LR-Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b5954ba",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9bf2087",
   "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",
   "id": "7fe1019e",
   "metadata": {},
   "source": [
    "# Loading the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b258e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('dataset.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6b84dc",
   "metadata": {},
   "source": [
    "### Exploring descriptive statistics of the variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af5f0cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4345</td>\n",
       "      <td>4173.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4195.000000</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345</td>\n",
       "      <td>4345.000000</td>\n",
       "      <td>4345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E-Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>3947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19418.746935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.237284</td>\n",
       "      <td>2.790734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.550058</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25584.242620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.705797</td>\n",
       "      <td>5.066437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.719097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4345    4173.000000   4345  4345.000000  4195.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           936            NaN   1649          NaN          NaN   \n",
       "mean           NaN   19418.746935    NaN   161.237284     2.790734   \n",
       "std            NaN   25584.242620    NaN   105.705797     5.066437   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    86.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   155.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year    Model  \n",
       "count         4345         4345  4345.000000     4345  \n",
       "unique           4            2          NaN      312  \n",
       "top         Diesel          yes          NaN  E-Class  \n",
       "freq          2019         3947          NaN      199  \n",
       "mean           NaN          NaN  2006.550058      NaN  \n",
       "std            NaN          NaN     6.719097      NaN  \n",
       "min            NaN          NaN  1969.000000      NaN  \n",
       "25%            NaN          NaN  2003.000000      NaN  \n",
       "50%            NaN          NaN  2008.000000      NaN  \n",
       "75%            NaN          NaN  2012.000000      NaN  \n",
       "max            NaN          NaN  2016.000000      NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe(include=\"all\")\n",
    "\n",
    "# Note that categorical variables don't have some types of numerical descriptives\n",
    "# and numerical variables don't have some types of categorical descriptives "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778dae79",
   "metadata": {},
   "source": [
    "### Determining the variable of interest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6afbf2d3",
   "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",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </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 = raw_data.drop(['Model'], axis = 1)\n",
    "data.describe(include='all')\n",
    "\n",
    "# Dropped the Model column as it has 312 dummy variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23518df4",
   "metadata": {},
   "source": [
    "### Dealing with missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "786e472f",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As you can see, Columns Price and EngineV are counted not the same as others \n",
    "\n",
    "data.isnull().sum() # data.isnull() # shows a df with the information whether a data point is null "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a31d4a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_mv = data.dropna(axis=0)\n",
    "# This is not always recommended, however, when we remove less than 5% of the data, it is okay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f7ed50f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025.000000</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025</td>\n",
       "      <td>4025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1861</td>\n",
       "      <td>3654</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19552.308065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.572174</td>\n",
       "      <td>2.764586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.379627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25815.734988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.394703</td>\n",
       "      <td>4.935941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.695595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21900.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           880            NaN   1534          NaN          NaN   \n",
       "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
       "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
       "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
       "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
       "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
       "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         4025         4025  4025.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1861         3654          NaN  \n",
       "mean           NaN          NaN  2006.379627  \n",
       "std            NaN          NaN     6.695595  \n",
       "min            NaN          NaN  1969.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2007.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking descriptive Statistics without missing values now \n",
    "data_no_mv.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02173ea",
   "metadata": {},
   "source": [
    "### Exploring PDFs (Probability Distribution Function)\n",
    "- The PDF will show us how that variable is distributed \n",
    "- This makes it very easy to spot anomalies, such as outliers\n",
    "- OUTLIERS: observations that lie on abnormal distance from other observations in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb757089",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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']) # Obviously, we have some outliers in Price columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f3e90d",
   "metadata": {},
   "source": [
    "### Dealing with outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a55308c3",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Without diving too deep into the topic, we can deal with the problem easily by removing 0.5%, or 1% of the problematic samples\n",
    "# Here, the outliers are situated around the higher prices (right side of the graph)\n",
    "# Logic should also be applied\n",
    "# This is a dataset about used cars, therefore one can imagine how $300,000 is an excessive price\n",
    "\n",
    "# Outliers are a great issue for OLS, thus we must deal with them in some way\n",
    "\n",
    "# Let's declare a variable that will be equal to the 99th percentile of the 'Price' variable\n",
    "q = data_no_mv['Price'].quantile(0.99)\n",
    "# Then we can create a new df, with the condition that all prices must be below the 99 percentile of 'Price'\n",
    "data_1 = data_no_mv[data_no_mv['Price'] < q]\n",
    "# In this way we have essentially removed the top 1% of the data about 'Price'\n",
    "data_1.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2c55a64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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_1['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee9bca5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 26,
     "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": [
    "# we continue treating other numeric values the sama way \n",
    "\n",
    "sns.distplot(data_1['Mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9ba639c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = data_1['Mileage'].quantile(0.99)\n",
    "data_2 = data_1[data_1['Mileage'] < q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0c47a363",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 28,
     "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": 29,
   "id": "59ef42ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 29,
     "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": [
    "# The situation with engine volume is very strange\n",
    "# In such cases it makes sense to manually check what may be causing the problem\n",
    "# In our case the issue comes from the fact that most missing values are indicated with 99.99 or 99\n",
    "# There are also some incorrect entries like 75\n",
    "sns.distplot(data_no_mv['EngineV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e09d1e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A simple Google search can indicate the natural domain of this variable\n",
    "# Car engine volumes are usually (always?) below 6.5l\n",
    "# This is a prime example of the fact that a domain expert (a person working in the car industry)\n",
    "# may find it much easier to determine problems with the data than an outsider\n",
    "data_3 = data_2[data_2['EngineV']<6.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2457fe38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 31,
     "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": 32,
   "id": "5812a62e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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twm6/fHPHZDLx8MMPM3fuXBYuXEhBQUEb0xJCBLrci5e/6XvS7XRFj/AQhg6I4UBOiUff/r86XozN7mTYgJavJq7oZ4xEpYKs3FKPY+lKPCoUL7/8MrNmzeKJJ55g6dKlLF26lGXLlrk9pqSkhI0bN/Luu++yc+dOtm3bRl5eXpN90tPTWb16Nbt370ZRFLZv3w7Ar371K6ZOnUpGRgZpaWls2LChnekJIQLVyQsVhIYEER2hbdNxtw01UlrZwLmiGrf7OZ0Ke78txBATRs/oMLf7akOCiO8ZzrenS7tl95NHheKf//wne/bs4V//+pfr57PPPnN7zL59+5gwYQLR0dHodDpSUlLIzMx0bS8sLKShoYHRo0cDMG/ePDIzMzGbzZw8eZKFCxcCMH/+fH7xi1+0LzshREByKgq5FyvpHadDpXL/WOy1brm5J5ogNfuOFbnd75tTpZirrde9mriiryECc7WVS+Vtf/w20Hm0HkXv3r3bPNeTyWRCr//+UTODwdBk6dRrt+v1ekpKSsjPzyc+Pp7f/OY3fP3118THx7d5ZHhcXESb9vcVvf76j/wFMskvsPkyv7PfPY562/BeREaENtkWHKxp1gag02nRfzcob9LoePYfK+bRBaMJ0zb/mOvZM4LPvvkWfXQYQwf1RH1NMWrpHDf3j+Wr4yWcN1kYPbSXR3ko5roWY7023o7W0b87jwrFD37wA1566SWmTZtGaOj3SQ8fPrzVY1q6PLv6m0Fr2+12OydOnGDp0qWsWrWK9957j5UrV7JlyxZPQgWgvLzW75dI1OsjKS11f2kcyCS/wObr/L74Nh+AmIgQamobmmyz2ezN2gDq6qyUOhwATBxm5N9ZBXz4nzzuHJPQZD+9PpK9By+Qe7GCn0wbgsVibfZerZ3DGBvG10cvcfswg0d51Flbfp9r4+1I7fndqdUqt1+wPSoUO3bsAGjSdaRSqdx2PxmNRg4dOuR6bTKZMBgMTbaXlZW5XpeWlmIwGNDr9YSHhzN16lQA5syZw9q1az0JUwjRRRw/Z6Z3nA5dqEcfUc0Miu9BP2ME//qmgCmj45t9Sd35xTlie2iZMLyXa7CdJ4b2j+XLo0XdbmZZj+5RXH1vwtN7FBMnTmT//v2YzWbq6+vZs2cPkydPdm1PSEhAq9WSlZUFQEZGBpMnT6Zfv34YjUb+85//APDvf//b7ZWLEKJrabQ5OJVfRWI/z+4dtESlUjFjbF8KSi3sO1bcZNtXx4o5U1jN7B8MQBPk0Uegy9ABMdjsTk4VVLY7tkDk0f9LFouFNWvW8MADD1BZWcmzzz6LxeJ+6UGj0cjy5ctZvHgxc+fOZc6cOSQnJ7NkyRKOHj0KwIYNG1i/fj0zZ86kvr6exYsXA7Bp0yb+8pe/MGfOHN5++20ZsyFEN3K6oAq7w0lSKwPgPPWDEb0YnNCDbf/Ko6auEbg8NuNP2w/TzxDBpOTebX7PIQlRaIJUnDhfcUOxBRqPruvWrl2LwWCgvLwcrVZLbW0tzz77LL/73e/cHpeamkpqamqTts2bN7v+nZSUxPvvv9/suEGDBrXpnoQQous4ft5MkFrFkD6X15hoL7VKxQMpSbzw1kE2/O0wd45J4F/fFGCzO3h07gg0QWqsbZzCNSQ4iIG9e5B7sbLdcQUij64ocnJyWL58ORqNhrCwMDZs2EBOTo63YxNCdEMnzpm5qU8U2g64B9DHEMH/zB1Bbb2NLbtzsTY6+NX9Y91OWX49if2iuVBcQ701cGZ/vVEeXVGo1U3ricPhaNYmhBA3qsrSyEVTLfMmD+qw9xxzs56hA2IoLLUwsHcPjMYeN/REV2LfGD7cd4EzhVXdZkEjjz7tx40bx8svv0xDQwOff/45TzzxBLfddpu3YxNCdDM5580ADB8Y26HvGxqiYXBC1HXXtPDEkIQogtQqcvMrbzywAOFRofjlL3+JTqcjMjKSP/zhDyQlJfGrX/3K27EJIbqZ4+fNhIdq6O/B+hO+og0JYkCvyG51n+K6XU+ffPIJb7zxBrm5uYSGhpKYmMgtt9yCVtu2+VeEEMIdRVE4cb6Cof1jOuSbvzfd3C+aPQfysdocHXIvxd+5LRQZGRm8+uqrLFu2jKSkJFQqFUePHmXdunVYrVbuuuuuzopTCNHFFZXXUVFj7fBup46kUquwWO3079UDh1Ph+Hkzif1i0AZr0HTh27ZuC8WWLVt46623iI+Pd7UNHjyYUaNG8fTTT0uhEEJ0mGPfrUs9fID/FgqrzcGRU6U02h2ogP8evkS1pZFxQ41oWphTqqtwWwNtNluTInHFwIEDsVqbz48ihBDtdeRMOQk9w6875bc/CNEEEdsjlBJz95hJ1m2hCApqve+tO87JLoTwjnqrnVP5lSQPDpzHTY2xYZRWNeBwtG3QXiDqwr1qQohAcfycGYdTCbBCocPpVCitanl22K7Ebadabm4ut9xyS7N2RVFobGz0WlBCiO7lyJkydFoNQ/pE+ToUjxljLneRdYfuJ7eF4pNPPumsOIQQ3ZTD6ST7TDkjBsUSFEAzPoQEBxETqaXEXO/rULzObaFISEhwt1kIIW7YqfwqaupsjE30bDEgf9IrVsep/ErsXfw+ReCUbyFEl3TopImQYDUjA+j+xBXG2DAcToWLxV13tUOQQiGE8CGnUyHrVCnJg+ICcoSz4bv7FKcLq3wciXdJoRBC+MzpgkqqLY2MTQq8bie4PNlgdEQIeQVSKIQQwiv2HStGGxwUUI/FXssYq+PspaoufZ9CCoUQwiesjQ4OnDQxLslAaEjgTn9hjNXRaHNyoaTr3qeQQiGE8IlDuSasjQ7uaMfa1f7kyniKU1142nEpFEIIn/jyaBGGmDBuCqBBdi0J02owxoZ16YWMpFAIITpdgamWkxcrmZTcG5XKv9ee8MSQhGhOF1TidHbNOfCkUAghOt2eQ/mEBKuZMrprDOod0ieKequDi6aueZ9CCoUQolNVWRr56ngxt4/oTURYsK/D6RBXus+66vKoUiiEEJ3q00P5OBwKM8b19XUoHSYqQkuvWB3Hz5t9HYpXSKEQQnSaKksjnxzKZ/wwI71idb4Op0ONGBRL7sVKrDaHr0PpcFIohBCd5qN957HbFebeMdDXoXS45MFx2OxOci9W+DqUDufVQrFr1y5mzZrFjBkz2Lp1a7PtOTk5zJ8/n5SUFFatWoXdbm+y/cSJE4wYMcKbIQohOklRuYW9hwu5I7kXxi52NQGQ2DeakGA1R890ve4nrxWKkpISNm7cyLvvvsvOnTvZtm0beXl5TfZJT09n9erV7N69G0VR2L59u2tbfX09a9aswWazeStEIUQnURSFrZ+cIlgTxD2TB/s6HK8I1gQxtF8M2WfLutxS0V4rFPv27WPChAlER0ej0+lISUkhMzPTtb2wsJCGhgZGjx4NwLx585ps/+1vf8uDDz7orfCEEJ3o6xMlnDhfwfwpg4gKD/F1OF6TPDiO0soGisq71qp3XptgxWQyodfrXa8NBgPZ2dmtbtfr9ZSUlADw2Wef0dDQwN13392uc8fFRbQz6s6l10f6OgSvkvwCW0flV1pRz9ZPT5PYL4YFM5IIUns2wE4x1xEZEdqsPThY02K7TqdF72GX1pXc2nqO65172oQBbNlzipMFVYwa2qvV929rvG3V0X+bXisULV16XT0Cs7XtpaWlvPbaa7z11lvtPnd5ea3fj5DU6yMpLe2ag3NA8gt0HZWf06mw4W/fYrc7eXBmIubyWo+PrbPaqaltaNZus7XcXldnpdRx/SeOrs6trefw5NyDE3rw328K+OHo+Fbfvy3xtlV7fndqtcrtF2yvdT0ZjUbKyspcr00mEwaDodXtpaWlGAwG9u7dS2VlJYsWLSItLQ2AtLQ0ams9/wMTQviHD/5zhpMXK1k042aMMc2/PdudYLHaW/zx8+96rbr1ZgMXTbWYKrvOWtpeu6KYOHEif/rTnzCbzYSFhbFnzx5efPFF1/aEhAS0Wi1ZWVnceuutZGRkMHnyZO69917uvfde136JiYns3LnTW2EKIbxk//Fi/vn1RaaOSWh1hlirzc7BnJIWt426Wd9iu78bm6hn+7/zyMo1MbmLTFHi1SuK5cuXs3jxYubOncucOXNITk5myZIlHD16FIANGzawfv16Zs6cSX19PYsXL/ZWOEKITnTsbDlvfpRDUr9ofjr9Jl+H06l6RocxsHckXx1vuQAGIq+uFpKamkpqamqTts2bN7v+nZSUxPvvv+/2PXJzc70SmxDCO3IuVLDp70eJ7xnOE/OS0QT5dlyv3Xn5yuUKxVxHnfXya291b90+sjfv7DlFvqlrdJkH7rJSQgi/k32mjD///Rj66DD+vx+PQhfq+4+Ya7u3IiNCXTeYvdW9ddswI3/7LI+vjxfTv1fgPx0nU3gIIW6Yoih8llXAK+9n0ztOx4r7xhAVofV1WD4THhrMLTf35OBJEw5n4K+lLYVCCHFD6q12Nu86wdZPTjFqcE9WLrqFSF3XHVTnqTuSe1PXYCffZPF1KDfM99eFQoiApCgKR/LKeeeTXCpqrNwzaSCzfzAAtYcD6rq6Yf1jiY4I4UxBFQMCvPtJCoUQos1OXqgg44tznMqvJKFnOI/+bARDElpf+/raG8pXBOpYCU+o1SpuG2Zkz4F86hps6EIDd5EmKRRCCI/lXqwg4/Nz5OZXEhURwn3Tb+LOMQnXfbKptfESgTpWwlO3De/F7gP5nCmsZuTgOF+H025SKIQQ15V7sYKdX5zj5MXLBeKn02/iztHxBGuCmuzXHa8c3NFHh2GMCSOvsIoRg2KbTGMUSKRQCNHFtPZhrQ3WoGnj4ytllfX89bPTfHu6jKjwEH467SamjI4nJDioxf2765WDSq3CYm25QA7pE8WXR4spqagP2FX9pFAI0cW09mE9bqgRjdbz/+S/Ol7M/9udi6IozJ8yiBlj+7ZaILo7q83BkVOlzdpH3aynf69IDuSYyCuokkIhhAhM116BOBWFbe8dZvdXFxjSJ4qHU4fRMyrMhxEGNk2QmoG9e3CmsIrxQw0BWWylUAjRzV19BaIoCvuOFnPmUjWTRsWzOOVmgtQy3OpG3dQnilP5lZwtqiapX4yvw2kz+QsQQgCXi8SBHBNnLlUzbpiRe6cOliLRQeKiQontoSWvoMrXobSL/BUIIQA4fs5M7sVKhg2IYfywXgH7hI6/GpIQhbnaSnlVywsZ+TMpFEIICkstfHOqjAG9Irk18fITSlee5Gnpxx740xd1uoHxPQhSqzgdgFcVco9CiG6uoqaBz7MvER0Rwg9GfH8l0dqTPND2J6gEaIOD6GuM4HxxNeOHGq5/gB+R37QQ3ZjTqfB2Zi5Op8KdYxIIbutACz/ibiyDvxjYuwfni2ooKg+siQKlUAjRjf3z6wvkFVRx+8he9Aj3fMZXf/xQdjeWwV/E99QRolFzrqjG16G0iRQKIbqpfFMtGZ+fY8xNPRkU36NNxwbCh7I/ClKr6dcrkgtFNTTaHYQHSPdd4F5nCiHaze5w8saHJwgP1fDjaTfJE06daECvSGwOJznnK3wdisekUAjRDX247zwXTbUsvjuJiLDAnf46EPWKvdz9lH2m3NeheEwKhRDdzPniaj7cd4EfDDdyi3QVdTq1WkUfQwTHz5YHzDKpUiiE6EYabQ427zpBj/Bg7ptxs6/D6bb6GiKwNNgDZqS2FAohupH39p6hqLyO/zN7GOEBvOJaoIvvGY4mSMW3p8t8HYpHpFAI0U3kXDDzWVYB02/tw/CBsb4Op1sL1qi5uV8Mh6VQCCH8RUOjg3d2n6JXrI6ZE/s3mY7DnwakdSfDBsRgqqynpKLO16FcV2A8xCuEaDdFUdh3rBhLvY1Jo3pz5JpvsTL2wTeGDYgFznDsrBnjrf69oJFXryh27drFrFmzmDFjBlu3bm22PScnh/nz55OSksKqVauw2y+P9MzKymL+/PmkpaXxwAMPUFhY6M0whejSjp4pp8BUy5w7BhDXI9TX4Yjv6KPDMESHceys/z8m67VCUVJSwsaNG3n33XfZuXMn27ZtIy8vr8k+6enprF69mt27d6MoCtu3b3e1r1u3jp07d5KamsratWu9FaYQXVqBqZbDeeUMiu/BpFHxvg5HXGP4oFhOXqzE5ufT8XqtUOzbt48JEyYQHR2NTqcjJSWFzMxM1/bCwkIaGhoYPXo0APPmzSMzM5PGxkaefPJJkpKSAEhMTKSoqMhbYQrRZVVbGvk8u4jYHlomDDfK6Gs/NHJgHFabg7yCSl+H4pbX7lGYTCb0+u/7Pg0GA9nZ2a1u1+v1lJSUEBISQlpaGgBOp5NNmzYxffr0Np07Li7iBqPvHHp9pK9D8CrJzzcUcx1qTRD//raQILWKOXcMIlIXQnCwhsiI5l1PbW1vzzH+do4rr72dh7v8dDotd9zal1czjnKmuJbJ4/q3uF97dPTfptcKhaI0f5Ti6m8019ve2NjIypUrsdvtPPLII206d3l5LU4/f5RDr4+ktDSwZpBsC8nPd8qq6snYm4el3saMcX3B6aSmtgGbzU5NbfPV1Vpqj4wIbXX/1o7pyHZvniMyItT12tt5uMuvrs6KyuFgSEIUB44XM2dCvxb3a6v2/G2q1Sq3X7C91vVkNBopK/v+6QqTyYTBYGh1e2lpqWu7xWLhoYcewm6389prrxEcLAODhPBEQ6Od1zKOUW2xMfWWBPTRYb4OSVzHyEFxFJTWUlFj9XUorfJaoZg4cSL79+/HbDZTX1/Pnj17mDx5smt7QkICWq2WrKwsADIyMlzb09PT6d+/P6+88gohIZ7PkS9Ed2ZtdPDH97O5WFzD5NG96R0X7uuQhAeuDH48ds5/n37yWteT0Whk+fLlLF68GJvNxoIFC0hOTmbJkiUsW7aMkSNHsmHDBp555hksFgvDhg1j8eLFnDhxgs8++4whQ4Ywd+5c4PL9jc2bN3srVCECXr3Vzsb3jnCmsIr7UxJ9HY5og76GCKLCQzh+zsykZP98Ms2rA+5SU1NJTU1t0nb1B35SUhLvv/9+k+3Dhg0jNzfXm2EJ0aXU1tvYuP0wF0tqeSxtBEMHxnIwp8TXYQkPqVQqRgyK5fDpMpxOBbXa/55Okyk8hAhgVZZGXv7rt+Sbann8npGMTTJc/yDhd0YOisPSYOdsUbWvQ2mRFAohAlRZVT2/fSeLkoo6li1IZvRNPX0dkminYQNiUakuj6L3R1IohAhAReUW1r/zDTV1Nn75kzGMGBjn65DEDYgIC2ZQfA+/vaEthUKIAHOhuIb173yDw6mwYtEtDOkT5euQRAcYOSiO80U1VNc1+jqUZqRQCBFAci9W8H/f/QZtcBC/XnQLfQ2BMQuBuL6Rg+JQgOPnzL4OpRkpFEIEiCN5Zfx++xFiIrX8atEtRISHNFlXQtaXCGz9e0USqQvmqB/OJivrUQgRAL46UcwbH+bQxxDB8h+PIihI3eojsLK+RGBSq1SMGBjL0bNmnIqC2o8mcZQrCiH83GdZBWz+xwmGJETxq5+OoYdOZivoqkYMiqO23saFYv+aR0yuKITwU06nwvZ/57HnYD5jburJIz8aTkhwkK/DEl40YmAsKuDo2XIG9u7h63Bc5IpCCD9ktTl4NeMYew7mM/3WPjx+z0gpEt1ApC6EAb17+N19CrmiEMLPlJjreDXjGAWmWn467abLU4WLbiN5cBz/+OIcVZZGosL9o5tRriiE8COHTpp44a2DmKsbePLeZCkS3dCtiXoU4JtTpb4OxUWuKITwA7X1Nt7fe4b/HrnEoPgePJY2grioUOxOsNrszfaXR2C7roSe4RhjdRw6aWLqmARfhwNIoRDdUGsfvtpgDZpOvsZWFIX9x4vZ9q88LPV27r6tH/MmD0ITdDkQq83e4mOw8ghs16VSqRibqOefX12kpq6RSD94yk0Kheh2WvvwHTfUiEbbOf9JKIrC0bNmdn15jjOXqhkU34OnfpJIP6N/rsMtOtfYRAMf7b/AN6dKmTLa91cVUiiE6ESmijoO5ZbyeXYRJeY64npoeXBmEhNG9MZmd2CxNr3SkS6m7qmfMYJesTq+PFYshUKIrqyuwUZReR0FpbWcyq/iVH4l5dUNAAxJiCJ1zlDGDzWiCVJjsUoXk/ieSqViUnJv3tt7hqJyi8+XtZVCIUQ72Z3Q0GijytJIcXkdJeY6SirqKDbXUWKup9ry/Sygkbpgbu4bzV3j+zJmSE96Rof5MHIRCCaO6MUH/znLF9lF3Dt1iE9jkUIhxHU4nQpVlkbM1Q2UVzdQWllPhcVGXn4lRWUWbA6na99gjZqo8BCG9o+hnyGC3nHh9O6pwxAdhsqP5u4R/i8qQkvy4Di+PFbM3EmDCO7sJy2uIoVCCKCh0c65S9XUWBopray//FPVgLm6gYoaK45rbhbERYXSMyqUwQk96BERQnS4lh7hIYRpg1CpVIwbaiS8k26Mi65r2q19OLztMPuOFfn0XoX8JYtux6kolFU1UFRmoayqgfKqBuquuYkcE6mlZ1QoQ/pEEdcjlNgeocT10H73v6H06xPD+YKKVmdwValVzW5MAwRrNNjsMi5CeGbYgBgG9Irkn19d5I7k3gSpfXNVIYVCdAv1Vjsnzps5kldO9ply1ypiPXTBGGPDiIsKZdxQo6sQXH2Z357xFVabgyMtjKwddbO+1XYhrqVSqZgzcQCbdhzlwAkTPxjRyydxSKEQXVaJuY4jeWUcOVPOqfxKHE6FMK2Gof1jCNMGkaAPJzTk+/8EhvSN5sipUvJLmk7xPH54L6y2pl/5FXOdXAWITjH6pp70N0ayfW8eo4b0RBfa+R/bUihEl2F3ODmVX/ndVUMZJRX1AMT3DGfGuL6MGhzH4IQorHZnq11GLWnp6iAyIpRB8TI4TnifWqVi8d2JrH37EB/85wz3pyR2egxSKERAq6q1kn3mcnfS8fNmGhodaILUJPWPZvrYviQPjkN/zaOoVruzlXcTwj8N7N2D6bf25ZND+dzcN5rbhhk79fxSKERAsdmd5BVWcfycmePnzFz4rpsoOiKEWxMNDB8Yy839ookI07puGstoZ9EVLLhzEOeLq3njoxP00AUzdEBsp53bq4Vi165dvPbaa9hsNh588EEWLVrUZHtOTg7PPPMMtbW1jB07lhdeeAGNRsOlS5dIT0+nvLycgQMHsmHDBsLDfTsyUfhGvdXOuaJqzlyqJq+gitz8ChptToLUKgYnRDHn9gGgKMREalGpVDQ02snOK2v1pjHIjWMRmII1QSydn8xvt37D77cf4d6pQ5h+ax/Uau+Pz/FaoSgpKWHjxo3s2LGDkJAQFi5cyG233caQId+PMExPT2ft2rWMHj2ap59+mu3bt3PffffxwgsvcN999zF79mz+/Oc/8+qrr5Kenu6tUMV1OJxOGhod1Nbbqa5rpKHRTkOjA2ujA7vDiSYoiCA1qLj8lEaQWkWcyYKltoEgtYqgIDWaIDWaoCv/VqFRq1GpoNHuxNrooM5qp6rWSmVtI+aaBorL6ygqt1BebXXF0TtOxx0jezNiYByJ/aIJ02panfpCiK4oIiyYp392K298dIK/fXaa/xwuZMa4voy5Se/VRY68Vij27dvHhAkTiI6OBiAlJYXMzEyeeOIJAAoLC2loaGD06NEAzJs3jz/+8Y/ce++9HDx4kD//+c+u9p/97GdtKhTtqbBOReFwXhnVtY0oioKigAIofPfvq9pQ+L6N7//X+f1Bl6m+/1/1d6NyVYAKFWFhwTRYbahQobpqv8sftt/loVK52kCFKy2VyvWhrKB8F4/yXR6XA1SU7+Lhqq6Wq+NWwKEo2OwObHYnjTYnjVf9u8Fmx2pzYm20Y+vkPv3gYDX6qDDGJhkxxITS1xBJfM8IgoKa/l6tdidqtRpdaHCz99AEtdzubltb2sO0Gq+foz3tHfVevs7Pm+cI02pw2IM7JY/r5deez6oIXTDLFiRz9KyZTw/lk/n1RTK/vkhMhJaU2/oxQx/Z5ve93v5eKxQmkwm9/vtLfIPBQHZ2dqvb9Xo9JSUlVFRUEBERgUajadLeFjEx7eummtFTnmIJRP3io1psH9QnptVjWtvWUe2dcY6unl9XOYe7c9+IqT0jmTq+f4vb4uIiOvRcXhvmd+Ub7tWunuumte3XO04IIUTn8lqhMBqNlJWVuV6bTCYMBkOr20tLSzEYDMTGxlJbW4vD4WjSLoQQwje8VigmTpzI/v37MZvN1NfXs2fPHiZPnuzanpCQgFarJSsrC4CMjAwmT55McHAwY8eO5eOPP27SLoQQwjdUSkt9PR1k165dvP7669hsNhYsWMCSJUtYsmQJy5YtY+TIkZw8eZJnnnkGi8XCsGHDWL9+PSEhIRQWFrJy5UrKy8vp3bs3v//974mKarkfWgghhHd5tVAIIYQIfL5bCUMIIURAkEIhhBDCLSkUQggh3JJCIYQQwi0pFB2straWOXPmUFBQAMCOHTuYNWsWqamprF27FrvdTnl5OWlpaa6fH/7wh4wZMwaA6upqHn74YWbOnMmiRYsoLW15Yjtf8SQ/gIKCAhYtWkRaWhr3338/hYWFADQ2NpKens7MmTO55557OHPmjM9yaYmn+WVnZzN//nxSU1N55JFHXL8nf85v06ZNzJ49m9mzZ/PSSy8Bl6faSU1N5a677mLjxo2ufXNycpg/fz4pKSmsWrXKlfelS5dYtGgRd999N4899hgWi8UnuVyrLbldsWLFCnbs2OF67a+5Qdvy+/TTT0lLS+NHP/oR//M//0NVVRVwg/kposMcPnxYmTNnjjJ8+HAlPz9fOXPmjDJp0iSlpKREURRFee6555Q333yzyTEOh0P52c9+pvzjH/9QFEVRXnjhBeX1119XFEVR/v73vytPPvlkp+bgTlvy++Uvf6ls3bpVURRFefvtt5WnnnpKURRF+ctf/qKsXr1aURRFOXDggLJgwQIfZNIyT/NzOp3KlClTlP379yuKoigfffSR8sgjjyiK4r/5ffnll8pPfvITxWq1Ko2NjcrixYuVXbt2KVOmTFEuXryo2Gw25ec//7myd+9eRVEUZfbs2cq3336rKIqi/PrXv3b9Lh9++GHlww8/VBRFUTZt2qS89NJLPsnnam3Nrbi4WHnkkUeU5ORk5YMPPnC9jz/mpihty6+mpka5/fbbleLiYkVRFOUPf/iD8uKLLyqKcmP5yRVFB9q+fTvPPfecayR5bm4uo0ePdr2eOnUqn376aZNjPvjgA8LCwkhNTQVg7969rn/PmTOH//73v9hstk7MonVtyc/pdFJbWwtAfX09oaGhwOX8fvSjHwEwbtw4KioquHTpUmen0iJP86uoqKChoYEJEya42r/44gsaGxv9Nj+9Xs/KlSsJCQkhODiYwYMHc/78efr370/fvn3RaDSkpqaSmZnZ4oSdmZmZ2Gw2Dh48SEpKSpN2X2tLbnB5fNe0adOYOXOm6z38NTdoW342m43nn38eo/HywkaJiYkUFRXdcH5SKDrQunXrGDt2rOt1UlISR44coaioCIfDQWZmZpNpSxwOB6+99hpPPfWUq+3qyRI1Gg0RERGYzebOS8KNtuT35JNP8tZbbzFp0iTefPNNlixZArQ8GWRxcXHnJtIKT/OLiYlBp9PxxRdfAPDRRx9hs9moqKjw2/xuuukm1wf/+fPn+fjjj1GpVM0m7iwpKfHqhJ3e0JbcAB566CHuvffeJu/hr7lB2/KLiYlh+vTpADQ0NPC///u/TJ8+/Ybzk0LhRQMHDuSpp57iscceY9GiRSQmJhIc/P2Uw59//jkDBw4kMdH9GrhqtX/+mtzlt2LFCtasWcPnn3/OCy+8wBNPPNHihI8QePmpVCr++Mc/8vrrrzN37lxqamqIjo5u8ru9mj/ld/r0aX7+85+zYsUK+vXr12y7u4k5W2v3F57k1hp/zw3all9NTQ1LliwhKSmJe+6554bz85+/4C7IarWSnJxMRkYGf/vb34iPj6dv376u7Z9++imzZs1qcozBYHB9K7fb7dTW1rrW9PA3reVnNps5e/as65tNSkoKpaWlVFRUYDAYmtyg9+dJH939/jQaDVu2bCEjI4N77rkHp9NJdHS0X+eXlZXFgw8+yFNPPcU999zT6sSdgThhp6e5tcafc4O25WcymbjvvvtISkpi3bp1wI3nJ4XCi+rq6njggQeora2lsbGRLVu2NCkMhw8fbtLVATBlyhQyMjIA+Pjjjxk7dmyr31R9rbX8YmJi0Gq1HDp0CLj8Rx4eHk5sbCxTpkxh586dABw6dAitVkt8fLwv02iVu9/f008/7Vpf5c033+Tuu+9GrVb7bX5FRUU8/vjjbNiwgdmzZwMwatQozp07x4ULF3A4HHz44YdMnjw54CbsbEturfHX3KBt+TkcDh599FFmzpzJqlWrXFcNN5qfzPXkBT/84Q95++236dOnD++99x5vvfUWdrudOXPmsHTpUtd+o0aN4sCBA2i1WldbZWUlK1euJD8/n8jISDZs2ECfPn18kUarPMkvOzubF198kYaGBsLDw3n22WcZNmwYVquVZ599lmPHjhESEsLatWsZPny4jzNqytP8nnvuOerr60lMTGTdunVERET4bX5r167lgw8+aNJlsXDhQgYMGMD69euxWq1MmTKFX//616hUqoCasLOtuV2xcuVKxo8fz7x58wD8MjdoW36ffvopS5cubdKdPWLECNatW3dD+UmhEEII4ZZ0PQkhhHBLCoUQQgi3pFAIIYRwSwqFEEIIt6RQCCGEcEsKhRA3YM2aNfz4xz92DWSCy1OzLFy4sMUZS4UIRFIohLgBK1asoK6ujtdff93V9vrrrxMUFMSyZct8GJkQHUfGUQhxg3Jzc/npT3/K1q1bURSFhx9+mA8++ID//ve//PWvf3VN77F69WoGDx7MuXPnWLNmDXV1dZhMJpKSkvjDH/6AVqtlxIgRTJs2jZMnT7JhwwZGjhzp6/SEQOPrAIQIdImJiSxfvpxVq1bhdDpZt24dFy5cICMjg61btxIWFsYXX3zB0qVL+fjjj9m+fTtz584lLS0Nm83GvHnz2Lt3LykpKdhsNqZOncorr7zi67SEcJFCIUQHuP/++9m9ezeDBw9mypQpvPTSS1y4cIGFCxe69qmqqqKyspL09HS+/PJLNm/ezPnz5zGZTNTV1bn2u3b+LyF8TQqFEB2kT58+rvl4nE4naWlppKenu16bTCaioqJYvnw5DoeDmTNncuedd1JUVNRkGmidTueT+IVojdzMFsILbr/9dj766CNMJhMAf/3rX3nggQcA+OKLL3j88ceZNWsWKpWKI0eONHlqSgh/I1cUQnjBpEmTWLJkCT//+c9RqVRERESwadMmVCoVy5cv5/HHHycqKoqwsDDGjRvHxYsXfR2yEK2Sp56EEEK4JV1PQggh3JJCIYQQwi0pFEIIIdySQiGEEMItKRRCCCHckkIhhBDCLSkUQggh3JJCIYQQwq3/H+cCVZmVFDQXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, the situation with 'Year' is similar to 'Price' and 'Mileage'\n",
    "# However, the outliers are on the low end\n",
    "sns.distplot(data_no_mv['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef723dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'll simply remove them\n",
    "q = data_3['Year'].quantile(0.01)\n",
    "data_4 = data_3[data_3['Year']>q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dadedec4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 34,
     "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": 35,
   "id": "b363b1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# When we remove observations, the original indexes are preserved\n",
    "# If we remove observations with indexes 2 and 3, the indexes will go as: 0,1,4,5,6\n",
    "\n",
    "# Finally, once we reset the index, a new column will be created containing the old index (just in case)\n",
    "# We won't be needing it, thus 'drop=True' to completely forget about it\n",
    "data_cleaned = data_4.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "324ef022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Price</th>\n",
       "      <th>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867</td>\n",
       "      <td>3867.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1807</td>\n",
       "      <td>3505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18194.455679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.542539</td>\n",
       "      <td>2.450440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.709853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19085.855165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.633291</td>\n",
       "      <td>0.949366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.103870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>129222.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Brand          Price   Body      Mileage      EngineV  \\\n",
       "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
       "unique           7            NaN      6          NaN          NaN   \n",
       "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
       "freq           848            NaN   1467          NaN          NaN   \n",
       "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
       "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
       "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
       "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
       "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
       "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
       "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
       "\n",
       "       Engine Type Registration         Year  \n",
       "count         3867         3867  3867.000000  \n",
       "unique           4            2          NaN  \n",
       "top         Diesel          yes          NaN  \n",
       "freq          1807         3505          NaN  \n",
       "mean           NaN          NaN  2006.709853  \n",
       "std            NaN          NaN     6.103870  \n",
       "min            NaN          NaN  1988.000000  \n",
       "25%            NaN          NaN  2003.000000  \n",
       "50%            NaN          NaN  2008.000000  \n",
       "75%            NaN          NaN  2012.000000  \n",
       "max            NaN          NaN  2016.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7a7ec9d9",
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0          yes  1991  \n",
       "1          yes  1999  \n",
       "2          yes  2003  \n",
       "3          yes  2007  \n",
       "4          yes  2011  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8307943c",
   "metadata": {},
   "source": [
    "## Checking OLS Assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "040b1b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price and Mileage')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#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",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, sharey=True, figsize = (15,3)) # sharey is '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 Engine')\n",
    "ax3.scatter(data_cleaned['Mileage'], data_cleaned['Price'])\n",
    "ax3.set_title('Price and Mileage')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2066c3f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 41,
     "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": [
    "# From the subplots and the PDF of price, we can easily determine that 'Price' is exponentially distributed\n",
    "# A good transformation in that case is a log transformation\n",
    "sns.distplot(data_cleaned['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99602c99",
   "metadata": {},
   "source": [
    "## Relaxing the assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e833c985",
   "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>3862</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>163</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.350102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>35</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.792556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2014</td>\n",
       "      <td>11.736069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3865</th>\n",
       "      <td>BMW</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.779557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3866</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>van</td>\n",
       "      <td>124</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>9.510445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3867 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
       "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
       "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
       "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
       "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
       "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
       "...             ...       ...        ...      ...      ...         ...   \n",
       "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
       "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
       "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
       "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
       "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
       "\n",
       "     Registration  Year  Log Price  \n",
       "0             yes  1991   8.342840  \n",
       "1             yes  1999   8.974618  \n",
       "2             yes  2003   9.495519  \n",
       "3             yes  2007  10.043249  \n",
       "4             yes  2011   9.814656  \n",
       "...           ...   ...        ...  \n",
       "3862          yes  2008   9.350102  \n",
       "3863          yes  2014   9.792556  \n",
       "3864          yes  2014  11.736069  \n",
       "3865          yes  1999   8.779557  \n",
       "3866          yes  2013   9.510445  \n",
       "\n",
       "[3867 rows x 9 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's transform 'Price' with a log transformation\n",
    "log_price = np.log(data_cleaned['Price'])\n",
    "\n",
    "# Then we add it to our data frame\n",
    "data_cleaned['Log Price'] = log_price\n",
    "data_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b000ef9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, sharey=True, figsize = (15,3)) # sharey is '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 Engine')\n",
    "ax3.scatter(data_cleaned['Mileage'], data_cleaned['Log Price'])\n",
    "ax3.set_title('Log Price and Mileage')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# The relationships show a clear linear relationship\n",
    "# This is some good linear regression material\n",
    "\n",
    "# Alternatively we could have transformed each of the independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c6ee32c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets drop original Price column since its no longer needed \n",
    "\n",
    "data_cleaned = data_cleaned.drop(['Price'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432f0a6a",
   "metadata": {},
   "source": [
    "## Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e5b4f733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
       "       'Registration', 'Year', 'Log Price'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's quickly see the columns of our data frame\n",
    "\n",
    "data_cleaned.columns.values \n",
    "\n",
    "# It is logical that year and mileage is correlated to each other, so we have some degree of Multicollinearity here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "08177a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn does not have a built-in way to check for multicollinearity\n",
    "\n",
    "# Here's the relevant module\n",
    "# full documentation: http://www.statsmodels.org/dev/_modules/statsmodels/stats/outliers_influence.html#variance_inflation_factor\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# To make this as easy as possible to use, we declare a variable where we put\n",
    "# all features where we want to check for multicollinearity\n",
    "# since our categorical data is not yet preprocessed, we will only take the numerical ones\n",
    "variables = data_cleaned[['Mileage','Year','EngineV']]\n",
    "\n",
    "# we create a new data frame which will include all the VIFs\n",
    "# note that each variable has its own variance inflation factor as this measure is variable specific (not model specific)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "# here we make use of the variance_inflation_factor, which will basically output the respective VIFs \n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "# Finally, I like to include names so it is easier to explore the result\n",
    "vif[\"Features\"] = variables.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff4427f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.791584</td>\n",
       "      <td>Mileage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.354854</td>\n",
       "      <td>Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.662068</td>\n",
       "      <td>EngineV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF Features\n",
       "0   3.791584  Mileage\n",
       "1  10.354854     Year\n",
       "2   7.662068  EngineV"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's explore the result\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f312ae72",
   "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": "code",
   "execution_count": 58,
   "id": "01b6d0e7",
   "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>Body</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Engine Type</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Log Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>sedan</td>\n",
       "      <td>277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.342840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>van</td>\n",
       "      <td>427</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>yes</td>\n",
       "      <td>8.974618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>sedan</td>\n",
       "      <td>358</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gas</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.495519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>crossover</td>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>10.043249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>crossover</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>yes</td>\n",
       "      <td>9.814656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand       Body  Mileage  EngineV Engine Type Registration  \\\n",
       "0            BMW      sedan      277      2.0      Petrol          yes   \n",
       "1  Mercedes-Benz        van      427      2.9      Diesel          yes   \n",
       "2  Mercedes-Benz      sedan      358      5.0         Gas          yes   \n",
       "3           Audi  crossover      240      4.2      Petrol          yes   \n",
       "4         Toyota  crossover      120      2.0      Petrol          yes   \n",
       "\n",
       "   Log Price  \n",
       "0   8.342840  \n",
       "1   8.974618  \n",
       "2   9.495519  \n",
       "3  10.043249  \n",
       "4   9.814656  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_multicollinearity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e763bd28",
   "metadata": {},
   "source": [
    "## Create Dummy Variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8436ea8e",
   "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",
    "\n",
    "data_with_dum = pd.get_dummies(data_no_multicollinearity, drop_first = False)\n",
    "data_with_dummies = pd.get_dummies(data_no_multicollinearity, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b1dc1368",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mileage</th>\n",
       "      <th>EngineV</th>\n",
       "      <th>Log Price</th>\n",
       "      <th>Brand_BMW</th>\n",
       "      <th>Brand_Mercedes-Benz</th>\n",
       "      <th>Brand_Mitsubishi</th>\n",
       "      <th>Brand_Renault</th>\n",
       "      <th>Brand_Toyota</th>\n",
       "      <th>Brand_Volkswagen</th>\n",
       "      <th>Body_hatch</th>\n",
       "      <th>Body_other</th>\n",
       "      <th>Body_sedan</th>\n",
       "      <th>Body_vagon</th>\n",
       "      <th>Body_van</th>\n",
       "      <th>Engine Type_Gas</th>\n",
       "      <th>Engine Type_Other</th>\n",
       "      <th>Engine Type_Petrol</th>\n",
       "      <th>Registration_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>240</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.043249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.814656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mileage  EngineV  Log Price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
       "0      277      2.0   8.342840          1                    0   \n",
       "1      427      2.9   8.974618          0                    1   \n",
       "2      358      5.0   9.495519          0                    1   \n",
       "3      240      4.2  10.043249          0                    0   \n",
       "4      120      2.0   9.814656          0                    0   \n",
       "\n",
       "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
       "0                 0              0             0                 0   \n",
       "1                 0              0             0                 0   \n",
       "2                 0              0             0                 0   \n",
       "3                 0              0             0                 0   \n",
       "4                 0              0             1                 0   \n",
       "\n",
       "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
       "0           0           0           1           0         0                0   \n",
       "1           0           0           0           0         1                0   \n",
       "2           0           0           1           0         0                1   \n",
       "3           0           0           0           0         0                0   \n",
       "4           0           0           0           0         0                0   \n",
       "\n",
       "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
       "0                  0                   1                 1  \n",
       "1                  0                   0                 1  \n",
       "2                  0                   0                 1  \n",
       "3                  0                   1                 1  \n",
       "4                  0                   1                 1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ebc28b7",
   "metadata": {},
   "source": [
    "## Rearrange a bit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5bc7d11b",
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b250de97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make the code a bit more parametrized, let's declare a new variable that will contain the preferred order\n",
    "# If you want a different order, just specify it here\n",
    "# Conventionally, the most intuitive order is: dependent variable, indepedendent numerical variables, dummies\n",
    "cols = ['Log Price','Mileage', 'EngineV','Brand_BMW',\n",
    "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
    "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
    "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
    "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a5bc919c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Engine Type_Petrol</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To implement the reordering, we will create a new df, which is equal to the old one but with the new order of features\n",
    "data_preprocessed = data_with_dummies[cols]\n",
    "data_preprocessed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e045c706",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.459662</td>\n",
       "      <td>Mileage</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.841729</td>\n",
       "      <td>EngineV</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.294007</td>\n",
       "      <td>Brand_BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.868649</td>\n",
       "      <td>Brand_Mercedes-Benz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.641712</td>\n",
       "      <td>Brand_Mitsubishi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.086774</td>\n",
       "      <td>Brand_Renault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.162166</td>\n",
       "      <td>Brand_Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.844515</td>\n",
       "      <td>Brand_Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.464260</td>\n",
       "      <td>Body_hatch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.534059</td>\n",
       "      <td>Body_other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.120431</td>\n",
       "      <td>Body_sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.581933</td>\n",
       "      <td>Body_vagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.470096</td>\n",
       "      <td>Body_van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.689146</td>\n",
       "      <td>Engine Type_Gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.082037</td>\n",
       "      <td>Engine Type_Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.498172</td>\n",
       "      <td>Engine Type_Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.641446</td>\n",
       "      <td>Registration_yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF             features\n",
       "0   4.459662              Mileage\n",
       "1   7.841729              EngineV\n",
       "2   2.294007            Brand_BMW\n",
       "3   2.868649  Brand_Mercedes-Benz\n",
       "4   1.641712     Brand_Mitsubishi\n",
       "5   2.086774        Brand_Renault\n",
       "6   2.162166         Brand_Toyota\n",
       "7   2.844515     Brand_Volkswagen\n",
       "8   1.464260           Body_hatch\n",
       "9   1.534059           Body_other\n",
       "10  3.120431           Body_sedan\n",
       "11  1.581933           Body_vagon\n",
       "12  2.470096             Body_van\n",
       "13  1.689146      Engine Type_Gas\n",
       "14  1.082037    Engine Type_Other\n",
       "15  2.498172   Engine Type_Petrol\n",
       "16  9.641446     Registration_yes"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check for multicolliniearity after converting dummies to numerical \n",
    "\n",
    "\n",
    "variables = data_preprocessed.drop(['Log Price'],axis=1)\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif[\"features\"] = variables.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6602e298",
   "metadata": {},
   "source": [
    "In the case of registration, the main issue is that most values are 'yes' so all types of problems come from there. One way this imbalance manifests is in multicollinearity.Well, if 'registration' is always 'yes', then if we predict 'Log Price' we are predicting registration, too (it is going to be 'yes'). That is why, whenever a single category is so predominant, we may just drop the variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9ddbad",
   "metadata": {},
   "source": [
    "# Linear Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155ca51e",
   "metadata": {},
   "source": [
    "### Declare the inputs and targers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "87a70445",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = data_preprocessed['Log Price']\n",
    "inputs = data_preprocessed.drop(['Log Price'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4f7b76",
   "metadata": {},
   "source": [
    "### Scale the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "abd78040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the scaling module\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a scaler object\n",
    "scaler = StandardScaler()\n",
    "# Fit the inputs (calculate the mean and standard deviation feature-wise)\n",
    "scaler.fit(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f1627ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the features and store them in a new variable (the actual scaling procedure)\n",
    "inputs_scaled = scaler.transform(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5040f73",
   "metadata": {},
   "source": [
    "### Train and Test Split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "204c9582",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6a2a0daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled, target, test_size = 0.2, random_state =365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6758b985",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3093, 17) (774, 17)\n",
      "(3093,) (774,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, x_test.shape)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ec5de9",
   "metadata": {},
   "source": [
    "### Create the regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "adc04b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression() # Creating LinRegression Model\n",
    "\n",
    "reg.fit(x_train, y_train) # training the scaled input with the train target "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "efca3069",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's check the outputs of the regression\n",
    "# I'll store them in y_hat as this is the 'theoretical' name of the predictions\n",
    "y_hat = reg.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b33aa818",
   "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",
    "plt.xlabel('Targets (y_train)',size=18)\n",
    "plt.ylabel('Predictions (y_hat)',size=18)\n",
    "# Sometimes the plot will have different scales of the x-axis and the y-axis\n",
    "# This is an issue as we won't be able to interpret the '45-degree line'\n",
    "# We want the x-axis and the y-axis to be the same\n",
    "plt.xlim(6,13)\n",
    "plt.ylim(6,13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cfc0058d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/iscodav/opt/anaconda3/envs/py38/lib/python3.8/site-packages/seaborn/distributions.py:2619: 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": 93,
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "32ca44e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.744996578792662"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the R-squared of the model\n",
    "reg.score(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885b1995",
   "metadata": {},
   "source": [
    "### Finding the weights and bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a53c017a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.415239458021299"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the bias (intercept) of the regression\n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "19a9ae70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
       "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
       "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
       "       -0.14690868,  0.32047333])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the weights (coefficients) of the regression\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "caf6ba44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mileage</td>\n",
       "      <td>-0.448713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EngineV</td>\n",
       "      <td>0.209035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brand_BMW</td>\n",
       "      <td>0.014250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brand_Mercedes-Benz</td>\n",
       "      <td>0.012882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brand_Mitsubishi</td>\n",
       "      <td>-0.140552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brand_Renault</td>\n",
       "      <td>-0.179909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brand_Toyota</td>\n",
       "      <td>-0.060550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brand_Volkswagen</td>\n",
       "      <td>-0.089924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Body_hatch</td>\n",
       "      <td>-0.145469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Body_other</td>\n",
       "      <td>-0.101444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Body_sedan</td>\n",
       "      <td>-0.200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Body_vagon</td>\n",
       "      <td>-0.129887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Body_van</td>\n",
       "      <td>-0.168597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Engine Type_Gas</td>\n",
       "      <td>-0.121490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Engine Type_Other</td>\n",
       "      <td>-0.033368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Engine Type_Petrol</td>\n",
       "      <td>-0.146909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Registration_yes</td>\n",
       "      <td>0.320473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Features   Weights\n",
       "0               Mileage -0.448713\n",
       "1               EngineV  0.209035\n",
       "2             Brand_BMW  0.014250\n",
       "3   Brand_Mercedes-Benz  0.012882\n",
       "4      Brand_Mitsubishi -0.140552\n",
       "5         Brand_Renault -0.179909\n",
       "6          Brand_Toyota -0.060550\n",
       "7      Brand_Volkswagen -0.089924\n",
       "8            Body_hatch -0.145469\n",
       "9            Body_other -0.101444\n",
       "10           Body_sedan -0.200630\n",
       "11           Body_vagon -0.129887\n",
       "12             Body_van -0.168597\n",
       "13      Engine Type_Gas -0.121490\n",
       "14    Engine Type_Other -0.033368\n",
       "15   Engine Type_Petrol -0.146909\n",
       "16     Registration_yes  0.320473"
      ]
     },
     "execution_count": 97,
     "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": 102,
   "id": "f82cce5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
       "       'Mitsubishi'], dtype=object)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the different categories in the 'Brand' variable\n",
    "data_cleaned['Brand'].unique()\n",
    "\n",
    "# In this way we can see which 'Brand' is actually the benchmark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36f4474",
   "metadata": {},
   "source": [
    "## Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7a48d488",
   "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",
    "\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": 104,
   "id": "87edae4b",
   "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": 107,
   "id": "bd048e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf = pd.DataFrame(np.exp(y_hat_test), columns = ['Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "74ad676c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.501696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.255242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.285218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.963017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.490075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.726363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.071179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.446953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.751303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.163043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Predictions\n",
       "0    10685.501696\n",
       "1     3499.255242\n",
       "2     7553.285218\n",
       "3     7463.963017\n",
       "4    11353.490075\n",
       "..            ...\n",
       "769  29651.726363\n",
       "770  10732.071179\n",
       "771  13922.446953\n",
       "772  27487.751303\n",
       "773  13491.163043\n",
       "\n",
       "[774 rows x 1 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5227e37b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pf['Target'] = np.exp(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f173f3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Targer</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.501696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.255242</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.285218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.963017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.490075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.726363</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>29500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.071179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9600.0</td>\n",
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       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.446953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18300.0</td>\n",
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       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.751303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68500.0</td>\n",
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       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.163043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Predictions  Targer   Target\n",
       "0    10685.501696     NaN   2300.0\n",
       "1     3499.255242  7900.0   2800.0\n",
       "2     7553.285218     NaN   2500.0\n",
       "3     7463.963017     NaN   6400.0\n",
       "4    11353.490075     NaN   9150.0\n",
       "..            ...     ...      ...\n",
       "769  29651.726363  6950.0  29500.0\n",
       "770  10732.071179     NaN   9600.0\n",
       "771  13922.446953     NaN  18300.0\n",
       "772  27487.751303     NaN  68500.0\n",
       "773  13491.163043     NaN  10800.0\n",
       "\n",
       "[774 rows x 3 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf\n",
    "\n",
    "# Note that we have a lot of missing values\n",
    "# There is no reason to have ANY missing values, though\n",
    "# This suggests that something is wrong with the data frame / indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "dd10f839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7.740664\n",
       "1       7.937375\n",
       "2       7.824046\n",
       "3       8.764053\n",
       "4       9.121509\n",
       "         ...    \n",
       "769    10.292146\n",
       "770     9.169518\n",
       "771     9.814656\n",
       "772    11.134589\n",
       "773     9.287301\n",
       "Name: Log Price, Length: 774, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The old indexes are preserved (recall earlier in that code we made a note on that)\n",
    "\n",
    "y_test = y_test.reset_index(drop=True)\n",
    "\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1c8f7e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>774 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "      Predictions   Target\n",
       "0    10685.501696   2300.0\n",
       "1     3499.255242   2800.0\n",
       "2     7553.285218   2500.0\n",
       "3     7463.963017   6400.0\n",
       "4    11353.490075   9150.0\n",
       "..            ...      ...\n",
       "769  29651.726363  29500.0\n",
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       "771  13922.446953  18300.0\n",
       "772  27487.751303  68500.0\n",
       "773  13491.163043  10800.0\n",
       "\n",
       "[774 rows x 2 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's overwrite the 'Target' column with the appropriate values\n",
    "# Again, we need the exponential of the test log price\n",
    "df_pf['Target'] = np.exp(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2687c8a8",
   "metadata": {},
   "outputs": [
    {
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       "<p>774 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      Predictions   Target      Residual  Difference%\n",
       "0    10685.501696   2300.0  -8385.501696   364.587030\n",
       "1     3499.255242   2800.0   -699.255242    24.973402\n",
       "2     7553.285218   2500.0  -5053.285218   202.131409\n",
       "3     7463.963017   6400.0  -1063.963017    16.624422\n",
       "4    11353.490075   9150.0  -2203.490075    24.081859\n",
       "..            ...      ...           ...          ...\n",
       "769  29651.726363  29500.0   -151.726363     0.514327\n",
       "770  10732.071179   9600.0  -1132.071179    11.792408\n",
       "771  13922.446953  18300.0   4377.553047    23.921055\n",
       "772  27487.751303  68500.0  41012.248697    59.871896\n",
       "773  13491.163043  10800.0  -2691.163043    24.918176\n",
       "\n",
       "[774 rows x 4 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Additionally, we can calculate the difference between the targets and the predictions\n",
    "\n",
    "df_pf['Residual'] = df_pf['Target'] - df_pf['Predictions']\n",
    "df_pf\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": 127,
   "id": "18d0bd1b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>771</th>\n",
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       "<p>774 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      Predictions   Target      Residual  Difference%\n",
       "0    10685.501696   2300.0  -8385.501696   364.587030\n",
       "1     3499.255242   2800.0   -699.255242    24.973402\n",
       "2     7553.285218   2500.0  -5053.285218   202.131409\n",
       "3     7463.963017   6400.0  -1063.963017    16.624422\n",
       "4    11353.490075   9150.0  -2203.490075    24.081859\n",
       "..            ...      ...           ...          ...\n",
       "769  29651.726363  29500.0   -151.726363     0.514327\n",
       "770  10732.071179   9600.0  -1132.071179    11.792408\n",
       "771  13922.446953  18300.0   4377.553047    23.921055\n",
       "772  27487.751303  68500.0  41012.248697    59.871896\n",
       "773  13491.163043  10800.0  -2691.163043    24.918176\n",
       "\n",
       "[774 rows x 4 columns]"
      ]
     },
     "execution_count": 127,
     "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": 129,
   "id": "8c34fedf",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1320.562768</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>-29456.498331</td>\n",
       "      <td>0.062794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7413.644234</td>\n",
       "      <td>6900.000000</td>\n",
       "      <td>-2044.191251</td>\n",
       "      <td>12.108022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11568.168859</td>\n",
       "      <td>11600.000000</td>\n",
       "      <td>142.518577</td>\n",
       "      <td>23.467728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20162.408805</td>\n",
       "      <td>20500.000000</td>\n",
       "      <td>3147.343497</td>\n",
       "      <td>39.563570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77403.055224</td>\n",
       "      <td>126000.000000</td>\n",
       "      <td>85106.162329</td>\n",
       "      <td>512.688080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predictions         Target      Residual  Difference%\n",
       "count    774.000000     774.000000    774.000000   774.000000\n",
       "mean   15946.760167   18165.817106   2219.056939    36.256693\n",
       "std    13133.197604   19967.858908  10871.218143    55.066507\n",
       "min     1320.562768    1200.000000 -29456.498331     0.062794\n",
       "25%     7413.644234    6900.000000  -2044.191251    12.108022\n",
       "50%    11568.168859   11600.000000    142.518577    23.467728\n",
       "75%    20162.408805   20500.000000   3147.343497    39.563570\n",
       "max    77403.055224  126000.000000  85106.162329   512.688080"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the descriptives here gives us additional insights\n",
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4ad96016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>30480.85</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>19.15</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>16960.31</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>38.69</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12469.21</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>30.79</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>25614.14</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>-114.14</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>42703.68</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>-203.68</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>3084.69</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>15.31</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>29651.73</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>-151.73</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>9749.53</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>50.47</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>23118.07</td>\n",
       "      <td>22999.00</td>\n",
       "      <td>-119.07</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>8734.58</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>65.42</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>3473.79</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>26.21</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>21174.10</td>\n",
       "      <td>21335.00</td>\n",
       "      <td>160.90</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>8967.74</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-67.74</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>17858.02</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>141.98</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>17654.84</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>145.16</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>11391.95</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>108.05</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>28625.56</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>274.44</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>7724.17</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>75.83</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>6429.03</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>70.97</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>7597.39</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-97.39</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>18555.09</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>244.91</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7396.87</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-96.87</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16178.14</td>\n",
       "      <td>16400.00</td>\n",
       "      <td>221.86</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>12327.10</td>\n",
       "      <td>12499.00</td>\n",
       "      <td>171.90</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>51287.19</td>\n",
       "      <td>52055.25</td>\n",
       "      <td>768.06</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>6009.63</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>90.37</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4973.17</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-73.17</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>27716.14</td>\n",
       "      <td>27300.00</td>\n",
       "      <td>-416.14</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>28498.91</td>\n",
       "      <td>28950.00</td>\n",
       "      <td>451.09</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2953.17</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>46.83</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>26425.14</td>\n",
       "      <td>25999.00</td>\n",
       "      <td>-426.14</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13111.91</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>-211.91</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12858.08</td>\n",
       "      <td>12650.00</td>\n",
       "      <td>-208.08</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>13421.16</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>-221.16</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>7327.18</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-127.18</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>3816.33</td>\n",
       "      <td>3750.00</td>\n",
       "      <td>-66.33</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>9721.50</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>178.50</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>44173.72</td>\n",
       "      <td>44999.00</td>\n",
       "      <td>825.28</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>40753.58</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>-753.58</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>22262.80</td>\n",
       "      <td>22711.65</td>\n",
       "      <td>448.85</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>18231.44</td>\n",
       "      <td>18600.00</td>\n",
       "      <td>368.56</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>9596.94</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>-196.94</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>6067.79</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>132.21</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>35371.16</td>\n",
       "      <td>34600.00</td>\n",
       "      <td>-771.16</td>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>11967.39</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-267.39</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>17908.00</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-408.00</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>8707.13</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-207.13</td>\n",
       "      <td>2.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>29049.27</td>\n",
       "      <td>28350.00</td>\n",
       "      <td>-699.27</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>14513.46</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>386.54</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>20453.89</td>\n",
       "      <td>21000.00</td>\n",
       "      <td>546.11</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>15383.35</td>\n",
       "      <td>14990.00</td>\n",
       "      <td>-393.35</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>24642.50</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-642.50</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>50099.92</td>\n",
       "      <td>51500.00</td>\n",
       "      <td>1400.08</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>16133.86</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>-433.86</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>17489.92</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>510.08</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1894.40</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>55.60</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>30810.25</td>\n",
       "      <td>29900.00</td>\n",
       "      <td>-910.25</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>8721.97</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>277.03</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>18866.50</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>-566.50</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5958.63</td>\n",
       "      <td>6150.00</td>\n",
       "      <td>191.37</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>5811.74</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>188.26</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>9800.43</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-300.43</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>7324.63</td>\n",
       "      <td>7100.00</td>\n",
       "      <td>-224.63</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>12583.52</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>416.48</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>10115.13</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-315.13</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>11156.38</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-356.38</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>9279.28</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>320.72</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>31369.37</td>\n",
       "      <td>32500.00</td>\n",
       "      <td>1130.63</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2315.71</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>84.29</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5175.77</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-175.77</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>21611.83</td>\n",
       "      <td>22400.00</td>\n",
       "      <td>788.17</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7976.26</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-276.26</td>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>24874.86</td>\n",
       "      <td>23999.00</td>\n",
       "      <td>-875.86</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>36516.35</td>\n",
       "      <td>37900.00</td>\n",
       "      <td>1383.65</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>4666.05</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-166.05</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>18672.68</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>-672.68</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>14815.83</td>\n",
       "      <td>15400.00</td>\n",
       "      <td>584.17</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>10581.62</td>\n",
       "      <td>10999.00</td>\n",
       "      <td>417.38</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>12663.54</td>\n",
       "      <td>12200.00</td>\n",
       "      <td>-463.54</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>1825.44</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>74.56</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>12247.90</td>\n",
       "      <td>12750.00</td>\n",
       "      <td>502.10</td>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>5930.73</td>\n",
       "      <td>5700.00</td>\n",
       "      <td>-230.73</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>9782.47</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>417.53</td>\n",
       "      <td>4.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>12452.22</td>\n",
       "      <td>11960.00</td>\n",
       "      <td>-492.22</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>23163.87</td>\n",
       "      <td>24171.42</td>\n",
       "      <td>1007.55</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>3246.94</td>\n",
       "      <td>3390.00</td>\n",
       "      <td>143.06</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>12354.16</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>545.84</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>33499.44</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>1500.56</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>16257.03</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>741.97</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>8415.81</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>384.19</td>\n",
       "      <td>4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>12318.39</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>581.61</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2765.14</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>134.86</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>11420.95</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>578.05</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2725.40</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-125.40</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>23624.69</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>-1124.69</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11377.84</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>621.16</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>12096.18</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-596.18</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>9363.27</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-463.27</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>12311.47</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-611.47</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>14218.94</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>-718.94</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>8431.89</td>\n",
       "      <td>7999.00</td>\n",
       "      <td>-432.89</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37600.15</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>2299.85</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>39882.67</td>\n",
       "      <td>37700.00</td>\n",
       "      <td>-2182.67</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>20588.57</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>1311.43</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>6861.84</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>438.16</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>18982.15</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-1082.15</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>15067.85</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>-867.85</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>12201.29</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>797.71</td>\n",
       "      <td>6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>11147.91</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-647.91</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>20183.30</td>\n",
       "      <td>18999.00</td>\n",
       "      <td>-1184.30</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>17334.11</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>1165.89</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21289.80</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-1289.80</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>6826.78</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>473.22</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>4472.47</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>-272.47</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>53294.61</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>3705.39</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>6658.73</td>\n",
       "      <td>6250.00</td>\n",
       "      <td>-408.73</td>\n",
       "      <td>6.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>19001.29</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>-1201.29</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>4590.49</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-290.49</td>\n",
       "      <td>6.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>43236.92</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>-2736.92</td>\n",
       "      <td>6.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>9076.42</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-576.42</td>\n",
       "      <td>6.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>10159.18</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>740.82</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>16027.02</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-1027.02</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>13502.35</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>997.65</td>\n",
       "      <td>6.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>6413.26</td>\n",
       "      <td>5999.00</td>\n",
       "      <td>-414.26</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>49221.63</td>\n",
       "      <td>52999.00</td>\n",
       "      <td>3777.37</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>21816.43</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>1683.57</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>11969.66</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>930.34</td>\n",
       "      <td>7.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>8263.95</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-563.95</td>\n",
       "      <td>7.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11581.54</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>918.46</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>6977.90</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-477.90</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>6978.91</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-478.91</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>16392.22</td>\n",
       "      <td>17700.00</td>\n",
       "      <td>1307.78</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>15742.41</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>1256.59</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>7869.47</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>630.53</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>21297.65</td>\n",
       "      <td>19800.00</td>\n",
       "      <td>-1497.65</td>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>6893.62</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-493.62</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>15627.03</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>-1127.03</td>\n",
       "      <td>7.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>3411.53</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>288.47</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>11336.71</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>963.29</td>\n",
       "      <td>7.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>8195.34</td>\n",
       "      <td>7600.00</td>\n",
       "      <td>-595.34</td>\n",
       "      <td>7.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>16717.08</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>-1217.08</td>\n",
       "      <td>7.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>7772.57</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-572.57</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>16197.42</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-1197.42</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6102.36</td>\n",
       "      <td>5650.00</td>\n",
       "      <td>-452.36</td>\n",
       "      <td>8.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>4488.43</td>\n",
       "      <td>4150.00</td>\n",
       "      <td>-338.43</td>\n",
       "      <td>8.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>15885.66</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>1414.34</td>\n",
       "      <td>8.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>17983.10</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>1616.90</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>7045.72</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-545.72</td>\n",
       "      <td>8.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>3795.06</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-295.06</td>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>6130.96</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>569.04</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>5947.51</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>552.49</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>7609.28</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-609.28</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>8117.44</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>782.56</td>\n",
       "      <td>8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>11098.18</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-898.18</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>17028.45</td>\n",
       "      <td>18700.00</td>\n",
       "      <td>1671.55</td>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>19847.25</td>\n",
       "      <td>18200.00</td>\n",
       "      <td>-1647.25</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>5454.34</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-454.34</td>\n",
       "      <td>9.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>20662.46</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-1762.46</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>11696.25</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>1203.75</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>10716.84</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-916.84</td>\n",
       "      <td>9.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>13459.92</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>-1159.92</td>\n",
       "      <td>9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>8767.54</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>932.46</td>\n",
       "      <td>9.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>23272.83</td>\n",
       "      <td>25749.75</td>\n",
       "      <td>2476.92</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>5484.97</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-485.97</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>9495.62</td>\n",
       "      <td>8650.00</td>\n",
       "      <td>-845.62</td>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>11858.48</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-1058.48</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>48535.50</td>\n",
       "      <td>-4777.31</td>\n",
       "      <td>9.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>47047.70</td>\n",
       "      <td>52300.00</td>\n",
       "      <td>5252.30</td>\n",
       "      <td>10.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6717.17</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>-617.17</td>\n",
       "      <td>10.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>5391.77</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>608.23</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>17591.09</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>2008.91</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>17591.09</td>\n",
       "      <td>19600.00</td>\n",
       "      <td>2008.91</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>25576.58</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>2923.42</td>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>20367.25</td>\n",
       "      <td>22700.00</td>\n",
       "      <td>2332.75</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>7267.36</td>\n",
       "      <td>8100.00</td>\n",
       "      <td>832.64</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>1792.36</td>\n",
       "      <td>1999.00</td>\n",
       "      <td>206.64</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8248.67</td>\n",
       "      <td>9200.00</td>\n",
       "      <td>951.33</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>5643.45</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>656.55</td>\n",
       "      <td>10.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6839.57</td>\n",
       "      <td>7650.00</td>\n",
       "      <td>810.43</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>41503.90</td>\n",
       "      <td>37500.00</td>\n",
       "      <td>-4003.90</td>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>9310.69</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>-910.69</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>35662.26</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>4337.74</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>16407.29</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>-1607.29</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>21303.57</td>\n",
       "      <td>23900.00</td>\n",
       "      <td>2596.43</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>15872.91</td>\n",
       "      <td>14300.00</td>\n",
       "      <td>-1572.91</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>10332.54</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>-1032.54</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>18575.43</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>2324.57</td>\n",
       "      <td>11.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>8882.65</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>1117.35</td>\n",
       "      <td>11.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>7797.18</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-798.18</td>\n",
       "      <td>11.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10621.84</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>1377.16</td>\n",
       "      <td>11.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>10732.07</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-1132.07</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>2381.01</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>318.99</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>11200.20</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-1201.20</td>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>2373.66</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>326.34</td>\n",
       "      <td>12.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>10696.56</td>\n",
       "      <td>12179.00</td>\n",
       "      <td>1482.44</td>\n",
       "      <td>12.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>3590.97</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-390.97</td>\n",
       "      <td>12.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>53063.22</td>\n",
       "      <td>60500.00</td>\n",
       "      <td>7436.78</td>\n",
       "      <td>12.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>7300.05</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-800.05</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>6392.70</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>907.30</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6746.43</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-746.43</td>\n",
       "      <td>12.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>9192.06</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>1307.94</td>\n",
       "      <td>12.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>11034.66</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-1234.66</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>16939.86</td>\n",
       "      <td>19400.00</td>\n",
       "      <td>2460.14</td>\n",
       "      <td>12.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2479.43</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-279.43</td>\n",
       "      <td>12.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>9593.52</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>1406.48</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>23967.81</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>3532.19</td>\n",
       "      <td>12.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>7491.01</td>\n",
       "      <td>8600.00</td>\n",
       "      <td>1108.99</td>\n",
       "      <td>12.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>8242.62</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-942.62</td>\n",
       "      <td>12.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>9489.56</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>1410.44</td>\n",
       "      <td>12.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>44312.98</td>\n",
       "      <td>50900.00</td>\n",
       "      <td>6587.02</td>\n",
       "      <td>12.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>9829.53</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-1129.53</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>9269.68</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>-1069.68</td>\n",
       "      <td>13.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>4524.77</td>\n",
       "      <td>3999.00</td>\n",
       "      <td>-525.77</td>\n",
       "      <td>13.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23650.15</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-2750.15</td>\n",
       "      <td>13.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>8850.21</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>1349.79</td>\n",
       "      <td>13.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>4770.52</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>729.48</td>\n",
       "      <td>13.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>29479.97</td>\n",
       "      <td>34000.00</td>\n",
       "      <td>4520.03</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>8316.92</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>1283.08</td>\n",
       "      <td>13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>24974.57</td>\n",
       "      <td>28900.00</td>\n",
       "      <td>3925.43</td>\n",
       "      <td>13.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>14943.75</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>2356.25</td>\n",
       "      <td>13.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>28498.91</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>4501.09</td>\n",
       "      <td>13.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>14591.35</td>\n",
       "      <td>16900.00</td>\n",
       "      <td>2308.65</td>\n",
       "      <td>13.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>10358.58</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>1641.42</td>\n",
       "      <td>13.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>13074.74</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-1574.74</td>\n",
       "      <td>13.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9097.13</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-1097.13</td>\n",
       "      <td>13.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>15010.51</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>-1810.51</td>\n",
       "      <td>13.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>12593.00</td>\n",
       "      <td>14600.00</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>20246.04</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>3253.96</td>\n",
       "      <td>13.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>8768.29</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-1068.29</td>\n",
       "      <td>13.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>39744.80</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>-4844.80</td>\n",
       "      <td>13.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>9909.62</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-1209.62</td>\n",
       "      <td>13.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>44680.77</td>\n",
       "      <td>51900.00</td>\n",
       "      <td>7219.23</td>\n",
       "      <td>13.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>21942.05</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>3557.95</td>\n",
       "      <td>13.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>12900.31</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>2099.69</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>18128.32</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>-2228.32</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>4296.77</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>703.23</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>6006.27</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>992.73</td>\n",
       "      <td>14.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>1972.28</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>327.72</td>\n",
       "      <td>14.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>8236.36</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-1036.36</td>\n",
       "      <td>14.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>10612.51</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>1787.49</td>\n",
       "      <td>14.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>14197.26</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>-1797.26</td>\n",
       "      <td>14.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>47013.28</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>7986.72</td>\n",
       "      <td>14.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>12028.91</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-1528.91</td>\n",
       "      <td>14.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3666.31</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-466.31</td>\n",
       "      <td>14.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2860.97</td>\n",
       "      <td>3350.00</td>\n",
       "      <td>489.03</td>\n",
       "      <td>14.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>7256.86</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>1243.14</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10499.60</td>\n",
       "      <td>12300.00</td>\n",
       "      <td>1800.40</td>\n",
       "      <td>14.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>12383.35</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-1583.35</td>\n",
       "      <td>14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>11937.31</td>\n",
       "      <td>13995.00</td>\n",
       "      <td>2057.69</td>\n",
       "      <td>14.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2810.71</td>\n",
       "      <td>2450.00</td>\n",
       "      <td>-360.71</td>\n",
       "      <td>14.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>7664.32</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>1335.68</td>\n",
       "      <td>14.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>8041.62</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-1042.62</td>\n",
       "      <td>14.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>13334.19</td>\n",
       "      <td>11600.00</td>\n",
       "      <td>-1734.19</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>13680.48</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>-1780.48</td>\n",
       "      <td>14.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>13500.69</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>2399.31</td>\n",
       "      <td>15.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>11638.89</td>\n",
       "      <td>13708.50</td>\n",
       "      <td>2069.61</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>15700.53</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>2799.47</td>\n",
       "      <td>15.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>11425.01</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>2074.99</td>\n",
       "      <td>15.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>63000.00</td>\n",
       "      <td>9687.19</td>\n",
       "      <td>15.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>6599.62</td>\n",
       "      <td>7800.00</td>\n",
       "      <td>1200.38</td>\n",
       "      <td>15.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>61639.73</td>\n",
       "      <td>72900.00</td>\n",
       "      <td>11260.27</td>\n",
       "      <td>15.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>7098.53</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>1301.47</td>\n",
       "      <td>15.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>10889.39</td>\n",
       "      <td>12900.00</td>\n",
       "      <td>2010.61</td>\n",
       "      <td>15.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2613.97</td>\n",
       "      <td>3099.00</td>\n",
       "      <td>485.03</td>\n",
       "      <td>15.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23721.58</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-3221.58</td>\n",
       "      <td>15.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>10133.68</td>\n",
       "      <td>8750.00</td>\n",
       "      <td>-1383.68</td>\n",
       "      <td>15.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>22261.50</td>\n",
       "      <td>19200.00</td>\n",
       "      <td>-3061.50</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>10448.50</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-1449.50</td>\n",
       "      <td>16.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>24271.90</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>-3371.90</td>\n",
       "      <td>16.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>14926.69</td>\n",
       "      <td>17800.00</td>\n",
       "      <td>2873.31</td>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>11619.16</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-1619.16</td>\n",
       "      <td>16.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>46465.02</td>\n",
       "      <td>39900.00</td>\n",
       "      <td>-6565.02</td>\n",
       "      <td>16.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>50954.68</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>10045.32</td>\n",
       "      <td>16.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>6848.88</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>1351.12</td>\n",
       "      <td>16.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>16547.03</td>\n",
       "      <td>14200.00</td>\n",
       "      <td>-2347.03</td>\n",
       "      <td>16.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>4590.16</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>909.84</td>\n",
       "      <td>16.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>13348.91</td>\n",
       "      <td>15999.00</td>\n",
       "      <td>2650.09</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>11191.05</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-1591.05</td>\n",
       "      <td>16.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2126.49</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>423.51</td>\n",
       "      <td>16.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.96</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-1063.96</td>\n",
       "      <td>16.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2749.08</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>550.92</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>18282.75</td>\n",
       "      <td>21950.00</td>\n",
       "      <td>3667.25</td>\n",
       "      <td>16.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>19071.43</td>\n",
       "      <td>22900.00</td>\n",
       "      <td>3828.57</td>\n",
       "      <td>16.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>32886.86</td>\n",
       "      <td>39500.00</td>\n",
       "      <td>6613.14</td>\n",
       "      <td>16.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>25752.19</td>\n",
       "      <td>30990.00</td>\n",
       "      <td>5237.81</td>\n",
       "      <td>16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>7719.69</td>\n",
       "      <td>9300.00</td>\n",
       "      <td>1580.31</td>\n",
       "      <td>16.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>25986.31</td>\n",
       "      <td>31310.00</td>\n",
       "      <td>5323.69</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>8250.47</td>\n",
       "      <td>9950.00</td>\n",
       "      <td>1699.53</td>\n",
       "      <td>17.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>28937.50</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>5962.50</td>\n",
       "      <td>17.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>8392.42</td>\n",
       "      <td>7150.00</td>\n",
       "      <td>-1242.42</td>\n",
       "      <td>17.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>8033.61</td>\n",
       "      <td>9750.00</td>\n",
       "      <td>1716.39</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>8156.11</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>1743.89</td>\n",
       "      <td>17.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>2470.61</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>529.39</td>\n",
       "      <td>17.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>19659.97</td>\n",
       "      <td>16700.00</td>\n",
       "      <td>-2959.97</td>\n",
       "      <td>17.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>17540.72</td>\n",
       "      <td>14899.00</td>\n",
       "      <td>-2641.72</td>\n",
       "      <td>17.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>7814.16</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>1685.84</td>\n",
       "      <td>17.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>21097.66</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>-3197.66</td>\n",
       "      <td>17.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>4266.27</td>\n",
       "      <td>5200.00</td>\n",
       "      <td>933.73</td>\n",
       "      <td>17.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>9380.46</td>\n",
       "      <td>7950.00</td>\n",
       "      <td>-1430.46</td>\n",
       "      <td>17.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>33614.62</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>7385.38</td>\n",
       "      <td>18.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33614.62</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>7385.38</td>\n",
       "      <td>18.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>9089.35</td>\n",
       "      <td>7700.00</td>\n",
       "      <td>-1389.35</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>27740.81</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-4240.81</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3542.72</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-542.72</td>\n",
       "      <td>18.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>25037.87</td>\n",
       "      <td>21200.00</td>\n",
       "      <td>-3837.87</td>\n",
       "      <td>18.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35991.51</td>\n",
       "      <td>43999.00</td>\n",
       "      <td>8007.49</td>\n",
       "      <td>18.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>13733.33</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>3066.67</td>\n",
       "      <td>18.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>11431.35</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>2567.65</td>\n",
       "      <td>18.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>6082.85</td>\n",
       "      <td>7450.00</td>\n",
       "      <td>1367.15</td>\n",
       "      <td>18.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>12674.57</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-1974.57</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>25975.12</td>\n",
       "      <td>21900.00</td>\n",
       "      <td>-4075.12</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>3026.25</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>-476.25</td>\n",
       "      <td>18.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>33172.20</td>\n",
       "      <td>40800.00</td>\n",
       "      <td>7627.80</td>\n",
       "      <td>18.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>13159.16</td>\n",
       "      <td>16200.00</td>\n",
       "      <td>3040.84</td>\n",
       "      <td>18.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>20216.67</td>\n",
       "      <td>24900.00</td>\n",
       "      <td>4683.33</td>\n",
       "      <td>18.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>10220.74</td>\n",
       "      <td>8600.00</td>\n",
       "      <td>-1620.74</td>\n",
       "      <td>18.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>29127.72</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-4627.72</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>5839.08</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>1360.92</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>53063.22</td>\n",
       "      <td>65500.00</td>\n",
       "      <td>12436.78</td>\n",
       "      <td>18.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>28256.79</td>\n",
       "      <td>34900.00</td>\n",
       "      <td>6643.21</td>\n",
       "      <td>19.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>4524.77</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-724.77</td>\n",
       "      <td>19.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2543.57</td>\n",
       "      <td>3149.25</td>\n",
       "      <td>605.68</td>\n",
       "      <td>19.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>60493.51</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>14506.49</td>\n",
       "      <td>19.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>8962.29</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-1462.29</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>14832.32</td>\n",
       "      <td>12400.00</td>\n",
       "      <td>-2432.32</td>\n",
       "      <td>19.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>19676.51</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>4823.49</td>\n",
       "      <td>19.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20349.62</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>-3350.62</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>1926.60</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>473.40</td>\n",
       "      <td>19.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>8381.60</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-1382.60</td>\n",
       "      <td>19.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>38334.67</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>-6334.67</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>20972.54</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>-3472.54</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6732.97</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>1667.03</td>\n",
       "      <td>19.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>10419.20</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>2580.80</td>\n",
       "      <td>19.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>24572.28</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>-4072.28</td>\n",
       "      <td>19.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>10014.71</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>2485.29</td>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>4209.17</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>-709.17</td>\n",
       "      <td>20.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>34678.95</td>\n",
       "      <td>43500.00</td>\n",
       "      <td>8821.05</td>\n",
       "      <td>20.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>18893.76</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>-3193.76</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>8121.22</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>2078.78</td>\n",
       "      <td>20.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>11443.85</td>\n",
       "      <td>9499.00</td>\n",
       "      <td>-1944.85</td>\n",
       "      <td>20.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>33392.40</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>8607.60</td>\n",
       "      <td>20.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6746.48</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>1753.52</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>17251.60</td>\n",
       "      <td>14300.00</td>\n",
       "      <td>-2951.60</td>\n",
       "      <td>20.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6981.34</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>1818.66</td>\n",
       "      <td>20.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2379.58</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>620.42</td>\n",
       "      <td>20.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>10932.17</td>\n",
       "      <td>13800.00</td>\n",
       "      <td>2867.83</td>\n",
       "      <td>20.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>2779.04</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-479.04</td>\n",
       "      <td>20.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>11002.01</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>2897.99</td>\n",
       "      <td>20.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>5144.02</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>1355.98</td>\n",
       "      <td>20.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>7110.32</td>\n",
       "      <td>8990.00</td>\n",
       "      <td>1879.68</td>\n",
       "      <td>20.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>67431.00</td>\n",
       "      <td>14118.19</td>\n",
       "      <td>20.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>7745.02</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-1345.02</td>\n",
       "      <td>21.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>63921.33</td>\n",
       "      <td>80999.00</td>\n",
       "      <td>17077.67</td>\n",
       "      <td>21.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>3940.49</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>1058.51</td>\n",
       "      <td>21.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>23630.85</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-4130.85</td>\n",
       "      <td>21.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>14792.29</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>4007.71</td>\n",
       "      <td>21.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11770.64</td>\n",
       "      <td>9700.00</td>\n",
       "      <td>-2070.64</td>\n",
       "      <td>21.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>9424.34</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>2575.66</td>\n",
       "      <td>21.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>8139.48</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-1439.48</td>\n",
       "      <td>21.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>22367.52</td>\n",
       "      <td>28500.00</td>\n",
       "      <td>6132.48</td>\n",
       "      <td>21.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13004.34</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-2304.34</td>\n",
       "      <td>21.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8512.03</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>-1512.03</td>\n",
       "      <td>21.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>32121.20</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>8878.80</td>\n",
       "      <td>21.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>12523.73</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>3476.27</td>\n",
       "      <td>21.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>19845.36</td>\n",
       "      <td>16300.00</td>\n",
       "      <td>-3545.36</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13292.71</td>\n",
       "      <td>16999.00</td>\n",
       "      <td>3706.29</td>\n",
       "      <td>21.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>8467.17</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>-1517.17</td>\n",
       "      <td>21.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11876.82</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>3323.18</td>\n",
       "      <td>21.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>22547.55</td>\n",
       "      <td>18500.00</td>\n",
       "      <td>-4047.55</td>\n",
       "      <td>21.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>50728.89</td>\n",
       "      <td>65000.00</td>\n",
       "      <td>14271.11</td>\n",
       "      <td>21.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>2625.10</td>\n",
       "      <td>2150.00</td>\n",
       "      <td>-475.10</td>\n",
       "      <td>22.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>5863.41</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-1063.41</td>\n",
       "      <td>22.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>9175.06</td>\n",
       "      <td>7499.00</td>\n",
       "      <td>-1676.06</td>\n",
       "      <td>22.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>8453.28</td>\n",
       "      <td>10900.00</td>\n",
       "      <td>2446.72</td>\n",
       "      <td>22.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>5350.91</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>1549.09</td>\n",
       "      <td>22.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>6819.44</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>1980.56</td>\n",
       "      <td>22.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>10303.21</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>2996.79</td>\n",
       "      <td>22.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>9139.04</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>2660.96</td>\n",
       "      <td>22.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>18621.12</td>\n",
       "      <td>24100.00</td>\n",
       "      <td>5478.88</td>\n",
       "      <td>22.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>15954.72</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>-2955.72</td>\n",
       "      <td>22.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>12270.01</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>3629.99</td>\n",
       "      <td>22.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>11490.44</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>3409.56</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>14136.01</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-2636.01</td>\n",
       "      <td>22.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>11095.57</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-2096.57</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>10350.25</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>3149.75</td>\n",
       "      <td>23.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>20680.40</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>6319.60</td>\n",
       "      <td>23.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>34446.96</td>\n",
       "      <td>27900.00</td>\n",
       "      <td>-6546.96</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4209.17</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>1290.83</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3086.38</td>\n",
       "      <td>2499.00</td>\n",
       "      <td>-587.38</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>16427.12</td>\n",
       "      <td>13300.00</td>\n",
       "      <td>-3127.12</td>\n",
       "      <td>23.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>53312.81</td>\n",
       "      <td>43163.25</td>\n",
       "      <td>-10149.56</td>\n",
       "      <td>23.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>3747.21</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>1152.79</td>\n",
       "      <td>23.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>14410.15</td>\n",
       "      <td>18932.55</td>\n",
       "      <td>4522.40</td>\n",
       "      <td>23.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>13922.45</td>\n",
       "      <td>18300.00</td>\n",
       "      <td>4377.55</td>\n",
       "      <td>23.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>9232.79</td>\n",
       "      <td>7450.00</td>\n",
       "      <td>-1782.79</td>\n",
       "      <td>23.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>18165.16</td>\n",
       "      <td>23900.00</td>\n",
       "      <td>5734.84</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353.49</td>\n",
       "      <td>9150.00</td>\n",
       "      <td>-2203.49</td>\n",
       "      <td>24.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>6205.48</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-1205.48</td>\n",
       "      <td>24.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>11004.05</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>3495.95</td>\n",
       "      <td>24.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>10505.98</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>3394.02</td>\n",
       "      <td>24.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>19920.63</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>-3920.63</td>\n",
       "      <td>24.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>3621.08</td>\n",
       "      <td>4799.00</td>\n",
       "      <td>1177.92</td>\n",
       "      <td>24.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>17316.59</td>\n",
       "      <td>13900.00</td>\n",
       "      <td>-3416.59</td>\n",
       "      <td>24.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>8471.55</td>\n",
       "      <td>6799.00</td>\n",
       "      <td>-1672.55</td>\n",
       "      <td>24.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>36883.77</td>\n",
       "      <td>49000.00</td>\n",
       "      <td>12116.23</td>\n",
       "      <td>24.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>8242.62</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>-1642.62</td>\n",
       "      <td>24.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>13491.16</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-2691.16</td>\n",
       "      <td>24.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>3001.37</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>998.63</td>\n",
       "      <td>24.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3499.26</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-699.26</td>\n",
       "      <td>24.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>23485.45</td>\n",
       "      <td>18777.00</td>\n",
       "      <td>-4708.45</td>\n",
       "      <td>25.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>10835.95</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>3664.05</td>\n",
       "      <td>25.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6013.05</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-1213.05</td>\n",
       "      <td>25.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>3735.04</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>1263.96</td>\n",
       "      <td>25.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>15623.39</td>\n",
       "      <td>20999.00</td>\n",
       "      <td>5375.61</td>\n",
       "      <td>25.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>8667.12</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-1767.12</td>\n",
       "      <td>25.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>8916.19</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>3082.81</td>\n",
       "      <td>25.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>8688.92</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>3011.08</td>\n",
       "      <td>25.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>12027.56</td>\n",
       "      <td>16200.00</td>\n",
       "      <td>4172.44</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>12976.15</td>\n",
       "      <td>17500.00</td>\n",
       "      <td>4523.85</td>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>44407.14</td>\n",
       "      <td>59999.00</td>\n",
       "      <td>15591.86</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>31059.18</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>10940.82</td>\n",
       "      <td>26.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>8110.06</td>\n",
       "      <td>10990.00</td>\n",
       "      <td>2879.94</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>5679.39</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>-1179.39</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>4255.62</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>1544.38</td>\n",
       "      <td>26.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>3164.66</td>\n",
       "      <td>2499.00</td>\n",
       "      <td>-665.66</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>3154.48</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>1145.52</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>4307.68</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>-907.68</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>5195.69</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-1095.69</td>\n",
       "      <td>26.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>34863.53</td>\n",
       "      <td>47600.00</td>\n",
       "      <td>12736.47</td>\n",
       "      <td>26.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>8638.63</td>\n",
       "      <td>11800.00</td>\n",
       "      <td>3161.37</td>\n",
       "      <td>26.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>32771.45</td>\n",
       "      <td>44800.00</td>\n",
       "      <td>12028.55</td>\n",
       "      <td>26.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>28154.84</td>\n",
       "      <td>38500.00</td>\n",
       "      <td>10345.16</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>67952.07</td>\n",
       "      <td>53500.00</td>\n",
       "      <td>-14452.07</td>\n",
       "      <td>27.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>13471.84</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>-2871.84</td>\n",
       "      <td>27.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>23227.15</td>\n",
       "      <td>31900.00</td>\n",
       "      <td>8672.85</td>\n",
       "      <td>27.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7131.92</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>2668.08</td>\n",
       "      <td>27.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8071.70</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>3028.30</td>\n",
       "      <td>27.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>26478.15</td>\n",
       "      <td>20800.00</td>\n",
       "      <td>-5678.15</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7116.68</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>2683.32</td>\n",
       "      <td>27.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>9312.21</td>\n",
       "      <td>7300.00</td>\n",
       "      <td>-2012.21</td>\n",
       "      <td>27.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10848.85</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-2348.85</td>\n",
       "      <td>27.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>10597.72</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-2297.72</td>\n",
       "      <td>27.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7940.44</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>3059.56</td>\n",
       "      <td>27.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>5040.46</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>1959.54</td>\n",
       "      <td>27.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>10716.00</td>\n",
       "      <td>14900.00</td>\n",
       "      <td>4184.00</td>\n",
       "      <td>28.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>9205.10</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>3594.90</td>\n",
       "      <td>28.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>31656.01</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>-6956.01</td>\n",
       "      <td>28.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>13853.36</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>-3053.36</td>\n",
       "      <td>28.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>7098.53</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>2801.47</td>\n",
       "      <td>28.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>8591.66</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>3408.34</td>\n",
       "      <td>28.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>12201.29</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-2701.29</td>\n",
       "      <td>28.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>50074.56</td>\n",
       "      <td>69990.00</td>\n",
       "      <td>19915.44</td>\n",
       "      <td>28.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>11432.63</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-2532.63</td>\n",
       "      <td>28.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6437.21</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>2561.79</td>\n",
       "      <td>28.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>8351.34</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-1851.34</td>\n",
       "      <td>28.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>35336.33</td>\n",
       "      <td>49500.00</td>\n",
       "      <td>14163.67</td>\n",
       "      <td>28.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>74232.55</td>\n",
       "      <td>103999.00</td>\n",
       "      <td>29766.45</td>\n",
       "      <td>28.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>45664.45</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>-10164.45</td>\n",
       "      <td>28.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>19298.61</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>-4299.61</td>\n",
       "      <td>28.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>11580.54</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-2580.54</td>\n",
       "      <td>28.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>23496.36</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>9503.64</td>\n",
       "      <td>28.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31557.80</td>\n",
       "      <td>24500.00</td>\n",
       "      <td>-7057.80</td>\n",
       "      <td>28.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>24323.48</td>\n",
       "      <td>18800.00</td>\n",
       "      <td>-5523.48</td>\n",
       "      <td>29.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>18285.07</td>\n",
       "      <td>25900.00</td>\n",
       "      <td>7614.93</td>\n",
       "      <td>29.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>25640.67</td>\n",
       "      <td>19800.00</td>\n",
       "      <td>-5840.67</td>\n",
       "      <td>29.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>5983.44</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>2516.56</td>\n",
       "      <td>29.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>8973.16</td>\n",
       "      <td>12750.00</td>\n",
       "      <td>3776.84</td>\n",
       "      <td>29.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>23336.06</td>\n",
       "      <td>33200.00</td>\n",
       "      <td>9863.94</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>6110.94</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>2589.06</td>\n",
       "      <td>29.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>7724.17</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>3275.83</td>\n",
       "      <td>29.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>10094.58</td>\n",
       "      <td>7777.00</td>\n",
       "      <td>-2317.58</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>21690.99</td>\n",
       "      <td>30900.00</td>\n",
       "      <td>9209.01</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>10809.48</td>\n",
       "      <td>15400.00</td>\n",
       "      <td>4590.52</td>\n",
       "      <td>29.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>11018.79</td>\n",
       "      <td>15700.00</td>\n",
       "      <td>4681.21</td>\n",
       "      <td>29.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>13377.99</td>\n",
       "      <td>10300.00</td>\n",
       "      <td>-3077.99</td>\n",
       "      <td>29.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>12731.26</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-2931.26</td>\n",
       "      <td>29.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>11178.46</td>\n",
       "      <td>8600.00</td>\n",
       "      <td>-2578.46</td>\n",
       "      <td>29.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>21681.00</td>\n",
       "      <td>30999.00</td>\n",
       "      <td>9318.00</td>\n",
       "      <td>30.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>27293.13</td>\n",
       "      <td>39040.00</td>\n",
       "      <td>11746.87</td>\n",
       "      <td>30.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>6510.80</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-1510.80</td>\n",
       "      <td>30.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>8298.32</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>3601.68</td>\n",
       "      <td>30.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>10821.19</td>\n",
       "      <td>8299.00</td>\n",
       "      <td>-2522.19</td>\n",
       "      <td>30.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>5346.97</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-1246.97</td>\n",
       "      <td>30.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5484.02</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>2415.98</td>\n",
       "      <td>30.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>5238.05</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>-1238.05</td>\n",
       "      <td>30.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>17597.10</td>\n",
       "      <td>25500.00</td>\n",
       "      <td>7902.90</td>\n",
       "      <td>30.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7727.53</td>\n",
       "      <td>11200.00</td>\n",
       "      <td>3472.47</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>6358.55</td>\n",
       "      <td>4850.00</td>\n",
       "      <td>-1508.55</td>\n",
       "      <td>31.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>2886.35</td>\n",
       "      <td>4200.00</td>\n",
       "      <td>1313.65</td>\n",
       "      <td>31.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>14049.76</td>\n",
       "      <td>10700.00</td>\n",
       "      <td>-3349.76</td>\n",
       "      <td>31.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>12974.89</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>5925.11</td>\n",
       "      <td>31.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>6858.89</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>3140.11</td>\n",
       "      <td>31.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>15691.29</td>\n",
       "      <td>22900.00</td>\n",
       "      <td>7208.71</td>\n",
       "      <td>31.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2393.34</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1106.66</td>\n",
       "      <td>31.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>6425.00</td>\n",
       "      <td>9400.00</td>\n",
       "      <td>2975.00</td>\n",
       "      <td>31.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>50206.83</td>\n",
       "      <td>73500.00</td>\n",
       "      <td>23293.17</td>\n",
       "      <td>31.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>25685.67</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>-6185.67</td>\n",
       "      <td>31.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>25951.31</td>\n",
       "      <td>19692.08</td>\n",
       "      <td>-6259.23</td>\n",
       "      <td>31.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>28910.96</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>13589.04</td>\n",
       "      <td>31.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9919.19</td>\n",
       "      <td>14600.00</td>\n",
       "      <td>4680.81</td>\n",
       "      <td>32.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>9113.05</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-2213.05</td>\n",
       "      <td>32.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>24427.14</td>\n",
       "      <td>36000.00</td>\n",
       "      <td>11572.86</td>\n",
       "      <td>32.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>4738.89</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>2261.11</td>\n",
       "      <td>32.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>25040.42</td>\n",
       "      <td>37000.00</td>\n",
       "      <td>11959.58</td>\n",
       "      <td>32.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>18403.35</td>\n",
       "      <td>13893.75</td>\n",
       "      <td>-4509.60</td>\n",
       "      <td>32.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>14576.33</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>-3576.33</td>\n",
       "      <td>32.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>6610.04</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>3189.96</td>\n",
       "      <td>32.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>10105.70</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>4894.30</td>\n",
       "      <td>32.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>5041.20</td>\n",
       "      <td>3800.00</td>\n",
       "      <td>-1241.20</td>\n",
       "      <td>32.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>6503.62</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-1603.62</td>\n",
       "      <td>32.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>13542.79</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>-3342.79</td>\n",
       "      <td>32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4364.08</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>2135.92</td>\n",
       "      <td>32.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>12006.66</td>\n",
       "      <td>17900.00</td>\n",
       "      <td>5893.34</td>\n",
       "      <td>32.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>7712.65</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>3787.35</td>\n",
       "      <td>32.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>31251.89</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-7751.89</td>\n",
       "      <td>32.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>7011.74</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>3488.26</td>\n",
       "      <td>33.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>5792.10</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>2907.90</td>\n",
       "      <td>33.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>8493.04</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>4306.96</td>\n",
       "      <td>33.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>7950.27</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>4049.73</td>\n",
       "      <td>33.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>8940.39</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>4559.61</td>\n",
       "      <td>33.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>4569.15</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>2330.85</td>\n",
       "      <td>33.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>13041.39</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>6658.61</td>\n",
       "      <td>33.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>20163.48</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>10336.52</td>\n",
       "      <td>33.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2839.03</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>1460.97</td>\n",
       "      <td>33.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>20433.23</td>\n",
       "      <td>30999.00</td>\n",
       "      <td>10565.77</td>\n",
       "      <td>34.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1746.19</td>\n",
       "      <td>2650.00</td>\n",
       "      <td>903.81</td>\n",
       "      <td>34.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>19855.31</td>\n",
       "      <td>14800.00</td>\n",
       "      <td>-5055.31</td>\n",
       "      <td>34.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>19741.65</td>\n",
       "      <td>29999.00</td>\n",
       "      <td>10257.35</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>65613.31</td>\n",
       "      <td>99999.00</td>\n",
       "      <td>34385.69</td>\n",
       "      <td>34.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>7178.20</td>\n",
       "      <td>10950.00</td>\n",
       "      <td>3771.80</td>\n",
       "      <td>34.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>9143.17</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2343.17</td>\n",
       "      <td>34.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>2285.24</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1214.76</td>\n",
       "      <td>34.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>22835.48</td>\n",
       "      <td>16950.00</td>\n",
       "      <td>-5885.48</td>\n",
       "      <td>34.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>10786.38</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>-2786.38</td>\n",
       "      <td>34.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>11477.47</td>\n",
       "      <td>17650.00</td>\n",
       "      <td>6172.53</td>\n",
       "      <td>34.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>23972.56</td>\n",
       "      <td>36900.00</td>\n",
       "      <td>12927.44</td>\n",
       "      <td>35.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3648.96</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-948.96</td>\n",
       "      <td>35.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>11706.93</td>\n",
       "      <td>18100.00</td>\n",
       "      <td>6393.07</td>\n",
       "      <td>35.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7178.20</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>3921.80</td>\n",
       "      <td>35.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>22213.53</td>\n",
       "      <td>34500.00</td>\n",
       "      <td>12286.47</td>\n",
       "      <td>35.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>22213.53</td>\n",
       "      <td>34500.00</td>\n",
       "      <td>12286.47</td>\n",
       "      <td>35.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>12897.92</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-3397.92</td>\n",
       "      <td>35.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>12904.68</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-3404.68</td>\n",
       "      <td>35.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>9611.80</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5388.20</td>\n",
       "      <td>35.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>20236.85</td>\n",
       "      <td>31600.00</td>\n",
       "      <td>11363.15</td>\n",
       "      <td>35.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>4151.02</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>2348.98</td>\n",
       "      <td>36.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>28951.20</td>\n",
       "      <td>45500.00</td>\n",
       "      <td>16548.80</td>\n",
       "      <td>36.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>25951.31</td>\n",
       "      <td>18988.13</td>\n",
       "      <td>-6963.18</td>\n",
       "      <td>36.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>5570.46</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>3229.54</td>\n",
       "      <td>36.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>22454.74</td>\n",
       "      <td>35500.00</td>\n",
       "      <td>13045.26</td>\n",
       "      <td>36.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>41711.18</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>-11211.18</td>\n",
       "      <td>36.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>3152.62</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>1846.38</td>\n",
       "      <td>36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>8902.52</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-2402.52</td>\n",
       "      <td>36.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>48699.98</td>\n",
       "      <td>77500.00</td>\n",
       "      <td>28800.02</td>\n",
       "      <td>37.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>16821.54</td>\n",
       "      <td>26800.00</td>\n",
       "      <td>9978.46</td>\n",
       "      <td>37.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>5521.28</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>3278.72</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>5268.17</td>\n",
       "      <td>8400.00</td>\n",
       "      <td>3131.83</td>\n",
       "      <td>37.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>65825.25</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>39173.75</td>\n",
       "      <td>37.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>12459.85</td>\n",
       "      <td>19900.00</td>\n",
       "      <td>7440.15</td>\n",
       "      <td>37.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>65723.62</td>\n",
       "      <td>104999.00</td>\n",
       "      <td>39275.38</td>\n",
       "      <td>37.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>9070.23</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>5429.77</td>\n",
       "      <td>37.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>9348.90</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2548.90</td>\n",
       "      <td>37.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>9369.66</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>5630.34</td>\n",
       "      <td>37.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>32954.19</td>\n",
       "      <td>52777.00</td>\n",
       "      <td>19822.81</td>\n",
       "      <td>37.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>17195.57</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-4695.57</td>\n",
       "      <td>37.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>3425.23</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>2074.77</td>\n",
       "      <td>37.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>3173.48</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>1926.52</td>\n",
       "      <td>37.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>10337.11</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-2837.11</td>\n",
       "      <td>37.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>65742.57</td>\n",
       "      <td>105999.00</td>\n",
       "      <td>40256.43</td>\n",
       "      <td>37.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>8661.95</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>5337.05</td>\n",
       "      <td>38.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26062.23</td>\n",
       "      <td>42500.00</td>\n",
       "      <td>16437.77</td>\n",
       "      <td>38.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>7630.64</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-2130.64</td>\n",
       "      <td>38.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>11378.43</td>\n",
       "      <td>18600.00</td>\n",
       "      <td>7221.57</td>\n",
       "      <td>38.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>66032.76</td>\n",
       "      <td>107999.00</td>\n",
       "      <td>41966.24</td>\n",
       "      <td>38.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>7279.97</td>\n",
       "      <td>11950.00</td>\n",
       "      <td>4670.03</td>\n",
       "      <td>39.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>18577.88</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>11922.12</td>\n",
       "      <td>39.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4043.74</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>-1143.74</td>\n",
       "      <td>39.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2178.94</td>\n",
       "      <td>3600.00</td>\n",
       "      <td>1421.06</td>\n",
       "      <td>39.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23286.27</td>\n",
       "      <td>38500.00</td>\n",
       "      <td>15213.73</td>\n",
       "      <td>39.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>12558.90</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-3559.90</td>\n",
       "      <td>39.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>18432.63</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>12067.37</td>\n",
       "      <td>39.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>9057.77</td>\n",
       "      <td>14999.00</td>\n",
       "      <td>5941.23</td>\n",
       "      <td>39.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>13476.90</td>\n",
       "      <td>22500.00</td>\n",
       "      <td>9023.10</td>\n",
       "      <td>40.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>37704.71</td>\n",
       "      <td>26900.00</td>\n",
       "      <td>-10804.71</td>\n",
       "      <td>40.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>32945.80</td>\n",
       "      <td>23500.00</td>\n",
       "      <td>-9445.80</td>\n",
       "      <td>40.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>7712.65</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-2212.65</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>65742.57</td>\n",
       "      <td>109999.00</td>\n",
       "      <td>44256.43</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>36764.38</td>\n",
       "      <td>62000.00</td>\n",
       "      <td>25235.62</td>\n",
       "      <td>40.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>77403.06</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>-22403.06</td>\n",
       "      <td>40.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>8530.54</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>5969.46</td>\n",
       "      <td>41.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>15259.81</td>\n",
       "      <td>26000.00</td>\n",
       "      <td>10740.19</td>\n",
       "      <td>41.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>16253.48</td>\n",
       "      <td>11500.00</td>\n",
       "      <td>-4753.48</td>\n",
       "      <td>41.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1815.28</td>\n",
       "      <td>3100.00</td>\n",
       "      <td>1284.72</td>\n",
       "      <td>41.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>44193.38</td>\n",
       "      <td>75500.00</td>\n",
       "      <td>31306.62</td>\n",
       "      <td>41.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45272.25</td>\n",
       "      <td>31990.00</td>\n",
       "      <td>-13282.25</td>\n",
       "      <td>41.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>9198.08</td>\n",
       "      <td>15800.00</td>\n",
       "      <td>6601.92</td>\n",
       "      <td>41.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>19854.79</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>-5854.79</td>\n",
       "      <td>41.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>10031.96</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>7268.04</td>\n",
       "      <td>42.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>9661.78</td>\n",
       "      <td>6800.00</td>\n",
       "      <td>-2861.78</td>\n",
       "      <td>42.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2539.57</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>1860.43</td>\n",
       "      <td>42.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>11474.22</td>\n",
       "      <td>19999.00</td>\n",
       "      <td>8524.78</td>\n",
       "      <td>42.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>65887.51</td>\n",
       "      <td>114999.00</td>\n",
       "      <td>49111.49</td>\n",
       "      <td>42.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2784.46</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>-834.46</td>\n",
       "      <td>42.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>15015.22</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-4515.22</td>\n",
       "      <td>43.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>31216.83</td>\n",
       "      <td>55000.00</td>\n",
       "      <td>23783.17</td>\n",
       "      <td>43.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>5374.74</td>\n",
       "      <td>3750.00</td>\n",
       "      <td>-1624.74</td>\n",
       "      <td>43.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7241.07</td>\n",
       "      <td>12800.00</td>\n",
       "      <td>5558.93</td>\n",
       "      <td>43.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>3450.69</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>2649.31</td>\n",
       "      <td>43.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>16724.45</td>\n",
       "      <td>29600.00</td>\n",
       "      <td>12875.55</td>\n",
       "      <td>43.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>65210.35</td>\n",
       "      <td>115800.00</td>\n",
       "      <td>50589.65</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>10740.09</td>\n",
       "      <td>19100.00</td>\n",
       "      <td>8359.91</td>\n",
       "      <td>43.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>12234.68</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>-3734.68</td>\n",
       "      <td>43.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>14402.34</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>-4402.34</td>\n",
       "      <td>44.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>10807.78</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-3307.78</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>39074.36</td>\n",
       "      <td>69999.00</td>\n",
       "      <td>30924.64</td>\n",
       "      <td>44.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>14512.61</td>\n",
       "      <td>25999.00</td>\n",
       "      <td>11486.39</td>\n",
       "      <td>44.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3316.44</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-1016.44</td>\n",
       "      <td>44.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>12255.45</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>9744.55</td>\n",
       "      <td>44.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>6781.86</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>-2081.86</td>\n",
       "      <td>44.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>3752.54</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-1152.54</td>\n",
       "      <td>44.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2389.14</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>-739.14</td>\n",
       "      <td>44.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>34761.74</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>-10761.74</td>\n",
       "      <td>44.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>11881.05</td>\n",
       "      <td>8200.00</td>\n",
       "      <td>-3681.05</td>\n",
       "      <td>44.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>1873.03</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>1526.97</td>\n",
       "      <td>44.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13212.52</td>\n",
       "      <td>23999.00</td>\n",
       "      <td>10786.48</td>\n",
       "      <td>44.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>17282.06</td>\n",
       "      <td>11900.00</td>\n",
       "      <td>-5382.06</td>\n",
       "      <td>45.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>17011.43</td>\n",
       "      <td>11700.00</td>\n",
       "      <td>-5311.43</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2020.25</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>1679.75</td>\n",
       "      <td>45.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>18942.34</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>-5943.34</td>\n",
       "      <td>45.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>11337.51</td>\n",
       "      <td>20900.00</td>\n",
       "      <td>9562.49</td>\n",
       "      <td>45.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>10547.82</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>8952.18</td>\n",
       "      <td>45.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>6421.18</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-2021.18</td>\n",
       "      <td>45.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>12414.72</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>10585.28</td>\n",
       "      <td>46.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>24796.09</td>\n",
       "      <td>46000.00</td>\n",
       "      <td>21203.91</td>\n",
       "      <td>46.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>4172.84</td>\n",
       "      <td>7750.00</td>\n",
       "      <td>3577.16</td>\n",
       "      <td>46.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>15956.95</td>\n",
       "      <td>29999.00</td>\n",
       "      <td>14042.05</td>\n",
       "      <td>46.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>1858.34</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>1641.66</td>\n",
       "      <td>46.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>8522.39</td>\n",
       "      <td>5800.00</td>\n",
       "      <td>-2722.39</td>\n",
       "      <td>46.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>13235.55</td>\n",
       "      <td>8999.00</td>\n",
       "      <td>-4236.55</td>\n",
       "      <td>47.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>13600.39</td>\n",
       "      <td>25800.00</td>\n",
       "      <td>12199.61</td>\n",
       "      <td>47.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>4032.10</td>\n",
       "      <td>7650.00</td>\n",
       "      <td>3617.90</td>\n",
       "      <td>47.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>12414.41</td>\n",
       "      <td>8420.00</td>\n",
       "      <td>-3994.41</td>\n",
       "      <td>47.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2573.48</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>2326.52</td>\n",
       "      <td>47.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>14572.70</td>\n",
       "      <td>27900.00</td>\n",
       "      <td>13327.30</td>\n",
       "      <td>47.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>15144.28</td>\n",
       "      <td>29000.00</td>\n",
       "      <td>13855.72</td>\n",
       "      <td>47.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20159.19</td>\n",
       "      <td>38888.00</td>\n",
       "      <td>18728.81</td>\n",
       "      <td>48.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7241.07</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>6758.93</td>\n",
       "      <td>48.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>25682.58</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>-8382.58</td>\n",
       "      <td>48.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>10563.39</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>9936.61</td>\n",
       "      <td>48.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>6546.81</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-2146.81</td>\n",
       "      <td>48.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>38260.36</td>\n",
       "      <td>75555.00</td>\n",
       "      <td>37294.64</td>\n",
       "      <td>49.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>38260.36</td>\n",
       "      <td>75555.00</td>\n",
       "      <td>37294.64</td>\n",
       "      <td>49.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>13443.32</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-4443.32</td>\n",
       "      <td>49.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>14933.54</td>\n",
       "      <td>29500.00</td>\n",
       "      <td>14566.46</td>\n",
       "      <td>49.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>13884.26</td>\n",
       "      <td>27800.00</td>\n",
       "      <td>13915.74</td>\n",
       "      <td>50.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>31373.47</td>\n",
       "      <td>20859.15</td>\n",
       "      <td>-10514.32</td>\n",
       "      <td>50.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>47014.52</td>\n",
       "      <td>95000.00</td>\n",
       "      <td>47985.48</td>\n",
       "      <td>50.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>41075.73</td>\n",
       "      <td>27200.00</td>\n",
       "      <td>-13875.73</td>\n",
       "      <td>51.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>10125.27</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-3425.27</td>\n",
       "      <td>51.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>60603.06</td>\n",
       "      <td>124000.00</td>\n",
       "      <td>63396.94</td>\n",
       "      <td>51.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>14363.76</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>-4863.76</td>\n",
       "      <td>51.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>25302.43</td>\n",
       "      <td>52000.00</td>\n",
       "      <td>26697.57</td>\n",
       "      <td>51.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>7391.67</td>\n",
       "      <td>15200.00</td>\n",
       "      <td>7808.33</td>\n",
       "      <td>51.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>9753.44</td>\n",
       "      <td>20400.00</td>\n",
       "      <td>10646.56</td>\n",
       "      <td>52.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>32925.60</td>\n",
       "      <td>69500.00</td>\n",
       "      <td>36574.40</td>\n",
       "      <td>52.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>22941.80</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>-7941.80</td>\n",
       "      <td>52.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>6199.59</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>7000.41</td>\n",
       "      <td>53.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>5597.06</td>\n",
       "      <td>11999.00</td>\n",
       "      <td>6401.94</td>\n",
       "      <td>53.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>13409.47</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>-4709.47</td>\n",
       "      <td>54.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>6635.73</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-2335.73</td>\n",
       "      <td>54.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>33707.27</td>\n",
       "      <td>73900.00</td>\n",
       "      <td>40192.73</td>\n",
       "      <td>54.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>19309.90</td>\n",
       "      <td>12500.00</td>\n",
       "      <td>-6809.90</td>\n",
       "      <td>54.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>19624.73</td>\n",
       "      <td>12700.00</td>\n",
       "      <td>-6924.73</td>\n",
       "      <td>54.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>13757.58</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-4857.58</td>\n",
       "      <td>54.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>12354.92</td>\n",
       "      <td>27500.00</td>\n",
       "      <td>15145.08</td>\n",
       "      <td>55.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>17714.62</td>\n",
       "      <td>11400.00</td>\n",
       "      <td>-6314.62</td>\n",
       "      <td>55.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>11555.80</td>\n",
       "      <td>7400.00</td>\n",
       "      <td>-4155.80</td>\n",
       "      <td>56.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>22011.47</td>\n",
       "      <td>13999.00</td>\n",
       "      <td>-8012.47</td>\n",
       "      <td>57.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>15457.91</td>\n",
       "      <td>9800.00</td>\n",
       "      <td>-5657.91</td>\n",
       "      <td>57.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>11099.09</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-4100.09</td>\n",
       "      <td>58.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>35039.40</td>\n",
       "      <td>85555.00</td>\n",
       "      <td>50515.60</td>\n",
       "      <td>59.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>34376.16</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>50623.84</td>\n",
       "      <td>59.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>16773.62</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>-6273.62</td>\n",
       "      <td>59.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>27487.75</td>\n",
       "      <td>68500.00</td>\n",
       "      <td>41012.25</td>\n",
       "      <td>59.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>7996.00</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-2997.00</td>\n",
       "      <td>59.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>5284.06</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-1984.06</td>\n",
       "      <td>60.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>34863.53</td>\n",
       "      <td>87777.00</td>\n",
       "      <td>52913.47</td>\n",
       "      <td>60.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>19701.70</td>\n",
       "      <td>49999.00</td>\n",
       "      <td>30297.30</td>\n",
       "      <td>60.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>30409.09</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>-11509.09</td>\n",
       "      <td>60.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>14486.38</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-5486.38</td>\n",
       "      <td>60.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>11601.33</td>\n",
       "      <td>7200.00</td>\n",
       "      <td>-4401.33</td>\n",
       "      <td>61.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>10824.89</td>\n",
       "      <td>6700.00</td>\n",
       "      <td>-4124.89</td>\n",
       "      <td>61.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>65448.37</td>\n",
       "      <td>40500.00</td>\n",
       "      <td>-24948.37</td>\n",
       "      <td>61.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>5351.86</td>\n",
       "      <td>3300.00</td>\n",
       "      <td>-2051.86</td>\n",
       "      <td>62.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>25864.65</td>\n",
       "      <td>69990.00</td>\n",
       "      <td>44125.35</td>\n",
       "      <td>63.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>15682.82</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>-6082.82</td>\n",
       "      <td>63.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>40765.33</td>\n",
       "      <td>112000.00</td>\n",
       "      <td>71234.67</td>\n",
       "      <td>63.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>1320.56</td>\n",
       "      <td>3700.00</td>\n",
       "      <td>2379.44</td>\n",
       "      <td>64.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3795.06</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-1495.06</td>\n",
       "      <td>65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>19335.34</td>\n",
       "      <td>55555.00</td>\n",
       "      <td>36219.66</td>\n",
       "      <td>65.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>26736.21</td>\n",
       "      <td>16100.00</td>\n",
       "      <td>-10636.21</td>\n",
       "      <td>66.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>26441.03</td>\n",
       "      <td>15900.00</td>\n",
       "      <td>-10541.03</td>\n",
       "      <td>66.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>5002.39</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>-2003.39</td>\n",
       "      <td>66.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>18317.20</td>\n",
       "      <td>10974.21</td>\n",
       "      <td>-7342.99</td>\n",
       "      <td>66.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>40893.84</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>85106.16</td>\n",
       "      <td>67.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>38473.71</td>\n",
       "      <td>119000.00</td>\n",
       "      <td>80526.29</td>\n",
       "      <td>67.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>34863.53</td>\n",
       "      <td>109999.00</td>\n",
       "      <td>75135.47</td>\n",
       "      <td>68.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>10946.44</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-4446.44</td>\n",
       "      <td>68.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>3205.38</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>-1305.38</td>\n",
       "      <td>68.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>16924.74</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>-6925.74</td>\n",
       "      <td>69.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9356.43</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-3856.43</td>\n",
       "      <td>70.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>15374.08</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>-6474.08</td>\n",
       "      <td>72.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>11056.52</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>-4656.52</td>\n",
       "      <td>72.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2440.90</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>-1040.90</td>\n",
       "      <td>74.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>3939.91</td>\n",
       "      <td>15500.00</td>\n",
       "      <td>11560.09</td>\n",
       "      <td>74.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>15380.04</td>\n",
       "      <td>8800.00</td>\n",
       "      <td>-6580.04</td>\n",
       "      <td>74.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>35093.27</td>\n",
       "      <td>19990.00</td>\n",
       "      <td>-15103.27</td>\n",
       "      <td>75.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>8270.38</td>\n",
       "      <td>4700.00</td>\n",
       "      <td>-3570.38</td>\n",
       "      <td>75.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>6916.13</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>-3016.13</td>\n",
       "      <td>77.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>6746.48</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-2947.48</td>\n",
       "      <td>77.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2555.02</td>\n",
       "      <td>11600.00</td>\n",
       "      <td>9044.98</td>\n",
       "      <td>77.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>13355.11</td>\n",
       "      <td>7500.00</td>\n",
       "      <td>-5855.11</td>\n",
       "      <td>78.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>3948.74</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-1748.74</td>\n",
       "      <td>79.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>9024.05</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>-4124.05</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>15292.10</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-6992.10</td>\n",
       "      <td>84.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>7621.25</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-3521.25</td>\n",
       "      <td>85.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>2975.40</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>-1375.40</td>\n",
       "      <td>85.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>14915.98</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-7015.98</td>\n",
       "      <td>88.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>5716.14</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-2716.14</td>\n",
       "      <td>90.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>15819.50</td>\n",
       "      <td>8300.00</td>\n",
       "      <td>-7519.50</td>\n",
       "      <td>90.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>13420.15</td>\n",
       "      <td>6999.00</td>\n",
       "      <td>-6421.15</td>\n",
       "      <td>91.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>23576.99</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>-11576.99</td>\n",
       "      <td>96.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>8648.80</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-4248.80</td>\n",
       "      <td>96.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15559.43</td>\n",
       "      <td>7900.00</td>\n",
       "      <td>-7659.43</td>\n",
       "      <td>96.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>4004.65</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-2004.65</td>\n",
       "      <td>100.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>27225.34</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>-13725.34</td>\n",
       "      <td>101.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>5462.52</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-2762.52</td>\n",
       "      <td>102.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>8131.92</td>\n",
       "      <td>3999.00</td>\n",
       "      <td>-4132.92</td>\n",
       "      <td>103.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>10810.62</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>-5810.62</td>\n",
       "      <td>116.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>7973.87</td>\n",
       "      <td>3650.00</td>\n",
       "      <td>-4323.87</td>\n",
       "      <td>118.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5948.04</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-3348.04</td>\n",
       "      <td>128.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>6891.99</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-3891.99</td>\n",
       "      <td>129.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>26331.41</td>\n",
       "      <td>11200.00</td>\n",
       "      <td>-15131.41</td>\n",
       "      <td>135.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>21493.44</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>-12493.44</td>\n",
       "      <td>138.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>6537.82</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>-3837.82</td>\n",
       "      <td>142.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>12891.96</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>-7591.96</td>\n",
       "      <td>143.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>8340.94</td>\n",
       "      <td>3350.00</td>\n",
       "      <td>-4990.94</td>\n",
       "      <td>148.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>3040.77</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>-1840.77</td>\n",
       "      <td>153.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>9631.08</td>\n",
       "      <td>3799.00</td>\n",
       "      <td>-5832.08</td>\n",
       "      <td>153.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13967.55</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>-8467.55</td>\n",
       "      <td>153.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>3818.71</td>\n",
       "      <td>1450.00</td>\n",
       "      <td>-2368.71</td>\n",
       "      <td>163.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>7809.13</td>\n",
       "      <td>2899.00</td>\n",
       "      <td>-4910.13</td>\n",
       "      <td>169.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>4590.49</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>-2890.49</td>\n",
       "      <td>170.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>7320.42</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>-4720.42</td>\n",
       "      <td>181.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>12261.19</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>-8161.19</td>\n",
       "      <td>199.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7553.29</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-5053.29</td>\n",
       "      <td>202.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>9817.06</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>-6617.06</td>\n",
       "      <td>206.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>7161.67</td>\n",
       "      <td>2200.00</td>\n",
       "      <td>-4961.67</td>\n",
       "      <td>225.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>7918.89</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>-5518.89</td>\n",
       "      <td>229.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>9984.87</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-6984.87</td>\n",
       "      <td>232.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>10381.62</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-7381.62</td>\n",
       "      <td>246.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>8843.22</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>-6343.22</td>\n",
       "      <td>253.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>10123.04</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>-7323.04</td>\n",
       "      <td>261.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>16165.79</td>\n",
       "      <td>4400.00</td>\n",
       "      <td>-11765.79</td>\n",
       "      <td>267.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>17937.36</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>-13137.36</td>\n",
       "      <td>273.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>7319.77</td>\n",
       "      <td>1850.00</td>\n",
       "      <td>-5469.77</td>\n",
       "      <td>295.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>16095.32</td>\n",
       "      <td>3600.00</td>\n",
       "      <td>-12495.32</td>\n",
       "      <td>347.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10685.50</td>\n",
       "      <td>2300.00</td>\n",
       "      <td>-8385.50</td>\n",
       "      <td>364.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>9664.46</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>-7764.46</td>\n",
       "      <td>408.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>32481.05</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>-26481.05</td>\n",
       "      <td>441.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>9954.42</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>-8154.42</td>\n",
       "      <td>453.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>35956.50</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>-29456.50</td>\n",
       "      <td>453.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>10019.90</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>-8219.90</td>\n",
       "      <td>456.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>30628.28</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>-25629.28</td>\n",
       "      <td>512.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predictions    Target  Residual  Difference%\n",
       "698     30480.85  30500.00     19.15         0.06\n",
       "742     16960.31  16999.00     38.69         0.23\n",
       "60      12469.21  12500.00     30.79         0.25\n",
       "110     25614.14  25500.00   -114.14         0.45\n",
       "367     42703.68  42500.00   -203.68         0.48\n",
       "369      3084.69   3100.00     15.31         0.49\n",
       "769     29651.73  29500.00   -151.73         0.51\n",
       "272      9749.53   9800.00     50.47         0.52\n",
       "714     23118.07  22999.00   -119.07         0.52\n",
       "630      8734.58   8800.00     65.42         0.74\n",
       "380      3473.79   3500.00     26.21         0.75\n",
       "648     21174.10  21335.00    160.90         0.75\n",
       "308      8967.74   8900.00    -67.74         0.76\n",
       "665     17858.02  18000.00    141.98         0.79\n",
       "379     17654.84  17800.00    145.16         0.82\n",
       "719     11391.95  11500.00    108.05         0.94\n",
       "102     28625.56  28900.00    274.44         0.95\n",
       "94       7724.17   7800.00     75.83         0.97\n",
       "561      6429.03   6500.00     70.97         1.09\n",
       "242      7597.39   7500.00    -97.39         1.30\n",
       "528     18555.09  18800.00    244.91         1.30\n",
       "61       7396.87   7300.00    -96.87         1.33\n",
       "19      16178.14  16400.00    221.86         1.35\n",
       "280     12327.10  12499.00    171.90         1.38\n",
       "311     51287.19  52055.25    768.06         1.48\n",
       "723      6009.63   6100.00     90.37         1.48\n",
       "49       4973.17   4900.00    -73.17         1.49\n",
       "114     27716.14  27300.00   -416.14         1.52\n",
       "636     28498.91  28950.00    451.09         1.56\n",
       "612      2953.17   3000.00     46.83         1.56\n",
       "47      26425.14  25999.00   -426.14         1.64\n",
       "23      13111.91  12900.00   -211.91         1.64\n",
       "31      12858.08  12650.00   -208.08         1.64\n",
       "91      13421.16  13200.00   -221.16         1.68\n",
       "329      7327.18   7200.00   -127.18         1.77\n",
       "549      3816.33   3750.00    -66.33         1.77\n",
       "252      9721.50   9900.00    178.50         1.80\n",
       "387     44173.72  44999.00    825.28         1.83\n",
       "267     40753.58  40000.00   -753.58         1.88\n",
       "467     22262.80  22711.65    448.85         1.98\n",
       "556     18231.44  18600.00    368.56         1.98\n",
       "165      9596.94   9400.00   -196.94         2.10\n",
       "259      6067.79   6200.00    132.21         2.13\n",
       "601     35371.16  34600.00   -771.16         2.23\n",
       "708     11967.39  11700.00   -267.39         2.29\n",
       "593     17908.00  17500.00   -408.00         2.33\n",
       "398      8707.13   8500.00   -207.13         2.44\n",
       "526     29049.27  28350.00   -699.27         2.47\n",
       "603     14513.46  14900.00    386.54         2.59\n",
       "53      20453.89  21000.00    546.11         2.60\n",
       "632     15383.35  14990.00   -393.35         2.62\n",
       "533     24642.50  24000.00   -642.50         2.68\n",
       "497     50099.92  51500.00   1400.08         2.72\n",
       "212     16133.86  15700.00   -433.86         2.76\n",
       "130     17489.92  18000.00    510.08         2.83\n",
       "290      1894.40   1950.00     55.60         2.85\n",
       "78      30810.25  29900.00   -910.25         3.04\n",
       "642      8721.97   8999.00    277.03         3.08\n",
       "437     18866.50  18300.00   -566.50         3.10\n",
       "101      5958.63   6150.00    191.37         3.11\n",
       "314      5811.74   6000.00    188.26         3.14\n",
       "150      9800.43   9500.00   -300.43         3.16\n",
       "565      7324.63   7100.00   -224.63         3.16\n",
       "574     12583.52  13000.00    416.48         3.20\n",
       "591     10115.13   9800.00   -315.13         3.22\n",
       "172     11156.38  10800.00   -356.38         3.30\n",
       "133      9279.28   9600.00    320.72         3.34\n",
       "480     31369.37  32500.00   1130.63         3.48\n",
       "87       2315.71   2400.00     84.29         3.51\n",
       "11       5175.77   5000.00   -175.77         3.52\n",
       "43      21611.83  22400.00    788.17         3.52\n",
       "96       7976.26   7700.00   -276.26         3.59\n",
       "406     24874.86  23999.00   -875.86         3.65\n",
       "173     36516.35  37900.00   1383.65         3.65\n",
       "540      4666.05   4500.00   -166.05         3.69\n",
       "40      18672.68  18000.00   -672.68         3.74\n",
       "340     14815.83  15400.00    584.17         3.79\n",
       "239     10581.62  10999.00    417.38         3.79\n",
       "109     12663.54  12200.00   -463.54         3.80\n",
       "256      1825.44   1900.00     74.56         3.92\n",
       "317     12247.90  12750.00    502.10         3.94\n",
       "77       5930.73   5700.00   -230.73         4.05\n",
       "301      9782.47  10200.00    417.53         4.09\n",
       "333     12452.22  11960.00   -492.22         4.12\n",
       "570     23163.87  24171.42   1007.55         4.17\n",
       "581      3246.94   3390.00    143.06         4.22\n",
       "693     12354.16  12900.00    545.84         4.23\n",
       "381     33499.44  35000.00   1500.56         4.29\n",
       "438     16257.03  16999.00    741.97         4.36\n",
       "368      8415.81   8800.00    384.19         4.37\n",
       "273     12318.39  12900.00    581.61         4.51\n",
       "235      2765.14   2900.00    134.86         4.65\n",
       "168     11420.95  11999.00    578.05         4.82\n",
       "707      2725.40   2600.00   -125.40         4.82\n",
       "72      23624.69  22500.00  -1124.69         5.00\n",
       "559     11377.84  11999.00    621.16         5.18\n",
       "134     12096.18  11500.00   -596.18         5.18\n",
       "446      9363.27   8900.00   -463.27         5.21\n",
       "127     12311.47  11700.00   -611.47         5.23\n",
       "450     14218.94  13500.00   -718.94         5.33\n",
       "293      8431.89   7999.00   -432.89         5.41\n",
       "18      37600.15  39900.00   2299.85         5.76\n",
       "452     39882.67  37700.00  -2182.67         5.79\n",
       "195     20588.57  21900.00   1311.43         5.99\n",
       "676      6861.84   7300.00    438.16         6.00\n",
       "766     18982.15  17900.00  -1082.15         6.05\n",
       "328     15067.85  14200.00   -867.85         6.11\n",
       "762     12201.29  12999.00    797.71         6.14\n",
       "649     11147.91  10500.00   -647.91         6.17\n",
       "299     20183.30  18999.00  -1184.30         6.23\n",
       "641     17334.11  18500.00   1165.89         6.30\n",
       "5       21289.80  20000.00  -1289.80         6.45\n",
       "545      6826.78   7300.00    473.22         6.48\n",
       "622      4472.47   4200.00   -272.47         6.49\n",
       "422     53294.61  57000.00   3705.39         6.50\n",
       "740      6658.73   6250.00   -408.73         6.54\n",
       "85      19001.29  17800.00  -1201.29         6.75\n",
       "315      4590.49   4300.00   -290.49         6.76\n",
       "462     43236.92  40500.00  -2736.92         6.76\n",
       "394      9076.42   8500.00   -576.42         6.78\n",
       "188     10159.18  10900.00    740.82         6.80\n",
       "731     16027.02  15000.00  -1027.02         6.85\n",
       "448     13502.35  14500.00    997.65         6.88\n",
       "254      6413.26   5999.00   -414.26         6.91\n",
       "667     49221.63  52999.00   3777.37         7.13\n",
       "600     21816.43  23500.00   1683.57         7.16\n",
       "144     11969.66  12900.00    930.34         7.21\n",
       "270      8263.95   7700.00   -563.95         7.32\n",
       "8       11581.54  12500.00    918.46         7.35\n",
       "433      6977.90   6500.00   -477.90         7.35\n",
       "251      6978.91   6500.00   -478.91         7.37\n",
       "553     16392.22  17700.00   1307.78         7.39\n",
       "725     15742.41  16999.00   1256.59         7.39\n",
       "615      7869.47   8500.00    630.53         7.42\n",
       "518     21297.65  19800.00  -1497.65         7.56\n",
       "268      6893.62   6400.00   -493.62         7.71\n",
       "71      15627.03  14500.00  -1127.03         7.77\n",
       "579      3411.53   3700.00    288.47         7.80\n",
       "330     11336.71  12300.00    963.29         7.83\n",
       "59       8195.34   7600.00   -595.34         7.83\n",
       "354     16717.08  15500.00  -1217.08         7.85\n",
       "147      7772.57   7200.00   -572.57         7.95\n",
       "508     16197.42  15000.00  -1197.42         7.98\n",
       "22       6102.36   5650.00   -452.36         8.01\n",
       "103      4488.43   4150.00   -338.43         8.16\n",
       "755     15885.66  17300.00   1414.34         8.18\n",
       "198     17983.10  19600.00   1616.90         8.25\n",
       "470      7045.72   6500.00   -545.72         8.40\n",
       "710      3795.06   3500.00   -295.06         8.43\n",
       "185      6130.96   6700.00    569.04         8.49\n",
       "542      5947.51   6500.00    552.49         8.50\n",
       "412      7609.28   7000.00   -609.28         8.70\n",
       "415      8117.44   8900.00    782.56         8.79\n",
       "200     11098.18  10200.00   -898.18         8.81\n",
       "754     17028.45  18700.00   1671.55         8.94\n",
       "65      19847.25  18200.00  -1647.25         9.05\n",
       "682      5454.34   5000.00   -454.34         9.09\n",
       "222     20662.46  18900.00  -1762.46         9.33\n",
       "89      11696.25  12900.00   1203.75         9.33\n",
       "52      10716.84   9800.00   -916.84         9.36\n",
       "126     13459.92  12300.00  -1159.92         9.43\n",
       "598      8767.54   9700.00    932.46         9.61\n",
       "504     23272.83  25749.75   2476.92         9.62\n",
       "493      5484.97   4999.00   -485.97         9.72\n",
       "449      9495.62   8650.00   -845.62         9.78\n",
       "516     11858.48  10800.00  -1058.48         9.80\n",
       "261     53312.81  48535.50  -4777.31         9.84\n",
       "531     47047.70  52300.00   5252.30        10.04\n",
       "68       6717.17   6100.00   -617.17        10.12\n",
       "376      5391.77   6000.00    608.23        10.14\n",
       "538     17591.09  19600.00   2008.91        10.25\n",
       "419     17591.09  19600.00   2008.91        10.25\n",
       "595     25576.58  28500.00   2923.42        10.26\n",
       "472     20367.25  22700.00   2332.75        10.28\n",
       "386      7267.36   8100.00    832.64        10.28\n",
       "473      1792.36   1999.00    206.64        10.34\n",
       "14       8248.67   9200.00    951.33        10.34\n",
       "123      5643.45   6300.00    656.55        10.42\n",
       "142      6839.57   7650.00    810.43        10.59\n",
       "257     41503.90  37500.00  -4003.90        10.68\n",
       "194      9310.69   8400.00   -910.69        10.84\n",
       "637     35662.26  40000.00   4337.74        10.84\n",
       "410     16407.29  14800.00  -1607.29        10.86\n",
       "395     21303.57  23900.00   2596.43        10.86\n",
       "213     15872.91  14300.00  -1572.91        11.00\n",
       "653     10332.54   9300.00  -1032.54        11.10\n",
       "54      18575.43  20900.00   2324.57        11.12\n",
       "105      8882.65  10000.00   1117.35        11.17\n",
       "597      7797.18   6999.00   -798.18        11.40\n",
       "15      10621.84  11999.00   1377.16        11.48\n",
       "770     10732.07   9600.00  -1132.07        11.79\n",
       "674      2381.01   2700.00    318.99        11.81\n",
       "132     11200.20   9999.00  -1201.20        12.01\n",
       "525      2373.66   2700.00    326.34        12.09\n",
       "730     10696.56  12179.00   1482.44        12.17\n",
       "506      3590.97   3200.00   -390.97        12.22\n",
       "278     53063.22  60500.00   7436.78        12.29\n",
       "108      7300.05   6500.00   -800.05        12.31\n",
       "503      6392.70   7300.00    907.30        12.43\n",
       "536      6746.43   6000.00   -746.43        12.44\n",
       "156      9192.06  10500.00   1307.94        12.46\n",
       "765     11034.66   9800.00  -1234.66        12.60\n",
       "534     16939.86  19400.00   2460.14        12.68\n",
       "50       2479.43   2200.00   -279.43        12.70\n",
       "562      9593.52  11000.00   1406.48        12.79\n",
       "131     23967.81  27500.00   3532.19        12.84\n",
       "157      7491.01   8600.00   1108.99        12.90\n",
       "638      8242.62   7300.00   -942.62        12.91\n",
       "543      9489.56  10900.00   1410.44        12.94\n",
       "724     44312.98  50900.00   6587.02        12.94\n",
       "527      9829.53   8700.00  -1129.53        12.98\n",
       "610      9269.68   8200.00  -1069.68        13.04\n",
       "359      4524.77   3999.00   -525.77        13.15\n",
       "24      23650.15  20900.00  -2750.15        13.16\n",
       "402      8850.21  10200.00   1349.79        13.23\n",
       "360      4770.52   5500.00    729.48        13.26\n",
       "184     29479.97  34000.00   4520.03        13.29\n",
       "324      8316.92   9600.00   1283.08        13.37\n",
       "307     24974.57  28900.00   3925.43        13.58\n",
       "118     14943.75  17300.00   2356.25        13.62\n",
       "607     28498.91  33000.00   4501.09        13.64\n",
       "332     14591.35  16900.00   2308.65        13.66\n",
       "140     10358.58  12000.00   1641.42        13.68\n",
       "627     13074.74  11500.00  -1574.74        13.69\n",
       "761      9097.13   8000.00  -1097.13        13.71\n",
       "207     15010.51  13200.00  -1810.51        13.72\n",
       "685     12593.00  14600.00   2007.00        13.75\n",
       "350     20246.04  23500.00   3253.96        13.85\n",
       "143      8768.29   7700.00  -1068.29        13.87\n",
       "318     39744.80  34900.00  -4844.80        13.88\n",
       "377      9909.62   8700.00  -1209.62        13.90\n",
       "152     44680.77  51900.00   7219.23        13.91\n",
       "57      21942.05  25500.00   3557.95        13.95\n",
       "277     12900.31  15000.00   2099.69        14.00\n",
       "253     18128.32  15900.00  -2228.32        14.01\n",
       "384      4296.77   5000.00    703.23        14.06\n",
       "113      6006.27   6999.00    992.73        14.18\n",
       "287      1972.28   2300.00    327.72        14.25\n",
       "650      8236.36   7200.00  -1036.36        14.39\n",
       "319     10612.51  12400.00   1787.49        14.42\n",
       "430     14197.26  12400.00  -1797.26        14.49\n",
       "95      47013.28  55000.00   7986.72        14.52\n",
       "757     12028.91  10500.00  -1528.91        14.56\n",
       "153      3666.31   3200.00   -466.31        14.57\n",
       "720      2860.97   3350.00    489.03        14.60\n",
       "631      7256.86   8500.00   1243.14        14.63\n",
       "36      10499.60  12300.00   1800.40        14.64\n",
       "763     12383.35  10800.00  -1583.35        14.66\n",
       "344     11937.31  13995.00   2057.69        14.70\n",
       "389      2810.71   2450.00   -360.71        14.72\n",
       "204      7664.32   9000.00   1335.68        14.84\n",
       "718      8041.62   6999.00  -1042.62        14.90\n",
       "484     13334.19  11600.00  -1734.19        14.95\n",
       "258     13680.48  11900.00  -1780.48        14.96\n",
       "616     13500.69  15900.00   2399.31        15.09\n",
       "281     11638.89  13708.50   2069.61        15.10\n",
       "476     15700.53  18500.00   2799.47        15.13\n",
       "716     11425.01  13500.00   2074.99        15.37\n",
       "529     53312.81  63000.00   9687.19        15.38\n",
       "481      6599.62   7800.00   1200.38        15.39\n",
       "706     61639.73  72900.00  11260.27        15.45\n",
       "558      7098.53   8400.00   1301.47        15.49\n",
       "151     10889.39  12900.00   2010.61        15.59\n",
       "663      2613.97   3099.00    485.03        15.65\n",
       "16      23721.58  20500.00  -3221.58        15.72\n",
       "356     10133.68   8750.00  -1383.68        15.81\n",
       "297     22261.50  19200.00  -3061.50        15.95\n",
       "119     10448.50   8999.00  -1449.50        16.11\n",
       "390     24271.90  20900.00  -3371.90        16.13\n",
       "439     14926.69  17800.00   2873.31        16.14\n",
       "441     11619.16  10000.00  -1619.16        16.19\n",
       "548     46465.02  39900.00  -6565.02        16.45\n",
       "460     50954.68  61000.00  10045.32        16.47\n",
       "211      6848.88   8200.00   1351.12        16.48\n",
       "106     16547.03  14200.00  -2347.03        16.53\n",
       "599      4590.16   5500.00    909.84        16.54\n",
       "220     13348.91  15999.00   2650.09        16.56\n",
       "226     11191.05   9600.00  -1591.05        16.57\n",
       "197      2126.49   2550.00    423.51        16.61\n",
       "3        7463.96   6400.00  -1063.96        16.62\n",
       "378      2749.08   3300.00    550.92        16.69\n",
       "507     18282.75  21950.00   3667.25        16.71\n",
       "186     19071.43  22900.00   3828.57        16.72\n",
       "100     32886.86  39500.00   6613.14        16.74\n",
       "620     25752.19  30990.00   5237.81        16.90\n",
       "229      7719.69   9300.00   1580.31        16.99\n",
       "400     25986.31  31310.00   5323.69        17.00\n",
       "336      8250.47   9950.00   1699.53        17.08\n",
       "483     28937.50  34900.00   5962.50        17.08\n",
       "171      8392.42   7150.00  -1242.42        17.38\n",
       "421      8033.61   9750.00   1716.39        17.60\n",
       "643      8156.11   9900.00   1743.89        17.62\n",
       "696      2470.61   3000.00    529.39        17.65\n",
       "286     19659.97  16700.00  -2959.97        17.72\n",
       "496     17540.72  14899.00  -2641.72        17.73\n",
       "655      7814.16   9500.00   1685.84        17.75\n",
       "407     21097.66  17900.00  -3197.66        17.86\n",
       "464      4266.27   5200.00    933.73        17.96\n",
       "758      9380.46   7950.00  -1430.46        17.99\n",
       "670     33614.62  41000.00   7385.38        18.01\n",
       "9       33614.62  41000.00   7385.38        18.01\n",
       "263      9089.35   7700.00  -1389.35        18.04\n",
       "521     27740.81  23500.00  -4240.81        18.05\n",
       "46       3542.72   3000.00   -542.72        18.09\n",
       "237     25037.87  21200.00  -3837.87        18.10\n",
       "28      35991.51  43999.00   8007.49        18.20\n",
       "125     13733.33  16800.00   3066.67        18.25\n",
       "721     11431.35  13999.00   2567.65        18.34\n",
       "749      6082.85   7450.00   1367.15        18.35\n",
       "55      12674.57  10700.00  -1974.57        18.45\n",
       "466     25975.12  21900.00  -4075.12        18.61\n",
       "651      3026.25   2550.00   -476.25        18.68\n",
       "560     33172.20  40800.00   7627.80        18.70\n",
       "247     13159.16  16200.00   3040.84        18.77\n",
       "738     20216.67  24900.00   4683.33        18.81\n",
       "715     10220.74   8600.00  -1620.74        18.85\n",
       "608     29127.72  24500.00  -4627.72        18.89\n",
       "509      5839.08   7200.00   1360.92        18.90\n",
       "435     53063.22  65500.00  12436.78        18.99\n",
       "187     28256.79  34900.00   6643.21        19.03\n",
       "689      4524.77   3800.00   -724.77        19.07\n",
       "51       2543.57   3149.25    605.68        19.23\n",
       "501     60493.51  75000.00  14506.49        19.34\n",
       "292      8962.29   7500.00  -1462.29        19.50\n",
       "193     14832.32  12400.00  -2432.32        19.62\n",
       "711     19676.51  24500.00   4823.49        19.69\n",
       "7       20349.62  16999.00  -3350.62        19.71\n",
       "569      1926.60   2400.00    473.40        19.72\n",
       "453      8381.60   6999.00  -1382.60        19.75\n",
       "444     38334.67  32000.00  -6334.67        19.80\n",
       "454     20972.54  17500.00  -3472.54        19.84\n",
       "176      6732.97   8400.00   1667.03        19.85\n",
       "240     10419.20  13000.00   2580.80        19.85\n",
       "688     24572.28  20500.00  -4072.28        19.86\n",
       "313     10014.71  12500.00   2485.29        19.88\n",
       "471      4209.17   3500.00   -709.17        20.26\n",
       "535     34678.95  43500.00   8821.05        20.28\n",
       "79      18893.76  15700.00  -3193.76        20.34\n",
       "409      8121.22  10200.00   2078.78        20.38\n",
       "154     11443.85   9499.00  -1944.85        20.47\n",
       "494     33392.40  42000.00   8607.60        20.49\n",
       "98       6746.48   8500.00   1753.52        20.63\n",
       "227     17251.60  14300.00  -2951.60        20.64\n",
       "115      6981.34   8800.00   1818.66        20.67\n",
       "744      2379.58   3000.00    620.42        20.68\n",
       "474     10932.17  13800.00   2867.83        20.78\n",
       "459      2779.04   2300.00   -479.04        20.83\n",
       "331     11002.01  13900.00   2897.99        20.85\n",
       "76       5144.02   6500.00   1355.98        20.86\n",
       "244      7110.32   8990.00   1879.68        20.91\n",
       "644     53312.81  67431.00  14118.19        20.94\n",
       "174      7745.02   6400.00  -1345.02        21.02\n",
       "358     63921.33  80999.00  17077.67        21.08\n",
       "425      3940.49   4999.00   1058.51        21.17\n",
       "321     23630.85  19500.00  -4130.85        21.18\n",
       "312     14792.29  18800.00   4007.71        21.32\n",
       "17      11770.64   9700.00  -2070.64        21.35\n",
       "517      9424.34  12000.00   2575.66        21.46\n",
       "392      8139.48   6700.00  -1439.48        21.48\n",
       "416     22367.52  28500.00   6132.48        21.52\n",
       "335     13004.34  10700.00  -2304.34        21.54\n",
       "45       8512.03   7000.00  -1512.03        21.60\n",
       "515     32121.20  41000.00   8878.80        21.66\n",
       "557     12523.73  16000.00   3476.27        21.73\n",
       "41      19845.36  16300.00  -3545.36        21.75\n",
       "13      13292.71  16999.00   3706.29        21.80\n",
       "691      8467.17   6950.00  -1517.17        21.83\n",
       "20      11876.82  15200.00   3323.18        21.86\n",
       "275     22547.55  18500.00  -4047.55        21.88\n",
       "120     50728.89  65000.00  14271.11        21.96\n",
       "647      2625.10   2150.00   -475.10        22.10\n",
       "524      5863.41   4800.00  -1063.41        22.15\n",
       "547      9175.06   7499.00  -1676.06        22.35\n",
       "747      8453.28  10900.00   2446.72        22.45\n",
       "295      5350.91   6900.00   1549.09        22.45\n",
       "80       6819.44   8800.00   1980.56        22.51\n",
       "661     10303.21  13300.00   2996.79        22.53\n",
       "537      9139.04  11800.00   2660.96        22.55\n",
       "626     18621.12  24100.00   5478.88        22.73\n",
       "576     15954.72  12999.00  -2955.72        22.74\n",
       "499     12270.01  15900.00   3629.99        22.83\n",
       "218     11490.44  14900.00   3409.56        22.88\n",
       "458     14136.01  11500.00  -2636.01        22.92\n",
       "138     11095.57   8999.00  -2096.57        23.30\n",
       "320     10350.25  13500.00   3149.75        23.33\n",
       "606     20680.40  27000.00   6319.60        23.41\n",
       "709     34446.96  27900.00  -6546.96        23.47\n",
       "75       4209.17   5500.00   1290.83        23.47\n",
       "155      3086.38   2499.00   -587.38        23.50\n",
       "602     16427.12  13300.00  -3127.12        23.51\n",
       "736     53312.81  43163.25 -10149.56        23.51\n",
       "495      3747.21   4900.00   1152.79        23.53\n",
       "399     14410.15  18932.55   4522.40        23.89\n",
       "771     13922.45  18300.00   4377.55        23.92\n",
       "662      9232.79   7450.00  -1782.79        23.93\n",
       "634     18165.16  23900.00   5734.84        24.00\n",
       "4       11353.49   9150.00  -2203.49        24.08\n",
       "580      6205.48   5000.00  -1205.48        24.11\n",
       "687     11004.05  14500.00   3495.95        24.11\n",
       "305     10505.98  13900.00   3394.02        24.42\n",
       "206     19920.63  16000.00  -3920.63        24.50\n",
       "659      3621.08   4799.00   1177.92        24.55\n",
       "178     17316.59  13900.00  -3416.59        24.58\n",
       "692      8471.55   6799.00  -1672.55        24.60\n",
       "479     36883.77  49000.00  12116.23        24.73\n",
       "209      8242.62   6600.00  -1642.62        24.89\n",
       "773     13491.16  10800.00  -2691.16        24.92\n",
       "594      3001.37   4000.00    998.63        24.97\n",
       "1        3499.26   2800.00   -699.26        24.97\n",
       "625     23485.45  18777.00  -4708.45        25.08\n",
       "370     10835.95  14500.00   3664.05        25.27\n",
       "83       6013.05   4800.00  -1213.05        25.27\n",
       "443      3735.04   4999.00   1263.96        25.28\n",
       "158     15623.39  20999.00   5375.61        25.60\n",
       "519      8667.12   6900.00  -1767.12        25.61\n",
       "177      8916.19  11999.00   3082.81        25.69\n",
       "361      8688.92  11700.00   3011.08        25.74\n",
       "482     12027.56  16200.00   4172.44        25.76\n",
       "705     12976.15  17500.00   4523.85        25.85\n",
       "135     44407.14  59999.00  15591.86        25.99\n",
       "264     31059.18  42000.00  10940.82        26.05\n",
       "372      8110.06  10990.00   2879.94        26.21\n",
       "672      5679.39   4500.00  -1179.39        26.21\n",
       "699      4255.62   5800.00   1544.38        26.63\n",
       "306      3164.66   2499.00   -665.66        26.64\n",
       "279      3154.48   4300.00   1145.52        26.64\n",
       "145      4307.68   3400.00   -907.68        26.70\n",
       "681      5195.69   4100.00  -1095.69        26.72\n",
       "175     34863.53  47600.00  12736.47        26.76\n",
       "440      8638.63  11800.00   3161.37        26.79\n",
       "683     32771.45  44800.00  12028.55        26.85\n",
       "592     28154.84  38500.00  10345.16        26.87\n",
       "429     67952.07  53500.00 -14452.07        27.01\n",
       "294     13471.84  10600.00  -2871.84        27.09\n",
       "623     23227.15  31900.00   8672.85        27.19\n",
       "107      7131.92   9800.00   2668.08        27.23\n",
       "86       8071.70  11100.00   3028.30        27.28\n",
       "37      26478.15  20800.00  -5678.15        27.30\n",
       "92       7116.68   9800.00   2683.32        27.38\n",
       "231      9312.21   7300.00  -2012.21        27.56\n",
       "62      10848.85   8500.00  -2348.85        27.63\n",
       "572     10597.72   8300.00  -2297.72        27.68\n",
       "568      7940.44  11000.00   3059.56        27.81\n",
       "202      5040.46   7000.00   1959.54        27.99\n",
       "327     10716.00  14900.00   4184.00        28.08\n",
       "310      9205.10  12800.00   3594.90        28.09\n",
       "461     31656.01  24700.00  -6956.01        28.16\n",
       "613     13853.36  10800.00  -3053.36        28.27\n",
       "323      7098.53   9900.00   2801.47        28.30\n",
       "752      8591.66  12000.00   3408.34        28.40\n",
       "348     12201.29   9500.00  -2701.29        28.43\n",
       "424     50074.56  69990.00  19915.44        28.45\n",
       "584     11432.63   8900.00  -2532.63        28.46\n",
       "99       6437.21   8999.00   2561.79        28.47\n",
       "149      8351.34   6500.00  -1851.34        28.48\n",
       "58      35336.33  49500.00  14163.67        28.61\n",
       "423     74232.55 103999.00  29766.45        28.62\n",
       "726     45664.45  35500.00 -10164.45        28.63\n",
       "614     19298.61  14999.00  -4299.61        28.67\n",
       "129     11580.54   9000.00  -2580.54        28.67\n",
       "303     23496.36  33000.00   9503.64        28.80\n",
       "21      31557.80  24500.00  -7057.80        28.81\n",
       "767     24323.48  18800.00  -5523.48        29.38\n",
       "192     18285.07  25900.00   7614.93        29.40\n",
       "700     25640.67  19800.00  -5840.67        29.50\n",
       "552      5983.44   8500.00   2516.56        29.61\n",
       "500      8973.16  12750.00   3776.84        29.62\n",
       "734     23336.06  33200.00   9863.94        29.71\n",
       "233      6110.94   8700.00   2589.06        29.76\n",
       "582      7724.17  11000.00   3275.83        29.78\n",
       "201     10094.58   7777.00  -2317.58        29.80\n",
       "296     21690.99  30900.00   9209.01        29.80\n",
       "523     10809.48  15400.00   4590.52        29.81\n",
       "357     11018.79  15700.00   4681.21        29.82\n",
       "190     13377.99  10300.00  -3077.99        29.88\n",
       "234     12731.26   9800.00  -2931.26        29.91\n",
       "695     11178.46   8600.00  -2578.46        29.98\n",
       "364     21681.00  30999.00   9318.00        30.06\n",
       "362     27293.13  39040.00  11746.87        30.09\n",
       "309      6510.80   5000.00  -1510.80        30.22\n",
       "146      8298.32  11900.00   3601.68        30.27\n",
       "373     10821.19   8299.00  -2522.19        30.39\n",
       "513      5346.97   4100.00  -1246.97        30.41\n",
       "12       5484.02   7900.00   2415.98        30.58\n",
       "352      5238.05   4000.00  -1238.05        30.95\n",
       "181     17597.10  25500.00   7902.90        30.99\n",
       "128      7727.53  11200.00   3472.47        31.00\n",
       "753      6358.55   4850.00  -1508.55        31.10\n",
       "671      2886.35   4200.00   1313.65        31.28\n",
       "764     14049.76  10700.00  -3349.76        31.31\n",
       "216     12974.89  18900.00   5925.11        31.35\n",
       "260      6858.89   9999.00   3140.11        31.40\n",
       "124     15691.29  22900.00   7208.71        31.48\n",
       "214      2393.34   3500.00   1106.66        31.62\n",
       "405      6425.00   9400.00   2975.00        31.65\n",
       "70      50206.83  73500.00  23293.17        31.69\n",
       "596     25685.67  19500.00  -6185.67        31.72\n",
       "351     25951.31  19692.08  -6259.23        31.79\n",
       "489     28910.96  42500.00  13589.04        31.97\n",
       "38       9919.19  14600.00   4680.81        32.06\n",
       "274      9113.05   6900.00  -2213.05        32.07\n",
       "374     24427.14  36000.00  11572.86        32.15\n",
       "302      4738.89   7000.00   2261.11        32.30\n",
       "148     25040.42  37000.00  11959.58        32.32\n",
       "164     18403.35  13893.75  -4509.60        32.46\n",
       "420     14576.33  11000.00  -3576.33        32.51\n",
       "555      6610.04   9800.00   3189.96        32.55\n",
       "505     10105.70  15000.00   4894.30        32.63\n",
       "250      5041.20   3800.00  -1241.20        32.66\n",
       "491      6503.62   4900.00  -1603.62        32.73\n",
       "426     13542.79  10200.00  -3342.79        32.77\n",
       "84       4364.08   6500.00   2135.92        32.86\n",
       "136     12006.66  17900.00   5893.34        32.92\n",
       "139      7712.65  11500.00   3787.35        32.93\n",
       "618     31251.89  23500.00  -7751.89        32.99\n",
       "702      7011.74  10500.00   3488.26        33.22\n",
       "728      5792.10   8700.00   2907.90        33.42\n",
       "751      8493.04  12800.00   4306.96        33.65\n",
       "498      7950.27  12000.00   4049.73        33.75\n",
       "605      8940.39  13500.00   4559.61        33.77\n",
       "678      4569.15   6900.00   2330.85        33.78\n",
       "205     13041.39  19700.00   6658.61        33.80\n",
       "265     20163.48  30500.00  10336.52        33.89\n",
       "403      2839.03   4300.00   1460.97        33.98\n",
       "291     20433.23  30999.00  10565.77        34.08\n",
       "74       1746.19   2650.00    903.81        34.11\n",
       "741     19855.31  14800.00  -5055.31        34.16\n",
       "170     19741.65  29999.00  10257.35        34.19\n",
       "203     65613.31  99999.00  34385.69        34.39\n",
       "289      7178.20  10950.00   3771.80        34.45\n",
       "90       9143.17   6800.00  -2343.17        34.46\n",
       "563      2285.24   3500.00   1214.76        34.71\n",
       "112     22835.48  16950.00  -5885.48        34.72\n",
       "189     10786.38   8000.00  -2786.38        34.83\n",
       "669     11477.47  17650.00   6172.53        34.97\n",
       "163     23972.56  36900.00  12927.44        35.03\n",
       "97       3648.96   2700.00   -948.96        35.15\n",
       "690     11706.93  18100.00   6393.07        35.32\n",
       "117      7178.20  11100.00   3921.80        35.33\n",
       "673     22213.53  34500.00  12286.47        35.61\n",
       "739     22213.53  34500.00  12286.47        35.61\n",
       "341     12897.92   9500.00  -3397.92        35.77\n",
       "159     12904.68   9500.00  -3404.68        35.84\n",
       "64       9611.80  15000.00   5388.20        35.92\n",
       "300     20236.85  31600.00  11363.15        35.96\n",
       "269      4151.02   6500.00   2348.98        36.14\n",
       "345     28951.20  45500.00  16548.80        36.37\n",
       "442     25951.31  18988.13  -6963.18        36.67\n",
       "475      5570.46   8800.00   3229.54        36.70\n",
       "355     22454.74  35500.00  13045.26        36.75\n",
       "246     41711.18  30500.00 -11211.18        36.76\n",
       "111      3152.62   4999.00   1846.38        36.93\n",
       "490      8902.52   6500.00  -2402.52        36.96\n",
       "748     48699.98  77500.00  28800.02        37.16\n",
       "457     16821.54  26800.00   9978.46        37.23\n",
       "104      5521.28   8800.00   3278.72        37.26\n",
       "743      5268.17   8400.00   3131.83        37.28\n",
       "469     65825.25 104999.00  39173.75        37.31\n",
       "32      12459.85  19900.00   7440.15        37.39\n",
       "666     65723.62 104999.00  39275.38        37.41\n",
       "401      9070.23  14500.00   5429.77        37.45\n",
       "586      9348.90   6800.00  -2548.90        37.48\n",
       "541      9369.66  15000.00   5630.34        37.54\n",
       "160     32954.19  52777.00  19822.81        37.56\n",
       "652     17195.57  12500.00  -4695.57        37.56\n",
       "583      3425.23   5500.00   2074.77        37.72\n",
       "589      3173.48   5100.00   1926.52        37.77\n",
       "735     10337.11   7500.00  -2837.11        37.83\n",
       "375     65742.57 105999.00  40256.43        37.98\n",
       "217      8661.95  13999.00   5337.05        38.12\n",
       "29      26062.23  42500.00  16437.77        38.68\n",
       "283      7630.64   5500.00  -2130.64        38.74\n",
       "418     11378.43  18600.00   7221.57        38.83\n",
       "137     66032.76 107999.00  41966.24        38.86\n",
       "567      7279.97  11950.00   4670.03        39.08\n",
       "73      18577.88  30500.00  11922.12        39.09\n",
       "488      4043.74   2900.00  -1143.74        39.44\n",
       "26       2178.94   3600.00   1421.06        39.47\n",
       "42      23286.27  38500.00  15213.73        39.52\n",
       "468     12558.90   8999.00  -3559.90        39.56\n",
       "241     18432.63  30500.00  12067.37        39.57\n",
       "325      9057.77  14999.00   5941.23        39.61\n",
       "675     13476.90  22500.00   9023.10        40.10\n",
       "737     37704.71  26900.00 -10804.71        40.17\n",
       "346     32945.80  23500.00  -9445.80        40.19\n",
       "445      7712.65   5500.00  -2212.65        40.23\n",
       "266     65742.57 109999.00  44256.43        40.23\n",
       "478     36764.38  62000.00  25235.62        40.70\n",
       "48      77403.06  55000.00 -22403.06        40.73\n",
       "712      8530.54  14500.00   5969.46        41.17\n",
       "587     15259.81  26000.00  10740.19        41.31\n",
       "431     16253.48  11500.00  -4753.48        41.33\n",
       "141      1815.28   3100.00   1284.72        41.44\n",
       "363     44193.38  75500.00  31306.62        41.47\n",
       "25      45272.25  31990.00 -13282.25        41.52\n",
       "230      9198.08  15800.00   6601.92        41.78\n",
       "34      19854.79  14000.00  -5854.79        41.82\n",
       "697     10031.96  17300.00   7268.04        42.01\n",
       "371      9661.78   6800.00  -2861.78        42.09\n",
       "628      2539.57   4400.00   1860.43        42.28\n",
       "388     11474.22  19999.00   8524.78        42.63\n",
       "316     65887.51 114999.00  49111.49        42.71\n",
       "199      2784.46   1950.00   -834.46        42.79\n",
       "514     15015.22  10500.00  -4515.22        43.00\n",
       "210     31216.83  55000.00  23783.17        43.24\n",
       "337      5374.74   3750.00  -1624.74        43.33\n",
       "10       7241.07  12800.00   5558.93        43.43\n",
       "646      3450.69   6100.00   2649.31        43.43\n",
       "39      16724.45  29600.00  12875.55        43.50\n",
       "322     65210.35 115800.00  50589.65        43.69\n",
       "645     10740.09  19100.00   8359.91        43.77\n",
       "413     12234.68   8500.00  -3734.68        43.94\n",
       "640     14402.34  10000.00  -4402.34        44.02\n",
       "88      10807.78   7500.00  -3307.78        44.10\n",
       "585     39074.36  69999.00  30924.64        44.18\n",
       "590     14512.61  25999.00  11486.39        44.18\n",
       "66       3316.44   2300.00  -1016.44        44.19\n",
       "588     12255.45  22000.00   9744.55        44.29\n",
       "539      6781.86   4700.00  -2081.86        44.29\n",
       "756      3752.54   2600.00  -1152.54        44.33\n",
       "428      2389.14   1650.00   -739.14        44.80\n",
       "285     34761.74  24000.00 -10761.74        44.84\n",
       "566     11881.05   8200.00  -3681.05        44.89\n",
       "510      1873.03   3400.00   1526.97        44.91\n",
       "167     13212.52  23999.00  10786.48        44.95\n",
       "511     17282.06  11900.00  -5382.06        45.23\n",
       "522     17011.43  11700.00  -5311.43        45.40\n",
       "349      2020.25   3700.00   1679.75        45.40\n",
       "684     18942.34  12999.00  -5943.34        45.72\n",
       "35      11337.51  20900.00   9562.49        45.75\n",
       "365     10547.82  19500.00   8952.18        45.91\n",
       "745      6421.18   4400.00  -2021.18        45.94\n",
       "334     12414.72  23000.00  10585.28        46.02\n",
       "255     24796.09  46000.00  21203.91        46.10\n",
       "717      4172.84   7750.00   3577.16        46.16\n",
       "191     15956.95  29999.00  14042.05        46.81\n",
       "575      1858.34   3500.00   1641.66        46.90\n",
       "169      8522.39   5800.00  -2722.39        46.94\n",
       "385     13235.55   8999.00  -4236.55        47.08\n",
       "393     13600.39  25800.00  12199.61        47.29\n",
       "654      4032.10   7650.00   3617.90        47.29\n",
       "262     12414.41   8420.00  -3994.41        47.44\n",
       "617      2573.48   4900.00   2326.52        47.48\n",
       "342     14572.70  27900.00  13327.30        47.77\n",
       "512     15144.28  29000.00  13855.72        47.78\n",
       "6       20159.19  38888.00  18728.81        48.16\n",
       "44       7241.07  14000.00   6758.93        48.28\n",
       "564     25682.58  17300.00  -8382.58        48.45\n",
       "658     10563.39  20500.00   9936.61        48.47\n",
       "304      6546.81   4400.00  -2146.81        48.79\n",
       "768     38260.36  75555.00  37294.64        49.36\n",
       "338     38260.36  75555.00  37294.64        49.36\n",
       "760     13443.32   9000.00  -4443.32        49.37\n",
       "179     14933.54  29500.00  14566.46        49.38\n",
       "486     13884.26  27800.00  13915.74        50.06\n",
       "604     31373.47  20859.15 -10514.32        50.41\n",
       "183     47014.52  95000.00  47985.48        50.51\n",
       "121     41075.73  27200.00 -13875.73        51.01\n",
       "759     10125.27   6700.00  -3425.27        51.12\n",
       "343     60603.06 124000.00  63396.94        51.13\n",
       "414     14363.76   9500.00  -4863.76        51.20\n",
       "391     25302.43  52000.00  26697.57        51.34\n",
       "249      7391.67  15200.00   7808.33        51.37\n",
       "353      9753.44  20400.00  10646.56        52.19\n",
       "243     32925.60  69500.00  36574.40        52.63\n",
       "33      22941.80  15000.00  -7941.80        52.95\n",
       "223      6199.59  13200.00   7000.41        53.03\n",
       "703      5597.06  11999.00   6401.94        53.35\n",
       "411     13409.47   8700.00  -4709.47        54.13\n",
       "288      6635.73   4300.00  -2335.73        54.32\n",
       "573     33707.27  73900.00  40192.73        54.39\n",
       "732     19309.90  12500.00  -6809.90        54.48\n",
       "456     19624.73  12700.00  -6924.73        54.53\n",
       "383     13757.58   8900.00  -4857.58        54.58\n",
       "544     12354.92  27500.00  15145.08        55.07\n",
       "530     17714.62  11400.00  -6314.62        55.39\n",
       "551     11555.80   7400.00  -4155.80        56.16\n",
       "248     22011.47  13999.00  -8012.47        57.24\n",
       "245     15457.91   9800.00  -5657.91        57.73\n",
       "339     11099.09   6999.00  -4100.09        58.58\n",
       "656     35039.40  85555.00  50515.60        59.04\n",
       "219     34376.16  85000.00  50623.84        59.56\n",
       "520     16773.62  10500.00  -6273.62        59.75\n",
       "772     27487.75  68500.00  41012.25        59.87\n",
       "701      7996.00   4999.00  -2997.00        59.95\n",
       "215      5284.06   3300.00  -1984.06        60.12\n",
       "447     34863.53  87777.00  52913.47        60.28\n",
       "180     19701.70  49999.00  30297.30        60.60\n",
       "722     30409.09  18900.00 -11509.09        60.89\n",
       "577     14486.38   9000.00  -5486.38        60.96\n",
       "733     11601.33   7200.00  -4401.33        61.13\n",
       "487     10824.89   6700.00  -4124.89        61.57\n",
       "408     65448.37  40500.00 -24948.37        61.60\n",
       "63       5351.86   3300.00  -2051.86        62.18\n",
       "679     25864.65  69990.00  44125.35        63.05\n",
       "228     15682.82   9600.00  -6082.82        63.36\n",
       "284     40765.33 112000.00  71234.67        63.60\n",
       "232      1320.56   3700.00   2379.44        64.31\n",
       "417      3795.06   2300.00  -1495.06        65.00\n",
       "122     19335.34  55555.00  36219.66        65.20\n",
       "238     26736.21  16100.00 -10636.21        66.06\n",
       "347     26441.03  15900.00 -10541.03        66.30\n",
       "276      5002.39   2999.00  -2003.39        66.80\n",
       "93      18317.20  10974.21  -7342.99        66.91\n",
       "436     40893.84 126000.00  85106.16        67.54\n",
       "621     38473.71 119000.00  80526.29        67.67\n",
       "221     34863.53 109999.00  75135.47        68.31\n",
       "455     10946.44   6500.00  -4446.44        68.41\n",
       "578      3205.38   1900.00  -1305.38        68.70\n",
       "624     16924.74   9999.00  -6925.74        69.26\n",
       "326      9356.43   5500.00  -3856.43        70.12\n",
       "477     15374.08   8900.00  -6474.08        72.74\n",
       "633     11056.52   6400.00  -4656.52        72.76\n",
       "69       2440.90   1400.00  -1040.90        74.35\n",
       "224      3939.91  15500.00  11560.09        74.58\n",
       "56      15380.04   8800.00  -6580.04        74.77\n",
       "463     35093.27  19990.00 -15103.27        75.55\n",
       "668      8270.38   4700.00  -3570.38        75.97\n",
       "225      6916.13   3900.00  -3016.13        77.34\n",
       "546      6746.48   3799.00  -2947.48        77.59\n",
       "27       2555.02  11600.00   9044.98        77.97\n",
       "746     13355.11   7500.00  -5855.11        78.07\n",
       "271      3948.74   2200.00  -1748.74        79.49\n",
       "236      9024.05   4900.00  -4124.05        84.16\n",
       "166     15292.10   8300.00  -6992.10        84.24\n",
       "67       7621.25   4100.00  -3521.25        85.88\n",
       "432      2975.40   1600.00  -1375.40        85.96\n",
       "550     14915.98   7900.00  -7015.98        88.81\n",
       "208      5716.14   3000.00  -2716.14        90.54\n",
       "116     15819.50   8300.00  -7519.50        90.60\n",
       "366     13420.15   6999.00  -6421.15        91.74\n",
       "686     23576.99  12000.00 -11576.99        96.47\n",
       "404      8648.80   4400.00  -4248.80        96.56\n",
       "30      15559.43   7900.00  -7659.43        96.95\n",
       "713      4004.65   2000.00  -2004.65       100.23\n",
       "611     27225.34  13500.00 -13725.34       101.67\n",
       "427      5462.52   2700.00  -2762.52       102.32\n",
       "694      8131.92   3999.00  -4132.92       103.35\n",
       "727     10810.62   5000.00  -5810.62       116.21\n",
       "397      7973.87   3650.00  -4323.87       118.46\n",
       "196      5948.04   2600.00  -3348.04       128.77\n",
       "704      6891.99   3000.00  -3891.99       129.73\n",
       "571     26331.41  11200.00 -15131.41       135.10\n",
       "680     21493.44   9000.00 -12493.44       138.82\n",
       "465      6537.82   2700.00  -3837.82       142.14\n",
       "81      12891.96   5300.00  -7591.96       143.24\n",
       "554      8340.94   3350.00  -4990.94       148.98\n",
       "609      3040.77   1200.00  -1840.77       153.40\n",
       "677      9631.08   3799.00  -5832.08       153.52\n",
       "161     13967.55   5500.00  -8467.55       153.96\n",
       "635      3818.71   1450.00  -2368.71       163.36\n",
       "660      7809.13   2899.00  -4910.13       169.37\n",
       "664      4590.49   1700.00  -2890.49       170.03\n",
       "82       7320.42   2600.00  -4720.42       181.55\n",
       "282     12261.19   4100.00  -8161.19       199.05\n",
       "2        7553.29   2500.00  -5053.29       202.13\n",
       "729      9817.06   3200.00  -6617.06       206.78\n",
       "492      7161.67   2200.00  -4961.67       225.53\n",
       "382      7918.89   2400.00  -5518.89       229.95\n",
       "502      9984.87   3000.00  -6984.87       232.83\n",
       "750     10381.62   3000.00  -7381.62       246.05\n",
       "434      8843.22   2500.00  -6343.22       253.73\n",
       "182     10123.04   2800.00  -7323.04       261.54\n",
       "396     16165.79   4400.00 -11765.79       267.40\n",
       "298     17937.36   4800.00 -13137.36       273.69\n",
       "629      7319.77   1850.00  -5469.77       295.66\n",
       "619     16095.32   3600.00 -12495.32       347.09\n",
       "0       10685.50   2300.00  -8385.50       364.59\n",
       "485      9664.46   1900.00  -7764.46       408.66\n",
       "657     32481.05   6000.00 -26481.05       441.35\n",
       "162      9954.42   1800.00  -8154.42       453.02\n",
       "451     35956.50   6500.00 -29456.50       453.18\n",
       "532     10019.90   1800.00  -8219.90       456.66\n",
       "639     30628.28   4999.00 -25629.28       512.69"
      ]
     },
     "execution_count": 130,
     "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": 155,
   "id": "fb13a8f6",
   "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>Predictions</th>\n",
       "      <th>Target</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Difference%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>774.00</td>\n",
       "      <td>774.00</td>\n",
       "      <td>774.00</td>\n",
       "      <td>774.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15946.76</td>\n",
       "      <td>18165.82</td>\n",
       "      <td>2219.06</td>\n",
       "      <td>36.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13133.20</td>\n",
       "      <td>19967.86</td>\n",
       "      <td>10871.22</td>\n",
       "      <td>55.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1320.56</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>-29456.50</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7413.64</td>\n",
       "      <td>6900.00</td>\n",
       "      <td>-2044.19</td>\n",
       "      <td>12.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11568.17</td>\n",
       "      <td>11600.00</td>\n",
       "      <td>142.52</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20162.41</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>3147.34</td>\n",
       "      <td>39.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77403.06</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>85106.16</td>\n",
       "      <td>512.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Predictions    Target  Residual  Difference%\n",
       "count       774.00    774.00    774.00       774.00\n",
       "mean      15946.76  18165.82   2219.06        36.26\n",
       "std       13133.20  19967.86  10871.22        55.07\n",
       "min        1320.56   1200.00 -29456.50         0.06\n",
       "25%        7413.64   6900.00  -2044.19        12.11\n",
       "50%       11568.17  11600.00    142.52        23.47\n",
       "75%       20162.41  20500.00   3147.34        39.56\n",
       "max       77403.06 126000.00  85106.16       512.69"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98f7af3",
   "metadata": {},
   "source": [
    "## Checking Accuracy with metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "65e8815c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of MODEL is 0.19\n",
      "RMSE of MODEL is 0.44\n",
      "MAE of MODEL is 0.32\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# Accuracy of the Training Model\n",
    "print(f\"MSE of MODEL is {round(mean_squared_error(y_train, y_hat),2)}\")\n",
    "print(f\"RMSE of MODEL is {round(mean_squared_error(y_train, y_hat, squared=False),2)}\")\n",
    "print(f\"MAE of MODEL is {round(mean_absolute_error(y_train, y_hat),2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "9b9b9913",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy of the Testing predictions\n",
    "pred = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "d86b965f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE of MODEL is 0.18\n",
      "RMSE of MODEL is 0.43\n",
      "MAE of MODEL is 0.32\n"
     ]
    }
   ],
   "source": [
    "print(f\"MSE of MODEL is {round(mean_squared_error(y_test, pred),2)}\")\n",
    "print(f\"RMSE of MODEL is {round(mean_squared_error(y_test, pred, squared=False),2)}\")\n",
    "print(f\"MAE of MODEL is {round(mean_absolute_error(y_test, pred),2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "fafb714c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.25669285671207\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(df_pf['Difference%']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a606fde",
   "metadata": {},
   "source": [
    "## Saving the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "a24336cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trained_model_v01.pkl']"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the model to disk\n",
    "import joblib\n",
    "\n",
    "joblib.dump(reg, 'trained_model_v01.pkl', compress=9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c809fcbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7726984972665857\n"
     ]
    }
   ],
   "source": [
    "# load the model from disk\n",
    "loaded_model = joblib.load('trained_model_v01.pkl')\n",
    "result = loaded_model.score(x_test, y_test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a18746e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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