>\n",
       "      <td>-2039.90</td>\n",
       "      <td>30.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>6331.07</td>\n",
       "      <td>9150.00</td>\n",
       "      <td>2818.93</td>\n",
       "      <td>30.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7456.53</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>3343.47</td>\n",
       "      <td>30.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>11528.14</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-2728.14</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>24143.66</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>10856.34</td>\n",
       "      <td>31.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>6198.36</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>2800.64</td>\n",
       "      <td>31.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>6886.26</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>3112.74</td>\n",
       "      <td>31.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7573.11</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>3426.89</td>\n",
       "      <td>31.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>39232.79</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>17767.21</td>\n",
       "      <td>31.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>11328.57</td>\n",
       "      <td>16500.00</td>\n",
       "      <td>5171.43</td>\n",
       "      <td>31.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>25377.05</td>\n",
       "      <td>37000.00</td>\n",
       "      <td>11622.95</td>\n",
       "      <td>31.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17902.91</td>\n",
       "      <td>13621.43</td>\n",
       "      <td>-4281.48</td>\n",
       "      <td>31.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>24337.42</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>11162.58</td>\n",
       "      <td>31.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>15131.35</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-3631.35</td>\n",
       "      <td>31.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>21885.80</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>10114.20</td>\n",
       "      <td>31.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>21885.80</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>10114.20</td>\n",
       "      <td>31.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>8345.97</td>\n",
       "      <td>12250.00</td>\n",
       "      <td>3904.03</td>\n",
       "      <td>31.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>9461.29</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>4438.71</td>\n",
       "      <td>31.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>13921.43</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>6578.57</td>\n",
       "      <td>32.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3937.56</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>1862.44</td>\n",
       "      <td>32.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>14929.34</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>7070.66</td>\n",
       "      <td>32.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11100.42</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>-2700.42</td>\n",
       "      <td>32.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>15863.48</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>-3864.48</td>\n",
       "      <td>32.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>18471.95</td>\n",
       "      <td>27300.00</td>\n",
       "      <td>8828.05</td>\n",
       "      <td>32.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5945.43</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>2854.57</td>\n",
       "      <td>32.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>25845.65</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-6345.65</td>\n",
       "      <td>32.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5327.54</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>2572.46</td>\n",
       "      <td>32.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>37083.82</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>17916.18</td>\n",
       "      <td>32.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>35247.88</td>\n",
       "      <td>52300.00</td>\n",
       "      <td>17052.12</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>17147.48</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>8352.52</td>\n",
       "      <td>32.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>15935.10</td>\n",
       "      <td>23700.00</td>\n",
       "      <td>7764.90</td>\n",
       "      <td>32.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>7305.60</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>3594.40</td>\n",
       "      <td>32.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>7034.02</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3465.98</td>\n",
       "      <td>33.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>7034.02</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3465.98</td>\n",
       "      <td>33.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>5812.17</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>2887.83</td>\n",
       "      <td>33.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>2337.63</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1162.37</td>\n",
       "      <td>33.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>5003.46</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>2496.54</td>\n",
       "      <td>33.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>12336.02</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>6163.98</td>\n",
       "      <td>33.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>14526.03</td>\n",
       "      <td>21800.00</td>\n",
       "      <td>7273.97</td>\n",
       "      <td>33.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>6426.73</td>\n",
       "      <td>9650.00</td>\n",
       "      <td>3223.27</td>\n",
       "      <td>33.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>53258.34</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>-13358.34</td>\n",
       "      <td>33.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>26697.67</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-6697.67</td>\n",
       "      <td>33.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>13683.34</td>\n",
       "      <td>10250.00</td>\n",
       "      <td>-3433.34</td>\n",
       "      <td>33.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>6960.93</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3539.07</td>\n",
       "      <td>33.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>69607.89</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>35391.11</td>\n",
       "      <td>33.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>69607.89</td>\n",
       "      <td>105000.00</td>\n",
       "      <td>35392.11</td>\n",
       "      <td>33.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>19846.15</td>\n",
       "      <td>29999.00</td>\n",
       "      <td>10152.85</td>\n",
       "      <td>33.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>6591.44</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>3407.56</td>\n",
       "      <td>34.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>7210.58</td>\n",
       "      <td>10950.00</td>\n",
       "      <td>3739.42</td>\n",
       "      <td>34.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>27567.46</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-7067.46</td>\n",
       "      <td>34.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>5759.25</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>3040.75</td>\n",
       "      <td>34.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>13724.61</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>7275.39</td>\n",
       "      <td>34.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>15489.07</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-3989.07</td>\n",
       "      <td>34.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>4499.32</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>2400.68</td>\n",
       "      <td>34.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>24776.66</td>\n",
       "      <td>38000.00</td>\n",
       "      <td>13223.34</td>\n",
       "      <td>34.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>26006.24</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>13893.76</td>\n",
       "      <td>34.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>10296.26</td>\n",
       "      <td>15800.00</td>\n",
       "      <td>5503.74</td>\n",
       "      <td>34.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7491.85</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>4008.15</td>\n",
       "      <td>34.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>33044.68</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-8544.68</td>\n",
       "      <td>34.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>10662.55</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-2762.55</td>\n",
       "      <td>34.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>7158.09</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>-1858.09</td>\n",
       "      <td>35.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>68077.91</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>36921.09</td>\n",
       "      <td>35.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>4469.82</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>2430.18</td>\n",
       "      <td>35.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>29750.71</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>-7750.71</td>\n",
       "      <td>35.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>56742.51</td>\n",
       "      <td>41900.00</td>\n",
       "      <td>-14842.51</td>\n",
       "      <td>35.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>12831.34</td>\n",
       "      <td>19900.00</td>\n",
       "      <td>7068.66</td>\n",
       "      <td>35.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>20907.36</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>11592.64</td>\n",
       "      <td>35.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>5913.39</td>\n",
       "      <td>9250.00</td>\n",
       "      <td>3336.61</td>\n",
       "      <td>36.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>10207.26</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-2707.26</td>\n",
       "      <td>36.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2683.31</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>1516.69</td>\n",
       "      <td>36.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>34034.10</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>-9034.10</td>\n",
       "      <td>36.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>19776.03</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>-5276.03</td>\n",
       "      <td>36.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>12680.01</td>\n",
       "      <td>19999.00</td>\n",
       "      <td>7318.99</td>\n",
       "      <td>36.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>12978.20</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-3478.20</td>\n",
       "      <td>36.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2216.97</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1283.03</td>\n",
       "      <td>36.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>6331.07</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>3668.93</td>\n",
       "      <td>36.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>69607.89</td>\n",
       "      <td>109999.00</td>\n",
       "      <td>40391.11</td>\n",
       "      <td>36.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>69593.43</td>\n",
       "      <td>109999.00</td>\n",
       "      <td>40405.57</td>\n",
       "      <td>36.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>15588.26</td>\n",
       "      <td>11400.00</td>\n",
       "      <td>-4188.26</td>\n",
       "      <td>36.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9613.94</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>5586.06</td>\n",
       "      <td>36.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>13006.49</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-3506.49</td>\n",
       "      <td>36.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>22329.58</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>13170.42</td>\n",
       "      <td>37.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>69953.88</td>\n",
       "      <td>111999.00</td>\n",
       "      <td>42045.12</td>\n",
       "      <td>37.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>6194.66</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-1694.66</td>\n",
       "      <td>37.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>6526.40</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3973.60</td>\n",
       "      <td>37.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>14289.69</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>8710.31</td>\n",
       "      <td>37.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>16299.63</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-4499.63</td>\n",
       "      <td>38.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>9608.03</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-2658.03</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>80278.92</td>\n",
       "      <td>129999.00</td>\n",
       "      <td>49720.08</td>\n",
       "      <td>38.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>16327.91</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>-4527.91</td>\n",
       "      <td>38.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>26395.97</td>\n",
       "      <td>42900.00</td>\n",
       "      <td>16504.03</td>\n",
       "      <td>38.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>26395.97</td>\n",
       "      <td>42900.00</td>\n",
       "      <td>16504.03</td>\n",
       "      <td>38.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>9699.18</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-2699.18</td>\n",
       "      <td>38.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>17841.08</td>\n",
       "      <td>29040.00</td>\n",
       "      <td>11198.92</td>\n",
       "      <td>38.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>42961.17</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>27037.83</td>\n",
       "      <td>38.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>9200.22</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5799.78</td>\n",
       "      <td>38.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2497.12</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>-697.12</td>\n",
       "      <td>38.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>47625.76</td>\n",
       "      <td>77777.00</td>\n",
       "      <td>30151.24</td>\n",
       "      <td>38.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>13587.74</td>\n",
       "      <td>22200.00</td>\n",
       "      <td>8612.26</td>\n",
       "      <td>38.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>42767.44</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>27232.56</td>\n",
       "      <td>38.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>10418.78</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-2918.78</td>\n",
       "      <td>38.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>22890.90</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>14609.10</td>\n",
       "      <td>38.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>27097.76</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-7597.76</td>\n",
       "      <td>38.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>7020.21</td>\n",
       "      <td>5050.00</td>\n",
       "      <td>-1970.21</td>\n",
       "      <td>39.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>23765.67</td>\n",
       "      <td>39000.00</td>\n",
       "      <td>15234.33</td>\n",
       "      <td>39.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>18563.10</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>11936.90</td>\n",
       "      <td>39.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>10446.39</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-2946.39</td>\n",
       "      <td>39.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>55681.47</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>-15781.47</td>\n",
       "      <td>39.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>9604.54</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>6295.46</td>\n",
       "      <td>39.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>3216.19</td>\n",
       "      <td>2299.00</td>\n",
       "      <td>-917.19</td>\n",
       "      <td>39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>3923.37</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-1123.37</td>\n",
       "      <td>40.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>17945.48</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>-5145.48</td>\n",
       "      <td>40.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>10516.97</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-3016.97</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>14087.57</td>\n",
       "      <td>23700.00</td>\n",
       "      <td>9612.43</td>\n",
       "      <td>40.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>14356.70</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-4156.70</td>\n",
       "      <td>40.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6193.74</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-1793.74</td>\n",
       "      <td>40.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2605.93</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>1794.07</td>\n",
       "      <td>40.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>7990.62</td>\n",
       "      <td>13499.00</td>\n",
       "      <td>5508.38</td>\n",
       "      <td>40.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8735.15</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>-2535.15</td>\n",
       "      <td>40.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>4654.45</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-1354.45</td>\n",
       "      <td>41.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>19776.88</td>\n",
       "      <td>13995.00</td>\n",
       "      <td>-5781.88</td>\n",
       "      <td>41.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>5794.06</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-1694.06</td>\n",
       "      <td>41.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>7601.07</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>5397.93</td>\n",
       "      <td>41.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>14436.30</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-4236.30</td>\n",
       "      <td>41.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8733.87</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>6266.13</td>\n",
       "      <td>41.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>21783.59</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>15716.41</td>\n",
       "      <td>41.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>7804.30</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>5695.70</td>\n",
       "      <td>42.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5592.98</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>4107.02</td>\n",
       "      <td>42.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>7392.21</td>\n",
       "      <td>12850.00</td>\n",
       "      <td>5457.79</td>\n",
       "      <td>42.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>31058.31</td>\n",
       "      <td>53999.00</td>\n",
       "      <td>22940.69</td>\n",
       "      <td>42.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>46871.86</td>\n",
       "      <td>81500.00</td>\n",
       "      <td>34628.14</td>\n",
       "      <td>42.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>8782.09</td>\n",
       "      <td>15300.00</td>\n",
       "      <td>6517.91</td>\n",
       "      <td>42.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>15432.90</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>11567.10</td>\n",
       "      <td>42.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>16257.74</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>12242.26</td>\n",
       "      <td>42.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>29591.72</td>\n",
       "      <td>52222.00</td>\n",
       "      <td>22630.28</td>\n",
       "      <td>43.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>18401.05</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>14098.95</td>\n",
       "      <td>43.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>9965.51</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-3015.51</td>\n",
       "      <td>43.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>5940.74</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>4559.26</td>\n",
       "      <td>43.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>10176.78</td>\n",
       "      <td>17999.00</td>\n",
       "      <td>7822.22</td>\n",
       "      <td>43.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>26006.24</td>\n",
       "      <td>46000.00</td>\n",
       "      <td>19993.76</td>\n",
       "      <td>43.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>14692.36</td>\n",
       "      <td>25999.00</td>\n",
       "      <td>11306.64</td>\n",
       "      <td>43.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>7081.13</td>\n",
       "      <td>12600.00</td>\n",
       "      <td>5518.87</td>\n",
       "      <td>43.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19320.02</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>15679.98</td>\n",
       "      <td>44.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>7719.88</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>6279.12</td>\n",
       "      <td>44.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2867.14</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>2332.86</td>\n",
       "      <td>44.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>18598.57</td>\n",
       "      <td>33900.00</td>\n",
       "      <td>15301.43</td>\n",
       "      <td>45.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>25853.04</td>\n",
       "      <td>47300.00</td>\n",
       "      <td>21446.96</td>\n",
       "      <td>45.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>67521.65</td>\n",
       "      <td>124000.00</td>\n",
       "      <td>56478.35</td>\n",
       "      <td>45.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>24339.92</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>20660.08</td>\n",
       "      <td>45.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>11829.68</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>10070.32</td>\n",
       "      <td>45.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>26179.52</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-8279.52</td>\n",
       "      <td>46.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>22008.72</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>18991.28</td>\n",
       "      <td>46.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>14424.69</td>\n",
       "      <td>9850.00</td>\n",
       "      <td>-4574.69</td>\n",
       "      <td>46.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10696.32</td>\n",
       "      <td>19995.00</td>\n",
       "      <td>9298.68</td>\n",
       "      <td>46.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>20749.78</td>\n",
       "      <td>38888.00</td>\n",
       "      <td>18138.22</td>\n",
       "      <td>46.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>26434.23</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>-8434.23</td>\n",
       "      <td>46.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>5300.05</td>\n",
       "      <td>3600.00</td>\n",
       "      <td>-1700.05</td>\n",
       "      <td>47.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1315.05</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>1184.95</td>\n",
       "      <td>47.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>4940.94</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>4459.06</td>\n",
       "      <td>47.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>14752.75</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-4753.75</td>\n",
       "      <td>47.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>8747.68</td>\n",
       "      <td>16700.00</td>\n",
       "      <td>7952.32</td>\n",
       "      <td>47.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>13287.51</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-4287.51</td>\n",
       "      <td>47.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>7840.57</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>7159.43</td>\n",
       "      <td>47.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>17384.11</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-5684.11</td>\n",
       "      <td>48.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>27426.54</td>\n",
       "      <td>18400.00</td>\n",
       "      <td>-9026.54</td>\n",
       "      <td>49.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>3876.50</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-1276.50</td>\n",
       "      <td>49.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>78840.45</td>\n",
       "      <td>154999.00</td>\n",
       "      <td>76158.55</td>\n",
       "      <td>49.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4819.96</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>4680.04</td>\n",
       "      <td>49.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>10351.77</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>10148.23</td>\n",
       "      <td>49.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>8530.10</td>\n",
       "      <td>5699.00</td>\n",
       "      <td>-2831.10</td>\n",
       "      <td>49.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>38723.42</td>\n",
       "      <td>77000.00</td>\n",
       "      <td>38276.58</td>\n",
       "      <td>49.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>29202.31</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-9702.31</td>\n",
       "      <td>49.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>14801.26</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>14698.74</td>\n",
       "      <td>49.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6520.72</td>\n",
       "      <td>4350.00</td>\n",
       "      <td>-2170.72</td>\n",
       "      <td>49.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>54778.40</td>\n",
       "      <td>36500.00</td>\n",
       "      <td>-18278.40</td>\n",
       "      <td>50.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>99473.69</td>\n",
       "      <td>199999.00</td>\n",
       "      <td>100525.31</td>\n",
       "      <td>50.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>99473.69</td>\n",
       "      <td>199999.00</td>\n",
       "      <td>100525.31</td>\n",
       "      <td>50.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>15182.41</td>\n",
       "      <td>10100.00</td>\n",
       "      <td>-5082.41</td>\n",
       "      <td>50.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>42870.80</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>-14370.80</td>\n",
       "      <td>50.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>32042.10</td>\n",
       "      <td>64999.00</td>\n",
       "      <td>32956.90</td>\n",
       "      <td>50.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>10252.61</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-3452.61</td>\n",
       "      <td>50.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>3188.66</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>3311.34</td>\n",
       "      <td>50.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>9819.79</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-3319.79</td>\n",
       "      <td>51.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2271.06</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>-772.06</td>\n",
       "      <td>51.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>6233.63</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>6666.37</td>\n",
       "      <td>51.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>24991.96</td>\n",
       "      <td>52000.00</td>\n",
       "      <td>27008.04</td>\n",
       "      <td>51.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>2477.09</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>2722.91</td>\n",
       "      <td>52.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>4757.26</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>5241.74</td>\n",
       "      <td>52.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>23204.90</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>-8004.90</td>\n",
       "      <td>52.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>5064.97</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>5635.03</td>\n",
       "      <td>52.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>4583.45</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>5116.55</td>\n",
       "      <td>52.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>6116.56</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-2116.56</td>\n",
       "      <td>52.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>43729.54</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>-15229.54</td>\n",
       "      <td>53.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>42216.50</td>\n",
       "      <td>90999.00</td>\n",
       "      <td>48782.50</td>\n",
       "      <td>53.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>13388.55</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-4688.55</td>\n",
       "      <td>53.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>6205.29</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>7294.71</td>\n",
       "      <td>54.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>22798.48</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>-7998.48</td>\n",
       "      <td>54.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>4314.35</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-1514.35</td>\n",
       "      <td>54.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>14154.08</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>16745.92</td>\n",
       "      <td>54.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>28229.88</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>-9929.88</td>\n",
       "      <td>54.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>6024.81</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>7175.19</td>\n",
       "      <td>54.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1791.17</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>2208.83</td>\n",
       "      <td>55.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>57288.57</td>\n",
       "      <td>129222.00</td>\n",
       "      <td>71933.43</td>\n",
       "      <td>55.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>7799.96</td>\n",
       "      <td>17777.00</td>\n",
       "      <td>9977.04</td>\n",
       "      <td>56.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>7826.62</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-2827.62</td>\n",
       "      <td>56.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>11257.44</td>\n",
       "      <td>7170.00</td>\n",
       "      <td>-4087.44</td>\n",
       "      <td>57.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>11786.16</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>15713.84</td>\n",
       "      <td>57.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>7308.40</td>\n",
       "      <td>4650.00</td>\n",
       "      <td>-2658.40</td>\n",
       "      <td>57.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7136.13</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-2636.13</td>\n",
       "      <td>58.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>35315.23</td>\n",
       "      <td>85555.00</td>\n",
       "      <td>50239.77</td>\n",
       "      <td>58.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>2805.75</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>3994.25</td>\n",
       "      <td>58.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>29994.27</td>\n",
       "      <td>72700.00</td>\n",
       "      <td>42705.73</td>\n",
       "      <td>58.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>1195.37</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>1704.63</td>\n",
       "      <td>58.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>40508.63</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>-15008.63</td>\n",
       "      <td>58.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>17748.19</td>\n",
       "      <td>44000.00</td>\n",
       "      <td>26251.81</td>\n",
       "      <td>59.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>20108.52</td>\n",
       "      <td>49999.00</td>\n",
       "      <td>29890.48</td>\n",
       "      <td>59.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>3047.73</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>-1147.73</td>\n",
       "      <td>60.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>32103.32</td>\n",
       "      <td>81500.00</td>\n",
       "      <td>49396.68</td>\n",
       "      <td>60.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>24795.52</td>\n",
       "      <td>63200.00</td>\n",
       "      <td>38404.48</td>\n",
       "      <td>60.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>2298.39</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>3601.61</td>\n",
       "      <td>61.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>20782.19</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>-7982.19</td>\n",
       "      <td>62.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>42727.45</td>\n",
       "      <td>115000.00</td>\n",
       "      <td>72272.55</td>\n",
       "      <td>62.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>4961.92</td>\n",
       "      <td>13566.00</td>\n",
       "      <td>8604.08</td>\n",
       "      <td>63.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>42052.90</td>\n",
       "      <td>115000.00</td>\n",
       "      <td>72947.10</td>\n",
       "      <td>63.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>13145.44</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-5145.44</td>\n",
       "      <td>64.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>14223.15</td>\n",
       "      <td>8650.00</td>\n",
       "      <td>-5573.15</td>\n",
       "      <td>64.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>38630.65</td>\n",
       "      <td>109900.00</td>\n",
       "      <td>71269.35</td>\n",
       "      <td>64.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36938.43</td>\n",
       "      <td>107000.00</td>\n",
       "      <td>70061.57</td>\n",
       "      <td>65.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>19031.56</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-7531.56</td>\n",
       "      <td>65.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>19041.16</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-7541.16</td>\n",
       "      <td>65.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2821.51</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>5378.49</td>\n",
       "      <td>65.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>17667.73</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>-7067.73</td>\n",
       "      <td>66.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>9225.60</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-3725.60</td>\n",
       "      <td>67.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5537.91</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-2237.91</td>\n",
       "      <td>67.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>5878.05</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-2378.05</td>\n",
       "      <td>67.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>36250.98</td>\n",
       "      <td>21300.00</td>\n",
       "      <td>-14950.98</td>\n",
       "      <td>70.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>10937.47</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-4537.47</td>\n",
       "      <td>70.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6498.94</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-2698.94</td>\n",
       "      <td>71.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>6513.43</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-2714.43</td>\n",
       "      <td>71.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>10981.78</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-4581.78</td>\n",
       "      <td>71.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>70830.57</td>\n",
       "      <td>249999.00</td>\n",
       "      <td>179168.43</td>\n",
       "      <td>71.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2408.45</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>-1008.45</td>\n",
       "      <td>72.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5335.96</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>-2235.96</td>\n",
       "      <td>72.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>45192.52</td>\n",
       "      <td>169000.00</td>\n",
       "      <td>123807.48</td>\n",
       "      <td>73.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>40758.50</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-17258.50</td>\n",
       "      <td>73.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>11018.67</td>\n",
       "      <td>6350.00</td>\n",
       "      <td>-4668.67</td>\n",
       "      <td>73.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>52973.22</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>-22473.22</td>\n",
       "      <td>73.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>9207.21</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>-3907.21</td>\n",
       "      <td>73.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>3958.95</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>11541.05</td>\n",
       "      <td>74.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>8656.61</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-3756.61</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>35582.50</td>\n",
       "      <td>19990.00</td>\n",
       "      <td>-15592.50</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>10101.76</td>\n",
       "      <td>5600.00</td>\n",
       "      <td>-4501.76</td>\n",
       "      <td>80.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>35063.02</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>164936.98</td>\n",
       "      <td>82.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>40968.92</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>-18968.92</td>\n",
       "      <td>86.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>46839.68</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>-21839.68</td>\n",
       "      <td>87.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>3687.51</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>-1737.51</td>\n",
       "      <td>89.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>15148.77</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-7148.77</td>\n",
       "      <td>89.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>11283.76</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>-5383.76</td>\n",
       "      <td>91.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>12397.72</td>\n",
       "      <td>6399.00</td>\n",
       "      <td>-5998.72</td>\n",
       "      <td>93.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>19711.93</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-9712.93</td>\n",
       "      <td>97.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>27468.28</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>-13568.28</td>\n",
       "      <td>97.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>3752.09</td>\n",
       "      <td>1850.00</td>\n",
       "      <td>-1902.09</td>\n",
       "      <td>102.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>21409.28</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-10909.28</td>\n",
       "      <td>103.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>9697.61</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>-4997.61</td>\n",
       "      <td>106.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>10335.12</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-5335.12</td>\n",
       "      <td>106.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>57562.85</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>-30062.85</td>\n",
       "      <td>109.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>45971.24</td>\n",
       "      <td>21300.00</td>\n",
       "      <td>-24671.24</td>\n",
       "      <td>115.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>10799.27</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-5799.27</td>\n",
       "      <td>115.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>9082.80</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-4882.80</td>\n",
       "      <td>116.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>15142.34</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-8142.34</td>\n",
       "      <td>116.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>20821.04</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-11321.04</td>\n",
       "      <td>119.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>31594.67</td>\n",
       "      <td>14300.00</td>\n",
       "      <td>-17294.67</td>\n",
       "      <td>120.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>16480.47</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-9280.47</td>\n",
       "      <td>128.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>5596.19</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-3196.19</td>\n",
       "      <td>133.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>17515.30</td>\n",
       "      <td>7150.00</td>\n",
       "      <td>-10365.30</td>\n",
       "      <td>144.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>8619.53</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-5119.53</td>\n",
       "      <td>146.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>6111.81</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-3661.81</td>\n",
       "      <td>149.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>13806.26</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-8306.26</td>\n",
       "      <td>151.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>17515.30</td>\n",
       "      <td>6750.00</td>\n",
       "      <td>-10765.30</td>\n",
       "      <td>159.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>3133.39</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>-1933.39</td>\n",
       "      <td>161.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>26124.98</td>\n",
       "      <td>9750.00</td>\n",
       "      <td>-16374.98</td>\n",
       "      <td>167.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>2333.07</td>\n",
       "      <td>800.00</td>\n",
       "      <td>-1533.07</td>\n",
       "      <td>191.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>13702.85</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>-9102.85</td>\n",
       "      <td>197.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>7151.28</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-4751.28</td>\n",
       "      <td>197.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>18274.28</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-12274.28</td>\n",
       "      <td>204.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>14638.44</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-9838.44</td>\n",
       "      <td>204.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>3887.69</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>-2687.69</td>\n",
       "      <td>223.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>4419.56</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>-3069.56</td>\n",
       "      <td>227.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>13937.37</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-9837.37</td>\n",
       "      <td>239.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>16578.54</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-11778.54</td>\n",
       "      <td>245.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>9508.35</td>\n",
       "      <td>2750.00</td>\n",
       "      <td>-6758.35</td>\n",
       "      <td>245.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>7126.24</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>-5626.24</td>\n",
       "      <td>375.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>39080.14</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-32580.14</td>\n",
       "      <td>501.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>38529.33</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-33729.33</td>\n",
       "      <td>702.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>46409.36</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-44009.36</td>\n",
       "      <td>1833.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Prediction    Target  Residual  Difference%\n",
       "54     32385.60  32400.00     14.40         0.04\n",
       "662    46574.25  46500.00    -74.25         0.16\n",
       "384    17600.14  17639.47     39.33         0.22\n",
       "526    54866.55  55000.00    133.45         0.24\n",
       "240     8278.72   8300.00     21.28         0.26\n",
       "497     8323.24   8350.00     26.76         0.32\n",
       "580    12944.41  12900.00    -44.41         0.34\n",
       "270    18930.10  18999.00     68.90         0.36\n",
       "607    32359.77  32500.00    140.23         0.43\n",
       "565    12839.50  12900.00     60.50         0.47\n",
       "314     2981.97   3000.00     18.03         0.60\n",
       "387     8957.99   8900.00    -57.99         0.65\n",
       "517    44665.52  44999.00    333.48         0.74\n",
       "203    21157.30  21000.00   -157.30         0.75\n",
       "594    29131.76  28900.00   -231.76         0.80\n",
       "342     5045.80   4999.00    -46.80         0.94\n",
       "196    11590.02  11700.00    109.98         0.94\n",
       "689    10096.43  10000.00    -96.43         0.96\n",
       "112     7221.34   7300.00     78.66         1.08\n",
       "53     12796.88  12650.00   -146.88         1.16\n",
       "109    39515.25  39999.00    483.75         1.21\n",
       "200     2864.16   2900.00     35.84         1.24\n",
       "498     8393.47   8500.00    106.53         1.25\n",
       "5       8782.09   8900.00    117.91         1.32\n",
       "67     32024.79  32500.00    475.21         1.46\n",
       "488     8070.30   8200.00    129.70         1.58\n",
       "235    11808.93  12000.00    191.07         1.59\n",
       "641     5083.27   5000.00    -83.27         1.67\n",
       "291    28182.91  28700.00    517.09         1.80\n",
       "610     4584.00   4500.00    -84.00         1.87\n",
       "45     34326.78  35000.00    673.22         1.92\n",
       "704    15084.98  15400.00    315.02         2.05\n",
       "720     3818.32   3900.00     81.68         2.09\n",
       "297    22998.30  22500.00   -498.30         2.21\n",
       "175    21477.39  21000.00   -477.39         2.27\n",
       "323    30783.64  31500.00    716.36         2.27\n",
       "459    30174.93  29500.00   -674.93         2.29\n",
       "448    11585.33  11300.00   -285.33         2.53\n",
       "393    55006.33  56500.00   1493.67         2.64\n",
       "592    30291.88  29500.00   -791.88         2.68\n",
       "309    12014.38  11700.00   -314.38         2.69\n",
       "337    11771.17  12100.00    328.83         2.72\n",
       "87      3696.69   3800.00    103.31         2.72\n",
       "72      9662.94   9400.00   -262.94         2.80\n",
       "430     6512.16   6700.00    187.84         2.80\n",
       "338    21283.46  21900.00    616.54         2.82\n",
       "157     1594.71   1550.00    -44.71         2.88\n",
       "560     6585.71   6399.00   -186.71         2.92\n",
       "4      16502.82  16999.00    496.18         2.92\n",
       "702    16500.91  16999.00    498.09         2.93\n",
       "521    12800.16  13200.00    399.84         3.03\n",
       "94      7729.91   7500.00   -229.91         3.07\n",
       "525    26282.33  25500.00   -782.33         3.07\n",
       "601    19860.22  20500.00    639.78         3.12\n",
       "585     5664.88   5850.00    185.12         3.16\n",
       "778    22010.98  21335.00   -675.98         3.17\n",
       "106     7641.91   7400.00   -241.91         3.27\n",
       "474    12396.44  12000.00   -396.44         3.30\n",
       "681     2295.70   2222.00    -73.70         3.32\n",
       "664     7233.33   7000.00   -233.33         3.33\n",
       "486     9199.74   8900.00   -299.74         3.37\n",
       "630    24828.01  24000.00   -828.01         3.45\n",
       "190    11155.32  10777.00   -378.32         3.51\n",
       "207    12628.30  12200.00   -428.30         3.51\n",
       "748    24881.61  25800.00    918.39         3.56\n",
       "133    12138.51  11700.00   -438.51         3.75\n",
       "149    18861.74  19600.00    738.26         3.77\n",
       "295    32714.54  34000.00   1285.46         3.78\n",
       "586    18478.99  17800.00   -678.99         3.81\n",
       "402     2547.70   2650.00    102.30         3.86\n",
       "604     7679.36   7990.00    310.64         3.89\n",
       "115    28827.18  30000.00   1172.82         3.91\n",
       "281     3546.13   3700.00    153.87         4.16\n",
       "777     1866.98   1950.00     83.02         4.26\n",
       "663    22329.56  23333.00   1003.44         4.30\n",
       "545     5216.28   5000.00   -216.28         4.33\n",
       "756    37783.29  39500.00   1716.71         4.35\n",
       "421    17694.00  18500.00    806.00         4.36\n",
       "484    11475.65  11999.00    523.35         4.36\n",
       "329    10413.44  10900.00    486.56         4.46\n",
       "540    81101.31  85000.00   3898.69         4.59\n",
       "713    17936.07  18800.00    863.93         4.60\n",
       "385     8869.55   9300.00    430.45         4.63\n",
       "20     18111.21  19000.00    888.79         4.68\n",
       "105    13924.90  13300.00   -624.90         4.70\n",
       "747    12514.56  11950.00   -564.56         4.72\n",
       "548     2951.76   3100.00    148.24         4.78\n",
       "138     9384.02   8950.00   -434.02         4.85\n",
       "509    16457.54  17300.00    842.46         4.87\n",
       "40      4353.85   4150.00   -203.85         4.91\n",
       "114     2098.78   1999.00    -99.78         4.99\n",
       "519     7125.42   7500.00    374.58         4.99\n",
       "636    17667.73  18600.00    932.27         5.01\n",
       "395     9143.26   8700.00   -443.26         5.09\n",
       "238    22072.25  21000.00  -1072.25         5.11\n",
       "191    10303.57   9800.00   -503.57         5.14\n",
       "485    21980.88  20900.00  -1080.88         5.17\n",
       "534     5830.47   6150.00    319.53         5.20\n",
       "168    24408.20  25749.75   1341.55         5.21\n",
       "125    73713.84  70000.00  -3713.84         5.31\n",
       "77     27347.60  28950.00   1602.40         5.54\n",
       "550    14062.56  14900.00    837.44         5.62\n",
       "551     8298.71   8800.00    501.29         5.70\n",
       "15      8996.80   8500.00   -496.80         5.84\n",
       "776    15528.65  16500.00    971.35         5.89\n",
       "199    21257.95  22600.00   1342.05         5.94\n",
       "635     9431.16   8900.00   -531.16         5.97\n",
       "253     9356.09   9950.00    593.91         5.97\n",
       "575    10050.47  10700.00    649.53         6.07\n",
       "468    12210.23  13000.00    789.77         6.08\n",
       "695    13157.71  12400.00   -757.71         6.11\n",
       "246    14363.15  15300.00    936.85         6.12\n",
       "457    17553.78  18700.00   1146.22         6.13\n",
       "179     9198.22   9800.00    601.78         6.14\n",
       "241    20592.06  19400.00  -1192.06         6.14\n",
       "605    11257.44  11999.00    741.56         6.18\n",
       "531    12186.89  12999.00    812.11         6.25\n",
       "757     6010.25   5650.00   -360.25         6.38\n",
       "316    15640.82  14700.00   -940.82         6.40\n",
       "535     6922.10   6500.00   -422.10         6.49\n",
       "361     8313.98   7800.00   -513.98         6.59\n",
       "223    24803.48  26600.00   1796.52         6.75\n",
       "764     8435.19   7900.00   -535.19         6.77\n",
       "496    14202.70  13300.00   -902.70         6.79\n",
       "12     44260.45  47500.00   3239.55         6.82\n",
       "771    12574.92  13500.00    925.08         6.85\n",
       "50      3863.93   4150.00    286.07         6.89\n",
       "259    12720.69  11900.00   -820.69         6.90\n",
       "41      8280.07   8900.00    619.93         6.97\n",
       "721    16624.33  17900.00   1275.67         7.13\n",
       "360    66841.73  72000.00   5158.27         7.16\n",
       "260    11408.45  12300.00    891.55         7.25\n",
       "85     11797.96  11000.00   -797.96         7.25\n",
       "450     4915.01   5300.00    384.99         7.26\n",
       "462    11957.75  12900.00    942.25         7.30\n",
       "308     3977.13   4299.00    321.87         7.49\n",
       "520    12659.37  13700.00   1040.63         7.60\n",
       "189     2860.82   3100.00    239.18         7.72\n",
       "257    15862.55  17200.00   1337.45         7.78\n",
       "236    22869.16  24800.00   1930.84         7.79\n",
       "218    16720.90  15500.00  -1220.90         7.88\n",
       "83      9170.44   8500.00   -670.44         7.89\n",
       "751    32263.63  29900.00  -2363.63         7.91\n",
       "600    14568.44  13500.00  -1068.44         7.91\n",
       "516     9660.89  10499.00    838.11         7.98\n",
       "217     7883.70   7300.00   -583.70         8.00\n",
       "733    15172.35  16500.00   1327.65         8.05\n",
       "165    55660.66  51500.00  -4160.66         8.08\n",
       "471    10841.92  11800.00    958.08         8.12\n",
       "226    16076.95  17500.00   1423.05         8.13\n",
       "160    44090.14  48000.00   3909.86         8.15\n",
       "363     2650.79   2450.00   -200.79         8.20\n",
       "285    11687.21  10800.00   -887.21         8.21\n",
       "177    13316.10  12300.00  -1016.10         8.26\n",
       "753    28706.06  26500.00  -2206.06         8.32\n",
       "640     4164.08   4550.00    385.92         8.48\n",
       "185     8869.60   9700.00    830.40         8.56\n",
       "344    49077.89  45200.00  -3877.89         8.58\n",
       "62      5668.03   6200.00    531.97         8.58\n",
       "552    17177.84  15820.35  -1357.49         8.58\n",
       "206     6670.59   7300.00    629.41         8.62\n",
       "741     8406.65   9200.00    793.35         8.62\n",
       "481    11416.96  12500.00   1083.04         8.66\n",
       "171    22280.29  20500.00  -1780.29         8.68\n",
       "205    15979.55  17500.00   1520.45         8.69\n",
       "559     9239.26   8500.00   -739.26         8.70\n",
       "466    13235.41  14500.00   1264.59         8.72\n",
       "693    11671.08  12800.00   1128.92         8.82\n",
       "375     5741.60   6300.00    558.40         8.86\n",
       "699     9419.35   8650.00   -769.35         8.89\n",
       "255     7187.35   7899.00    711.65         9.01\n",
       "107    12428.92  11400.00  -1028.92         9.03\n",
       "43     12538.66  11500.00  -1038.66         9.03\n",
       "477     7968.26   7300.00   -668.26         9.15\n",
       "300    35877.31  39500.00   3622.69         9.17\n",
       "406    11349.69  12500.00   1150.31         9.20\n",
       "761     7643.74   6999.00   -644.74         9.21\n",
       "280     2855.46   3150.00    294.54         9.35\n",
       "673     6343.22   7000.00    656.78         9.38\n",
       "637     7987.45   7300.00   -687.45         9.42\n",
       "2      13496.81  14900.00   1403.19         9.42\n",
       "312    17747.36  19600.00   1852.64         9.45\n",
       "382    14022.07  12800.00  -1222.07         9.55\n",
       "768     7235.05   6600.00   -635.05         9.62\n",
       "602    13924.90  12700.00  -1224.90         9.64\n",
       "503    11738.51  13000.00   1261.49         9.70\n",
       "261    10292.10  11400.00   1107.90         9.72\n",
       "648    39261.24  43500.00   4238.76         9.74\n",
       "633    39261.24  43500.00   4238.76         9.74\n",
       "576    10990.16  12179.00   1188.84         9.76\n",
       "372    11968.39  10900.00  -1068.39         9.80\n",
       "458    12523.66  13899.00   1375.34         9.90\n",
       "212     8820.13   9800.00    979.87        10.00\n",
       "643     5352.31   5950.00    597.69        10.05\n",
       "744    20484.79  22800.00   2315.21        10.15\n",
       "164     5389.13   6000.00    610.87        10.18\n",
       "144    34243.84  38233.00   3989.16        10.43\n",
       "467     2327.72   2600.00    272.28        10.47\n",
       "426    13038.02  11800.00  -1238.02        10.49\n",
       "434    32203.77  35999.00   3795.23        10.54\n",
       "310    29321.56  26500.00  -2821.56        10.65\n",
       "767     2767.09   2500.00   -267.09        10.68\n",
       "456     7943.91   8900.00    956.09        10.74\n",
       "770    13828.59  15499.00   1670.41        10.78\n",
       "296    27741.38  31111.00   3369.62        10.83\n",
       "268    23348.59  26200.00   2851.41        10.88\n",
       "438    26502.55  23900.00  -2602.55        10.89\n",
       "443    12975.50  11700.00  -1275.50        10.90\n",
       "672    16283.68  18300.00   2016.32        11.02\n",
       "619    27400.89  30800.00   3399.11        11.04\n",
       "507     7905.44   8900.00    994.56        11.17\n",
       "708     7192.45   8100.00    907.55        11.20\n",
       "263     3669.98   3300.00   -369.98        11.21\n",
       "556    11262.63  12700.00   1437.37        11.32\n",
       "631     8030.37   7200.00   -830.37        11.53\n",
       "487    22310.28  19999.00  -2311.28        11.56\n",
       "331    12049.19  10800.00  -1249.19        11.57\n",
       "570    25467.03  28800.00   3332.97        11.57\n",
       "523    11385.55  10200.00  -1185.55        11.62\n",
       "282    11043.32  12500.00   1456.68        11.65\n",
       "780     6923.71   6200.00   -723.71        11.67\n",
       "120    11392.30  10200.00  -1192.30        11.69\n",
       "476    11951.89  10700.00  -1251.89        11.70\n",
       "465     6792.13   7700.00    907.87        11.79\n",
       "353     9326.40  10600.00   1273.60        12.02\n",
       "327     7734.70   6900.00   -834.70        12.10\n",
       "698     8520.67   7600.00   -920.67        12.11\n",
       "398    19295.88  17200.00  -2095.88        12.19\n",
       "154    14038.09  12500.00  -1538.09        12.30\n",
       "616     7491.85   8550.00   1058.15        12.38\n",
       "690    15766.21  18000.00   2233.79        12.41\n",
       "439     1485.69   1700.00    214.31        12.61\n",
       "369    52842.42  60500.00   7657.58        12.66\n",
       "10      6724.33   7700.00    975.67        12.67\n",
       "192     4453.49   5100.00    646.51        12.68\n",
       "251    39466.48  35000.00  -4466.48        12.76\n",
       "561     9507.16  10900.00   1392.84        12.78\n",
       "254    26942.45  30900.00   3957.55        12.81\n",
       "687    10453.02  11999.00   1545.98        12.88\n",
       "568    25412.16  22500.00  -2912.16        12.94\n",
       "400     7214.23   8300.00   1085.77        13.08\n",
       "656    17779.77  20500.00   2720.23        13.27\n",
       "679    17570.86  15500.00  -2070.86        13.36\n",
       "227    43010.52  49700.00   6689.48        13.46\n",
       "642     4142.82   3650.00   -492.82        13.50\n",
       "47      4031.17   3550.00   -481.17        13.55\n",
       "716    60887.45  70500.00   9612.55        13.63\n",
       "582    60426.09  69999.00   9572.91        13.68\n",
       "181    16829.58  14800.00  -2029.58        13.71\n",
       "583     2717.33   3150.00    432.67        13.74\n",
       "407     9053.04  10500.00   1446.96        13.78\n",
       "299     9684.50   8500.00  -1184.50        13.94\n",
       "32     26560.07  23300.00  -3260.07        13.99\n",
       "746     9884.07  11500.00   1615.93        14.05\n",
       "209    11406.13   9999.00  -1407.13        14.07\n",
       "599     2233.47   2600.00    366.53        14.10\n",
       "274     9360.65  10900.00   1539.35        14.12\n",
       "569     9946.29   8700.00  -1246.29        14.33\n",
       "712    14760.83  12900.00  -1860.83        14.43\n",
       "96     25760.73  22500.00  -3260.73        14.49\n",
       "674     5128.13   6000.00    871.87        14.53\n",
       "514    52842.42  62000.00   9157.58        14.77\n",
       "248     4772.50   5600.00    827.50        14.78\n",
       "684    17008.34  20007.00   2998.66        14.99\n",
       "579    21749.35  18900.00  -2849.35        15.08\n",
       "81      9216.03   8000.00  -1216.03        15.20\n",
       "617    36866.69  32000.00  -4866.69        15.21\n",
       "549    24297.20  28660.00   4362.80        15.22\n",
       "612    19059.83  22500.00   3440.17        15.29\n",
       "29      5675.47   6700.00   1024.53        15.29\n",
       "37     63512.54  75000.00  11487.46        15.32\n",
       "622    16837.72  19900.00   3062.28        15.39\n",
       "608    12256.26  14500.00   2243.74        15.47\n",
       "194    18725.79  16200.00  -2525.79        15.59\n",
       "116    24297.20  28793.04   4495.84        15.61\n",
       "553    27752.79  24000.00  -3752.79        15.64\n",
       "31     12887.29  15300.00   2412.71        15.77\n",
       "232    26024.70  30900.00   4875.30        15.78\n",
       "76      4632.83   4000.00   -632.83        15.82\n",
       "500    34474.68  41000.00   6525.32        15.92\n",
       "334    25526.53  22000.00  -3526.53        16.03\n",
       "469    30984.62  36900.00   5915.38        16.03\n",
       "139     8712.12   7500.00  -1212.12        16.16\n",
       "357     4174.43   5000.00    825.57        16.51\n",
       "454     8163.91   9800.00   1636.09        16.69\n",
       "581     4331.20   5200.00    868.80        16.71\n",
       "762    27431.12  23500.00  -3931.12        16.73\n",
       "27     20794.97  24996.00   4201.03        16.81\n",
       "668    58184.07  69990.00  11805.93        16.87\n",
       "567     7850.10   9450.00   1599.90        16.93\n",
       "431     1988.73   1700.00   -288.73        16.98\n",
       "118     1618.78   1950.00    331.22        16.99\n",
       "365     5621.16   4800.00   -821.16        17.11\n",
       "267    12008.21  14500.00   2491.79        17.18\n",
       "70      9010.37  10900.00   1889.63        17.34\n",
       "354     7179.84   8700.00   1520.16        17.47\n",
       "717    10148.91  12300.00   2151.09        17.49\n",
       "219     5875.92   5000.00   -875.92        17.52\n",
       "163    10402.70   8850.00  -1552.70        17.54\n",
       "159     4697.05   5700.00   1002.95        17.60\n",
       "742    16351.92  13900.00  -2451.92        17.64\n",
       "33     20531.76  24950.00   4418.24        17.71\n",
       "134     7405.80   9000.00   1594.20        17.71\n",
       "93     17912.85  15200.00  -2712.85        17.85\n",
       "667    15682.71  13300.00  -2382.71        17.92\n",
       "19     12617.30  10700.00  -1917.30        17.92\n",
       "404     7433.90   6300.00  -1133.90        18.00\n",
       "52     24784.11  21000.00  -3784.11        18.02\n",
       "345     2699.74   3300.00    600.26        18.19\n",
       "677    14431.45  12200.00  -2231.45        18.29\n",
       "389    19589.33  23999.00   4409.67        18.37\n",
       "703    17596.06  21600.00   4003.94        18.54\n",
       "589     5098.66   4300.00   -798.66        18.57\n",
       "49      3558.86   3000.00   -558.86        18.63\n",
       "287    56894.51  69999.00  13104.49        18.72\n",
       "117    16514.09  20350.00   3835.91        18.85\n",
       "48     14418.61  17777.00   3358.39        18.89\n",
       "368     7619.45   6400.00  -1219.45        19.05\n",
       "30     14296.36  12000.00  -2296.36        19.14\n",
       "359    13702.85  11500.00  -2202.85        19.16\n",
       "69     25858.70  32000.00   6141.30        19.19\n",
       "707    23865.25  20000.00  -3865.25        19.33\n",
       "492     7865.52   9750.00   1884.48        19.33\n",
       "180     5491.62   4600.00   -891.62        19.38\n",
       "183     2054.19   2550.00    495.81        19.44\n",
       "173     5797.27   7200.00   1402.73        19.48\n",
       "755     6599.46   8200.00   1600.54        19.52\n",
       "97      5737.80   4800.00   -937.80        19.54\n",
       "21     11435.33   9555.00  -1880.33        19.68\n",
       "763     5387.94   4500.00   -887.94        19.73\n",
       "396    23948.80  20000.00  -3948.80        19.74\n",
       "204    52165.19  65000.00  12834.81        19.75\n",
       "349    22864.11  28500.00   5635.89        19.78\n",
       "558    26232.97  21900.00  -4332.97        19.79\n",
       "380     3835.81   3200.00   -635.81        19.87\n",
       "187    23375.89  19500.00  -3875.89        19.88\n",
       "351     8272.86   6900.00  -1372.86        19.90\n",
       "119    33614.79  42000.00   8385.21        19.96\n",
       "252    30594.65  25500.00  -5094.65        19.98\n",
       "732     9040.04  11300.00   2259.96        20.00\n",
       "290     6799.68   8500.00   1700.32        20.00\n",
       "362     8402.88   6999.00  -1403.88        20.06\n",
       "445     9988.12  12500.00   2511.88        20.10\n",
       "391    13574.21  17000.00   3425.79        20.15\n",
       "644     8300.12   6900.00  -1400.12        20.29\n",
       "184    52842.42  66500.00  13657.58        20.54\n",
       "676     7689.64   9700.00   2010.36        20.73\n",
       "63     17266.67  14300.00  -2966.67        20.75\n",
       "394    29596.84  24500.00  -5096.84        20.80\n",
       "696    13775.20  17400.00   3624.80        20.83\n",
       "148    34818.27  44000.00   9181.73        20.87\n",
       "71     22546.67  28500.00   5953.33        20.89\n",
       "711    21262.95  26900.00   5637.05        20.96\n",
       "493    56894.51  71999.00  15104.49        20.98\n",
       "639    50014.22  63333.00  13318.78        21.03\n",
       "728    11876.14   9800.00  -2076.14        21.19\n",
       "441    37084.67  30600.00  -6484.67        21.19\n",
       "325     7325.60   9300.00   1974.40        21.23\n",
       "532    33457.59  42500.00   9042.41        21.28\n",
       "340     9354.14  11900.00   2545.86        21.39\n",
       "436    33012.12  42000.00   8987.88        21.40\n",
       "156     9819.82  12500.00   2680.18        21.44\n",
       "715    15542.28  19800.00   4257.72        21.50\n",
       "153    22658.74  28900.00   6241.26        21.60\n",
       "247    81504.43 103999.00  22494.57        21.63\n",
       "110     5112.16   4200.00   -912.16        21.72\n",
       "284    21063.59  27000.00   5936.41        21.99\n",
       "743    31575.04  40500.00   8924.96        22.04\n",
       "108     8911.60   7300.00  -1611.60        22.08\n",
       "504     4884.12   4000.00   -884.12        22.10\n",
       "13      9283.26   7600.00  -1683.26        22.15\n",
       "611    12952.57  10600.00  -2352.57        22.19\n",
       "433    18333.81  15000.00  -3333.81        22.23\n",
       "752    25895.98  33300.00   7404.02        22.23\n",
       "91      5208.39   6700.00   1491.61        22.26\n",
       "623    36938.43  47600.00  10661.57        22.40\n",
       "376    14354.82  18500.00   4145.18        22.41\n",
       "135    13571.82  17500.00   3928.18        22.45\n",
       "234    10719.87   8750.00  -1969.87        22.51\n",
       "709    18748.54  15300.00  -3448.54        22.54\n",
       "73      8748.84  11300.00   2551.16        22.58\n",
       "335    14724.19  12000.00  -2724.19        22.70\n",
       "578    15837.95  12900.00  -2937.95        22.77\n",
       "174    11499.81  14900.00   3400.19        22.82\n",
       "273    11429.08   9300.00  -2129.08        22.89\n",
       "103     9157.19   7450.00  -1707.19        22.92\n",
       "701    14627.81  18999.00   4371.19        23.01\n",
       "572     2694.73   3500.00    805.27        23.01\n",
       "429    11260.33   9150.00  -2110.33        23.06\n",
       "320     9766.11  12700.00   2933.89        23.10\n",
       "423    27961.76  22700.00  -5261.76        23.18\n",
       "178    43092.38  34900.00  -8192.38        23.47\n",
       "343     3210.62   2600.00   -610.62        23.49\n",
       "271     7268.54   9500.00   2231.46        23.49\n",
       "292    20782.19  16800.00  -3982.19        23.70\n",
       "136    39608.99  32000.00  -7608.99        23.78\n",
       "98     22185.01  17900.00  -4285.01        23.94\n",
       "653    12407.95   9999.00  -2408.95        24.09\n",
       "710     9695.36  12800.00   3104.64        24.26\n",
       "527     6807.64   8990.00   2182.36        24.28\n",
       "305    22363.72  17990.00  -4373.72        24.31\n",
       "346    18011.91  14474.00  -3537.91        24.44\n",
       "415    30514.90  24500.00  -6014.90        24.55\n",
       "127     6262.09   8300.00   2037.91        24.55\n",
       "595    29022.66  38500.00   9477.34        24.62\n",
       "322    15789.30  20999.00   5209.70        24.81\n",
       "655     3000.61   4000.00    999.39        24.98\n",
       "501     8197.00   6557.85  -1639.15        25.00\n",
       "283    52424.48  41900.00 -10524.48        25.12\n",
       "99     52287.36  69900.00  17612.64        25.20\n",
       "591    30633.37  41000.00  10366.63        25.28\n",
       "475    12691.54  17000.00   4308.46        25.34\n",
       "286     6270.06   8400.00   2129.94        25.36\n",
       "101     3132.72   2499.00   -633.72        25.36\n",
       "18     10441.59  14000.00   3558.41        25.42\n",
       "618    29730.04  23700.00  -6030.04        25.44\n",
       "211    24577.81  33000.00   8422.19        25.52\n",
       "264    16748.08  22500.00   5751.92        25.56\n",
       "42     20601.52  16400.00  -4201.52        25.62\n",
       "7       5129.05   6900.00   1770.95        25.67\n",
       "615    17912.87  14250.00  -3662.87        25.70\n",
       "222     4463.14   3550.00   -913.14        25.72\n",
       "452     6607.63   8900.00   2292.37        25.76\n",
       "131    32142.37  25555.00  -6587.37        25.78\n",
       "625    16397.35  22100.00   5702.65        25.80\n",
       "336    12225.99  16500.00   4274.01        25.90\n",
       "658     8117.41  10990.00   2872.59        26.14\n",
       "411    12000.29   9500.00  -2500.29        26.32\n",
       "193     7879.54  10700.00   2820.46        26.36\n",
       "350    16823.57  13300.00  -3523.57        26.49\n",
       "176    12144.02   9599.00  -2545.02        26.51\n",
       "654     2214.64   1750.00   -464.64        26.55\n",
       "412     3668.67   5000.00   1331.33        26.63\n",
       "172    18122.50  24700.00   6577.50        26.63\n",
       "422     4585.23   6250.00   1664.77        26.64\n",
       "538    50566.22  39900.00 -10666.22        26.73\n",
       "461     4942.75   3900.00  -1042.75        26.74\n",
       "542     5492.72   7500.00   2007.28        26.76\n",
       "730    16101.21  12700.00  -3401.21        26.78\n",
       "333     6147.58   8400.00   2252.42        26.81\n",
       "651    23993.37  18900.00  -5093.37        26.95\n",
       "685    11466.33  15700.00   4233.67        26.97\n",
       "472    79597.64 109000.00  29402.36        26.97\n",
       "417     8748.44  11999.00   3250.56        27.09\n",
       "650    10566.75  14500.00   3933.25        27.13\n",
       "546     2404.13   3300.00    895.87        27.15\n",
       "266     8650.39   6800.00  -1850.39        27.21\n",
       "276     9371.55  12900.00   3528.45        27.35\n",
       "332    48593.61  66900.00  18306.39        27.36\n",
       "547     5735.32   7900.00   2164.68        27.40\n",
       "143    35035.92  27500.00  -7535.92        27.40\n",
       "358    11357.82   8900.00  -2457.82        27.62\n",
       "577    12635.41   9900.00  -2735.41        27.63\n",
       "511    53461.65  74000.00  20538.35        27.75\n",
       "410    27942.98  38764.00  10821.02        27.92\n",
       "749    15777.64  21900.00   6122.36        27.96\n",
       "781     9581.79  13300.00   3718.21        27.96\n",
       "665    22418.13  17500.00  -4918.13        28.10\n",
       "652    15069.33  21000.00   5930.67        28.24\n",
       "420     7004.03   9800.00   2795.97        28.53\n",
       "84      5993.19   8400.00   2406.81        28.65\n",
       "670    13256.48  10300.00  -2956.48        28.70\n",
       "22     13918.17  10799.00  -3119.17        28.88\n",
       "152    10451.57  14700.00   4248.43        28.90\n",
       "321     8522.67   6600.00  -1922.67        29.13\n",
       "166    11636.05   9000.00  -2636.05        29.29\n",
       "28     19280.80  27300.00   8019.20        29.37\n",
       "502     8312.14  11800.00   3487.86        29.56\n",
       "399    18204.03  25900.00   7695.97        29.71\n",
       "377    27110.47  20900.00  -6210.47        29.72\n",
       "544    16003.99  22800.00   6796.01        29.81\n",
       "66      8960.83   6900.00  -2060.83        29.87\n",
       "505     8061.27  11500.00   3438.73        29.90\n",
       "603    23262.50  17900.00  -5362.50        29.96\n",
       "304    11372.29   8750.00  -2622.29        29.97\n",
       "510    13261.97  10200.00  -3061.97        30.02\n",
       "587     7554.19  10800.00   3245.81        30.05\n",
       "328    12378.40  17700.00   5321.60        30.07\n",
       "737    16911.70  24193.65   7281.95        30.10\n",
       "24      3491.61   5000.00   1508.39        30.17\n",
       "347    24473.76  18800.00  -5673.76        30.18\n",
       "288    69773.42  99999.00  30225.58        30.23\n",
       "449    11462.31   8800.00  -2662.31        30.25\n",
       "65     24759.82  35500.00  10740.18        30.25\n",
       "122     5431.80   7800.00   2368.20        30.36\n",
       "425    20790.25  29900.00   9109.75        30.47\n",
       "495    18946.58  14500.00  -4446.58        30.67\n",
       "294     8689.90   6650.00  -2039.90        30.68\n",
       "169     6331.07   9150.00   2818.93        30.81\n",
       "1       7456.53  10800.00   3343.47        30.96\n",
       "428    11528.14   8800.00  -2728.14        31.00\n",
       "413    24143.66  35000.00  10856.34        31.02\n",
       "82      6198.36   8999.00   2800.64        31.12\n",
       "339     6886.26   9999.00   3112.74        31.13\n",
       "3       7573.11  11000.00   3426.89        31.15\n",
       "401    39232.79  57000.00  17767.21        31.17\n",
       "303    11328.57  16500.00   5171.43        31.34\n",
       "348    25377.05  37000.00  11622.95        31.41\n",
       "35     17902.91  13621.43  -4281.48        31.43\n",
       "293    24337.42  35500.00  11162.58        31.44\n",
       "39     15131.35  11500.00  -3631.35        31.58\n",
       "675    21885.80  32000.00  10114.20        31.61\n",
       "725    21885.80  32000.00  10114.20        31.61\n",
       "634     8345.97  12250.00   3904.03        31.87\n",
       "620     9461.29  13900.00   4438.71        31.93\n",
       "386    13921.43  20500.00   6578.57        32.09\n",
       "170     3937.56   5800.00   1862.44        32.11\n",
       "739    14929.34  22000.00   7070.66        32.14\n",
       "16     11100.42   8400.00  -2700.42        32.15\n",
       "306    15863.48  11999.00  -3864.48        32.21\n",
       "214    18471.95  27300.00   8828.05        32.34\n",
       "80      5945.43   8800.00   2854.57        32.44\n",
       "383    25845.65  19500.00  -6345.65        32.54\n",
       "44      5327.54   7900.00   2572.46        32.56\n",
       "167    37083.82  55000.00  17916.18        32.57\n",
       "657    35247.88  52300.00  17052.12        32.60\n",
       "686    17147.48  25500.00   8352.52        32.75\n",
       "123    15935.10  23700.00   7764.90        32.76\n",
       "596     7305.60  10900.00   3594.40        32.98\n",
       "512     7034.02  10500.00   3465.98        33.01\n",
       "275     7034.02  10500.00   3465.98        33.01\n",
       "482     5812.17   8700.00   2887.83        33.19\n",
       "645     2337.63   3500.00   1162.37        33.21\n",
       "318     5003.46   7500.00   2496.54        33.29\n",
       "726    12336.02  18500.00   6163.98        33.32\n",
       "89     14526.03  21800.00   7273.97        33.37\n",
       "750     6426.73   9650.00   3223.27        33.40\n",
       "692    53258.34  39900.00 -13358.34        33.48\n",
       "277    26697.67  20000.00  -6697.67        33.49\n",
       "132    13683.34  10250.00  -3433.34        33.50\n",
       "524     6960.93  10500.00   3539.07        33.71\n",
       "46     69607.89 104999.00  35391.11        33.71\n",
       "563    69607.89 105000.00  35392.11        33.71\n",
       "75     19846.15  29999.00  10152.85        33.84\n",
       "683     6591.44   9999.00   3407.56        34.08\n",
       "56      7210.58  10950.00   3739.42        34.15\n",
       "738    27567.46  20500.00  -7067.46        34.48\n",
       "573     5759.25   8800.00   3040.75        34.55\n",
       "513    13724.61  21000.00   7275.39        34.64\n",
       "432    15489.07  11500.00  -3989.07        34.69\n",
       "719     4499.32   6900.00   2400.68        34.79\n",
       "628    24776.66  38000.00  13223.34        34.80\n",
       "92     26006.24  39900.00  13893.76        34.82\n",
       "258    10296.26  15800.00   5503.74        34.83\n",
       "128     7491.85  11500.00   4008.15        34.85\n",
       "151    33044.68  24500.00  -8544.68        34.88\n",
       "289    10662.55   7900.00  -2762.55        34.97\n",
       "388     7158.09   5300.00  -1858.09        35.06\n",
       "661    68077.91 104999.00  36921.09        35.16\n",
       "265     4469.82   6900.00   2430.18        35.22\n",
       "700    29750.71  22000.00  -7750.71        35.23\n",
       "480    56742.51  41900.00 -14842.51        35.42\n",
       "330    12831.34  19900.00   7068.66        35.52\n",
       "237    20907.36  32500.00  11592.64        35.67\n",
       "437     5913.39   9250.00   3336.61        36.07\n",
       "272    10207.26   7500.00  -2707.26        36.10\n",
       "130     2683.31   4200.00   1516.69        36.11\n",
       "479    34034.10  25000.00  -9034.10        36.14\n",
       "355    19776.03  14500.00  -5276.03        36.39\n",
       "528    12680.01  19999.00   7318.99        36.60\n",
       "233    12978.20   9500.00  -3478.20        36.61\n",
       "250     2216.97   3500.00   1283.03        36.66\n",
       "225     6331.07  10000.00   3668.93        36.69\n",
       "483    69607.89 109999.00  40391.11        36.72\n",
       "381    69593.43 109999.00  40405.57        36.73\n",
       "606    15588.26  11400.00  -4188.26        36.74\n",
       "182     9613.94  15200.00   5586.06        36.75\n",
       "215    13006.49   9500.00  -3506.49        36.91\n",
       "186    22329.58  35500.00  13170.42        37.10\n",
       "104    69953.88 111999.00  42045.12        37.54\n",
       "669     6194.66   4500.00  -1694.66        37.66\n",
       "244     6526.40  10500.00   3973.60        37.84\n",
       "455    14289.69  23000.00   8710.31        37.87\n",
       "145    16299.63  11800.00  -4499.63        38.13\n",
       "646     9608.03   6950.00  -2658.03        38.25\n",
       "78     80278.92 129999.00  49720.08        38.25\n",
       "74     16327.91  11800.00  -4527.91        38.37\n",
       "34     26395.97  42900.00  16504.03        38.47\n",
       "621    26395.97  42900.00  16504.03        38.47\n",
       "319     9699.18   7000.00  -2699.18        38.56\n",
       "424    17841.08  29040.00  11198.92        38.56\n",
       "315    42961.17  69999.00  27037.83        38.63\n",
       "162     9200.22  15000.00   5799.78        38.67\n",
       "0       2497.12   1800.00   -697.12        38.73\n",
       "23     47625.76  77777.00  30151.24        38.77\n",
       "688    13587.74  22200.00   8612.26        38.79\n",
       "341    42767.44  70000.00  27232.56        38.90\n",
       "229    10418.78   7500.00  -2918.78        38.92\n",
       "213    22890.90  37500.00  14609.10        38.96\n",
       "140    27097.76  19500.00  -7597.76        38.96\n",
       "150     7020.21   5050.00  -1970.21        39.01\n",
       "158    23765.67  39000.00  15234.33        39.06\n",
       "392    18563.10  30500.00  11936.90        39.14\n",
       "146    10446.39   7500.00  -2946.39        39.29\n",
       "221    55681.47  39900.00 -15781.47        39.55\n",
       "731     9604.54  15900.00   6295.46        39.59\n",
       "210     3216.19   2299.00   -917.19        39.90\n",
       "367     3923.37   2800.00  -1123.37        40.12\n",
       "307    17945.48  12800.00  -5145.48        40.20\n",
       "490    10516.97   7500.00  -3016.97        40.23\n",
       "593    14087.57  23700.00   9612.43        40.56\n",
       "562    14356.70  10200.00  -4156.70        40.75\n",
       "26      6193.74   4400.00  -1793.74        40.77\n",
       "530     2605.93   4400.00   1794.07        40.77\n",
       "734     7990.62  13499.00   5508.38        40.81\n",
       "38      8735.15   6200.00  -2535.15        40.89\n",
       "723     4654.45   3300.00  -1354.45        41.04\n",
       "626    19776.88  13995.00  -5781.88        41.31\n",
       "706     5794.06   4100.00  -1694.06        41.32\n",
       "614     7601.07  12999.00   5397.93        41.53\n",
       "102    14436.30  10200.00  -4236.30        41.53\n",
       "36      8733.87  15000.00   6266.13        41.77\n",
       "671    21783.59  37500.00  15716.41        41.91\n",
       "779     7804.30  13500.00   5695.70        42.19\n",
       "765     5592.98   9700.00   4107.02        42.34\n",
       "356     7392.21  12850.00   5457.79        42.47\n",
       "506    31058.31  53999.00  22940.69        42.48\n",
       "775    46871.86  81500.00  34628.14        42.49\n",
       "758     8782.09  15300.00   6517.91        42.60\n",
       "224    15432.90  27000.00  11567.10        42.84\n",
       "278    16257.74  28500.00  12242.26        42.96\n",
       "202    29591.72  52222.00  22630.28        43.33\n",
       "324    18401.05  32500.00  14098.95        43.38\n",
       "557     9965.51   6950.00  -3015.51        43.39\n",
       "397     5940.74  10500.00   4559.26        43.42\n",
       "508    10176.78  17999.00   7822.22        43.46\n",
       "647    26006.24  46000.00  19993.76        43.46\n",
       "638    14692.36  25999.00  11306.64        43.49\n",
       "137     7081.13  12600.00   5518.87        43.80\n",
       "8      19320.02  35000.00  15679.98        44.80\n",
       "378     7719.88  13999.00   6279.12        44.85\n",
       "201     2867.14   5200.00   2332.86        44.86\n",
       "279    18598.57  33900.00  15301.43        45.14\n",
       "256    25853.04  47300.00  21446.96        45.34\n",
       "697    67521.65 124000.00  56478.35        45.55\n",
       "566    24339.92  45000.00  20660.08        45.91\n",
       "773    11829.68  21900.00  10070.32        45.98\n",
       "460    26179.52  17900.00  -8279.52        46.25\n",
       "79     22008.72  41000.00  18991.28        46.32\n",
       "90     14424.69   9850.00  -4574.69        46.44\n",
       "9      10696.32  19995.00   9298.68        46.51\n",
       "678    20749.78  38888.00  18138.22        46.64\n",
       "124    26434.23  18000.00  -8434.23        46.86\n",
       "447     5300.05   3600.00  -1700.05        47.22\n",
       "25      1315.05   2500.00   1184.95        47.40\n",
       "408     4940.94   9400.00   4459.06        47.44\n",
       "705    14752.75   9999.00  -4753.75        47.54\n",
       "55      8747.68  16700.00   7952.32        47.62\n",
       "543    13287.51   9000.00  -4287.51        47.64\n",
       "405     7840.57  15000.00   7159.43        47.73\n",
       "379    17384.11  11700.00  -5684.11        48.58\n",
       "245    27426.54  18400.00  -9026.54        49.06\n",
       "629     3876.50   2600.00  -1276.50        49.10\n",
       "735    78840.45 154999.00  76158.55        49.13\n",
       "95      4819.96   9500.00   4680.04        49.26\n",
       "624    10351.77  20500.00  10148.23        49.50\n",
       "666     8530.10   5699.00  -2831.10        49.68\n",
       "729    38723.42  77000.00  38276.58        49.71\n",
       "17     29202.31  19500.00  -9702.31        49.76\n",
       "541    14801.26  29500.00  14698.74        49.83\n",
       "142     6520.72   4350.00  -2170.72        49.90\n",
       "724    54778.40  36500.00 -18278.40        50.08\n",
       "464    99473.69 199999.00 100525.31        50.26\n",
       "374    99473.69 199999.00 100525.31        50.26\n",
       "714    15182.41  10100.00  -5082.41        50.32\n",
       "718    42870.80  28500.00 -14370.80        50.42\n",
       "419    32042.10  64999.00  32956.90        50.70\n",
       "649    10252.61   6800.00  -3452.61        50.77\n",
       "427     3188.66   6500.00   3311.34        50.94\n",
       "249     9819.79   6500.00  -3319.79        51.07\n",
       "129     2271.06   1499.00   -772.06        51.50\n",
       "609     6233.63  12900.00   6666.37        51.68\n",
       "463    24991.96  52000.00  27008.04        51.94\n",
       "529     2477.09   5200.00   2722.91        52.36\n",
       "197     4757.26   9999.00   5241.74        52.42\n",
       "515    23204.90  15200.00  -8004.90        52.66\n",
       "536     5064.97  10700.00   5635.03        52.66\n",
       "694     4583.45   9700.00   5116.55        52.75\n",
       "414     6116.56   4000.00  -2116.56        52.91\n",
       "416    43729.54  28500.00 -15229.54        53.44\n",
       "147    42216.50  90999.00  48782.50        53.61\n",
       "242    13388.55   8700.00  -4688.55        53.89\n",
       "57      6205.29  13500.00   7294.71        54.03\n",
       "627    22798.48  14800.00  -7998.48        54.04\n",
       "302     4314.35   2800.00  -1514.35        54.08\n",
       "759    14154.08  30900.00  16745.92        54.19\n",
       "555    28229.88  18300.00  -9929.88        54.26\n",
       "155     6024.81  13200.00   7175.19        54.36\n",
       "564     1791.17   4000.00   2208.83        55.22\n",
       "370    57288.57 129222.00  71933.43        55.67\n",
       "590     7799.96  17777.00   9977.04        56.12\n",
       "571     7826.62   4999.00  -2827.62        56.56\n",
       "588    11257.44   7170.00  -4087.44        57.01\n",
       "61     11786.16  27500.00  15713.84        57.14\n",
       "188     7308.40   4650.00  -2658.40        57.17\n",
       "126     7136.13   4500.00  -2636.13        58.58\n",
       "584    35315.23  85555.00  50239.77        58.72\n",
       "442     2805.75   6800.00   3994.25        58.74\n",
       "440    29994.27  72700.00  42705.73        58.74\n",
       "453     1195.37   2900.00   1704.63        58.78\n",
       "533    40508.63  25500.00 -15008.63        58.86\n",
       "141    17748.19  44000.00  26251.81        59.66\n",
       "317    20108.52  49999.00  29890.48        59.78\n",
       "373     3047.73   1900.00  -1147.73        60.41\n",
       "230    32103.32  81500.00  49396.68        60.61\n",
       "660    24795.52  63200.00  38404.48        60.77\n",
       "262     2298.39   5900.00   3601.61        61.04\n",
       "113    20782.19  12800.00  -7982.19        62.36\n",
       "774    42727.45 115000.00  72272.55        62.85\n",
       "745     4961.92  13566.00   8604.08        63.42\n",
       "51     42052.90 115000.00  72947.10        63.43\n",
       "216    13145.44   8000.00  -5145.44        64.32\n",
       "64     14223.15   8650.00  -5573.15        64.43\n",
       "231    38630.65 109900.00  71269.35        64.85\n",
       "6      36938.43 107000.00  70061.57        65.48\n",
       "766    19031.56  11500.00  -7531.56        65.49\n",
       "680    19041.16  11500.00  -7541.16        65.58\n",
       "478     2821.51   8200.00   5378.49        65.59\n",
       "111    17667.73  10600.00  -7067.73        66.68\n",
       "473     9225.60   5500.00  -3725.60        67.74\n",
       "86      5537.91   3300.00  -2237.91        67.82\n",
       "489     5878.05   3500.00  -2378.05        67.94\n",
       "435    36250.98  21300.00 -14950.98        70.19\n",
       "691    10937.47   6400.00  -4537.47        70.90\n",
       "14      6498.94   3800.00  -2698.94        71.02\n",
       "269     6513.43   3799.00  -2714.43        71.45\n",
       "554    10981.78   6400.00  -4581.78        71.59\n",
       "722    70830.57 249999.00 179168.43        71.67\n",
       "598     2408.45   1400.00  -1008.45        72.03\n",
       "11      5335.96   3100.00  -2235.96        72.13\n",
       "499    45192.52 169000.00 123807.48        73.26\n",
       "769    40758.50  23500.00 -17258.50        73.44\n",
       "68     11018.67   6350.00  -4668.67        73.52\n",
       "403    52973.22  30500.00 -22473.22        73.68\n",
       "418     9207.21   5300.00  -3907.21        73.72\n",
       "451     3958.95  15500.00  11541.05        74.46\n",
       "352     8656.61   4900.00  -3756.61        76.67\n",
       "220    35582.50  19990.00 -15592.50        78.00\n",
       "364    10101.76   5600.00  -4501.76        80.39\n",
       "659    35063.02 200000.00 164936.98        82.47\n",
       "298    40968.92  22000.00 -18968.92        86.22\n",
       "390    46839.68  25000.00 -21839.68        87.36\n",
       "760     3687.51   1950.00  -1737.51        89.10\n",
       "537    15148.77   8000.00  -7148.77        89.36\n",
       "161    11283.76   5900.00  -5383.76        91.25\n",
       "326    12397.72   6399.00  -5998.72        93.74\n",
       "754    19711.93   9999.00  -9712.93        97.14\n",
       "518    27468.28  13900.00 -13568.28        97.61\n",
       "121     3752.09   1850.00  -1902.09       102.82\n",
       "208    21409.28  10500.00 -10909.28       103.90\n",
       "446     9697.61   4700.00  -4997.61       106.33\n",
       "313    10335.12   5000.00  -5335.12       106.70\n",
       "494    57562.85  27500.00 -30062.85       109.32\n",
       "301    45971.24  21300.00 -24671.24       115.83\n",
       "597    10799.27   5000.00  -5799.27       115.99\n",
       "239     9082.80   4200.00  -4882.80       116.26\n",
       "740    15142.34   7000.00  -8142.34       116.32\n",
       "311    20821.04   9500.00 -11321.04       119.17\n",
       "58     31594.67  14300.00 -17294.67       120.94\n",
       "371    16480.47   7200.00  -9280.47       128.90\n",
       "539     5596.19   2400.00  -3196.19       133.17\n",
       "60     17515.30   7150.00 -10365.30       144.97\n",
       "727     8619.53   3500.00  -5119.53       146.27\n",
       "470     6111.81   2450.00  -3661.81       149.46\n",
       "100    13806.26   5500.00  -8306.26       151.02\n",
       "613    17515.30   6750.00 -10765.30       159.49\n",
       "632     3133.39   1200.00  -1933.39       161.12\n",
       "736    26124.98   9750.00 -16374.98       167.95\n",
       "228     2333.07    800.00  -1533.07       191.63\n",
       "491    13702.85   4600.00  -9102.85       197.89\n",
       "682     7151.28   2400.00  -4751.28       197.97\n",
       "198    18274.28   6000.00 -12274.28       204.57\n",
       "243    14638.44   4800.00  -9838.44       204.97\n",
       "574     3887.69   1200.00  -2687.69       223.97\n",
       "195     4419.56   1350.00  -3069.56       227.38\n",
       "366    13937.37   4100.00  -9837.37       239.94\n",
       "522    16578.54   4800.00 -11778.54       245.39\n",
       "772     9508.35   2750.00  -6758.35       245.76\n",
       "59      7126.24   1500.00  -5626.24       375.08\n",
       "88     39080.14   6500.00 -32580.14       501.23\n",
       "444    38529.33   4800.00 -33729.33       702.69\n",
       "409    46409.36   2400.00 -44009.36      1833.72"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sometimes it is useful to check these outputs manually\n",
    "# To see all rows, we use the relevant pandas syntax\n",
    "pd.options.display.max_rows = 999\n",
    "# Moreover, to make the dataset clear, we can display the result with only 2 digits after the dot \n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "# Finally, we sort by difference in % and manually check the model\n",
    "df_pf.sort_values(by=['Difference%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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