udemy-ML/09-Feature-Engineering/.ipynb_checkpoints/00-Dealing-with-Outliers-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with Outliers\n",
    "\n",
    "In statistics, an outlier is a data point that differs significantly from other observations.An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. An outlier can cause serious problems in statistical analyses.\n",
    "\n",
    "Remember that even if a data point is an outlier, its still a data point! Carefully consider your data, its sources, and your goals whenver deciding to remove an outlier. Each case is different!\n",
    "\n",
    "## Lecture Goals\n",
    "* Understand different mathmatical definitions of outliers\n",
    "* Use Python tools to recognize outliers and remove them\n",
    "\n",
    "### Useful Links\n",
    "\n",
    "* [Wikipedia Article](https://en.wikipedia.org/wiki/Outlier)\n",
    "* [NIST Outlier Links](https://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm)\n",
    "\n",
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose a mean,standard deviation, and number of samples\n",
    "\n",
    "def create_ages(mu=50,sigma=13,num_samples=100,seed=42):\n",
    "\n",
    "    # Set a random seed in the same cell as the random call to get the same values as us\n",
    "    # We set seed to 42 (42 is an arbitrary choice from Hitchhiker's Guide to the Galaxy)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "    sample_ages = np.random.normal(loc=mu,scale=sigma,size=num_samples)\n",
    "    sample_ages = np.round(sample_ages,decimals=0)\n",
    "    \n",
    "    return sample_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = create_ages()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([56., 48., 58., 70., 47., 47., 71., 60., 44., 57., 44., 44., 53.,\n",
       "       25., 28., 43., 37., 54., 38., 32., 69., 47., 51., 31., 43., 51.,\n",
       "       35., 55., 42., 46., 42., 74., 50., 36., 61., 34., 53., 25., 33.,\n",
       "       53., 60., 52., 48., 46., 31., 41., 44., 64., 54., 27., 54., 45.,\n",
       "       41., 58., 63., 62., 39., 46., 54., 63., 44., 48., 36., 34., 61.,\n",
       "       68., 49., 63., 55., 42., 55., 70., 50., 70., 16., 61., 51., 46.,\n",
       "       51., 24., 47., 55., 69., 43., 39., 43., 62., 54., 43., 57., 51.,\n",
       "       63., 41., 46., 45., 31., 54., 53., 50., 47.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize and Describe the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(sample,bins=10,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100.00000\n",
       "mean      48.66000\n",
       "std       11.82039\n",
       "min       16.00000\n",
       "25%       42.00000\n",
       "50%       48.00000\n",
       "75%       55.25000\n",
       "max       74.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series(sample)\n",
    "ser.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trimming or Fixing Based Off Domain Knowledge\n",
    "\n",
    "If we know we're dealing with a dataset pertaining to voting age (18 years old in the USA), then it makes sense to either drop anything less than that OR fix values lower than 18 and push them up to 18."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     56.0\n",
       "1     48.0\n",
       "2     58.0\n",
       "3     70.0\n",
       "4     47.0\n",
       "      ... \n",
       "95    31.0\n",
       "96    54.0\n",
       "97    53.0\n",
       "98    50.0\n",
       "99    47.0\n",
       "Length: 99, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[ser > 18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It dropped one person\n",
    "len(ser[ser > 18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_values(age):\n",
    "    \n",
    "    if age < 18:\n",
    "        return 18\n",
    "    else:\n",
    "        return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     56.0\n",
       "1     48.0\n",
       "2     58.0\n",
       "3     70.0\n",
       "4     47.0\n",
       "      ... \n",
       "95    31.0\n",
       "96    54.0\n",
       "97    53.0\n",
       "98    50.0\n",
       "99    47.0\n",
       "Length: 100, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Fixes\" one person's age\n",
    "ser.apply(fix_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ser.apply(fix_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to identify and remove outliers:\n",
    "* Trimming based off a provided value\n",
    "* Capping based off IQR or STD\n",
    "* https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba\n",
    "* https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ames Data Set\n",
    "\n",
    "Let's explore any extreme outliers in our Ames Housing Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../DATA/Ames_Housing_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0  526301100           20        RL         141.0     31770   Pave   NaN   \n",
       "1  526350040           20        RH          80.0     11622   Pave   NaN   \n",
       "2  526351010           20        RL          81.0     14267   Pave   NaN   \n",
       "3  526353030           20        RL          93.0     11160   Pave   NaN   \n",
       "4  527105010           60        RL          74.0     13830   Pave   NaN   \n",
       "\n",
       "  Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence Misc Feature  \\\n",
       "0       IR1          Lvl    AllPub  ...         0     NaN    NaN          NaN   \n",
       "1       Reg          Lvl    AllPub  ...         0     NaN  MnPrv          NaN   \n",
       "2       IR1          Lvl    AllPub  ...         0     NaN    NaN         Gar2   \n",
       "3       Reg          Lvl    AllPub  ...         0     NaN    NaN          NaN   \n",
       "4       IR1          Lvl    AllPub  ...         0     NaN  MnPrv          NaN   \n",
       "\n",
       "  Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  SalePrice  \n",
       "0        0       5    2010        WD           Normal     215000  \n",
       "1        0       6    2010        WD           Normal     105000  \n",
       "2    12500       6    2010        WD           Normal     172000  \n",
       "3        0       4    2010        WD           Normal     244000  \n",
       "4        0       3    2010        WD           Normal     189900  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID               -0.246521\n",
       "Enclosed Porch    -0.128787\n",
       "Kitchen AbvGr     -0.119814\n",
       "Overall Cond      -0.101697\n",
       "MS SubClass       -0.085092\n",
       "Low Qual Fin SF   -0.037660\n",
       "Bsmt Half Bath    -0.035835\n",
       "Yr Sold           -0.030569\n",
       "Misc Val          -0.015691\n",
       "BsmtFin SF 2       0.005891\n",
       "3Ssn Porch         0.032225\n",
       "Mo Sold            0.035259\n",
       "Pool Area          0.068403\n",
       "Screen Porch       0.112151\n",
       "Bedroom AbvGr      0.143913\n",
       "Bsmt Unf SF        0.182855\n",
       "Lot Area           0.266549\n",
       "2nd Flr SF         0.269373\n",
       "Bsmt Full Bath     0.276050\n",
       "Half Bath          0.285056\n",
       "Open Porch SF      0.312951\n",
       "Wood Deck SF       0.327143\n",
       "Lot Frontage       0.357318\n",
       "BsmtFin SF 1       0.432914\n",
       "Fireplaces         0.474558\n",
       "TotRms AbvGrd      0.495474\n",
       "Mas Vnr Area       0.508285\n",
       "Garage Yr Blt      0.526965\n",
       "Year Remod/Add     0.532974\n",
       "Full Bath          0.545604\n",
       "Year Built         0.558426\n",
       "1st Flr SF         0.621676\n",
       "Total Bsmt SF      0.632280\n",
       "Garage Area        0.640401\n",
       "Garage Cars        0.647877\n",
       "Gr Liv Area        0.706780\n",
       "Overall Qual       0.799262\n",
       "SalePrice          1.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['SalePrice'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SalePrice'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Overall Qual', ylabel='SalePrice'>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Overall Qual',y='SalePrice',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>533350090</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24572</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Family</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>908154235</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>480</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>908154195</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>128.0</td>\n",
       "      <td>39290</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elev</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>183850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>908154205</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>40094</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>184750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "1182  533350090           60        RL           NaN     24572   Pave   NaN   \n",
       "1498  908154235           60        RL         313.0     63887   Pave   NaN   \n",
       "2180  908154195           20        RL         128.0     39290   Pave   NaN   \n",
       "2181  908154205           60        RL         130.0     40094   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC Fence  \\\n",
       "1182       IR1          Lvl    AllPub  ...         0     NaN   NaN   \n",
       "1498       IR3          Bnk    AllPub  ...       480      Gd   NaN   \n",
       "2180       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "2181       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "1182          NaN        0       6    2008        WD           Family   \n",
       "1498          NaN        0       1    2008        New         Partial   \n",
       "2180         Elev    17000      10    2007        New         Partial   \n",
       "2181          NaN        0      10    2007        New         Partial   \n",
       "\n",
       "      SalePrice  \n",
       "1182     150000  \n",
       "1498     160000  \n",
       "2180     183850  \n",
       "2181     184750  \n",
       "\n",
       "[4 rows x 81 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Overall Qual']>8) & (df['SalePrice']<200000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gr Liv Area', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4oqqe8uoGIpHYd/gtGByMvi7tpb+N5vBCCxjNYYnThtcly9fqp+z9NS01F2gbymq46x9bWDZtOGtuOZM+hVktXruqPsQ9r2xh6ojerFxfRlV9iIraIF/tbmwTIZNuDpYpTtOx0CYyzWGJteHFO50LcvyOIcJOT9mWaemZmSNoCsdwCwR8qZ/GncKPK+uC+DxuCnL8VNYGW7x2KBJlXGkxS9ZuZ+qI3tyycm/9s0VTSjnxqNx27SxvT/1tNIcPrY4iE5FjgL5KqX+KSADwKKVq07q6g4iOIjv8sHwaTeEo4WiMZW99wWn9uiVs3i1FOu1PZNS+jm0pbLm6MUxjOEIoothWWcfsFzalPepLozlYtBRF1ioBIyJXY9Ts6qyU6iMifYGFSqlRbbvUQ4cWMIcvX1U32M71QcV5zDijD12yfHTvlEH3ToGUT9mtDfG1BFksFiOqQCnl+ATf3ImfH/CytbIuITAgJ8PD+IVvJK1lzS1n0jM/sw1/KxrNwaEtwpSvBUYCewCUUluBwhZnaDRpJD5kWGGECIPhG5n+2HrGL3yDqCKl5lJZG6QhtLfasUVzn018OPSw/36ViYveYE9TxNE81NzZXt0YTgoM6Jzl26ezXNcW03QUWuuDCSqlQmZWvlX3S//Vaw4JTuaoueMHcNc/jIrFYGzaGd7k56d9VTtuvtl/l1IsVmCApVXlBbzsqg/xyOWncNnitxNMaZazXCc0ajoSrdVg/iUi/wUEROSHwNPAX9O3LI0mNU6b/k0rNnL9qL7A3ginrlnJAmBf1Y6bR0Z9l/wPn8fN6JJCbjy7H7Nf2MSFD6zjP5e9RzAS4/nrRrLmljN5dubIBOGhExo1HYnWajC3YnSP/ACYjtFV8i/pWpRG0xKpNv0+hdmsueXMFiOcUs098agcu8xL/LxUxStbk//RJcvHr88r4ZK/vJkgMKY/tp5nZ4509Lm0VUKjrhumaQ+0VoMJAA8rpSYopcYDD5tjGs1BJ1XSX8Dr3meyYaq5GWaI8o7djQl+j++S/+FyCW6XpBQYTr6Wtkho1CX8Ne2F1gqYVSQKlADwz7Zfjkazb/IDXhZOLk3Y9BdOLiU/4AVadpKnEhh1TRHHDTk+/8PJpNWc5tf2elyOAsPrcTkKgfyA9zsnNGozm6a90Now5feUUifva+xwRocpHz7sqg/yyc46umb7cIvwzZ4mFq/Zzh3nD6BLlm+fTvLm5iO3C35yb8vhyq0xOTk56B+9YijBSCxhbNHkUgpy/Fxw/9q0VGP+qrqBkXNeSxrXodCadNBSmHJrfTD1IjJYKfWuecJSoHEfczSa74xTbsmOmiZufPr9hCiwylrjmFRP78/MHEFhTgawN5zY4qvqhhb9Hq2N7HK69qUPv8Uz14zgmWtG0BCKsv3ben79fx9y23knprxm8/XtL9/Fb7Q/aD+PZl+0VsD8J/C0iFhth7sDF6ZlRRqNidPGvmhKKX9a9UlSFNjssf0BHMvol1c30hCMEstSjhvgvjbkVEJr2bThttCrbgzbeTULV2+zw6XLqxv5clcDhTl+Jj+019lfVR86YCGwr43dqYxOW9cN0+HUmtbQKgGjlHpbRE4A+gECbFZKhdO6Ms0Rj9PGPv2x9cwaU8LLmyrs48qrGzmmSybXPbGB60f1ddy4t39bT5bf46gZ7GtDThXZVV7dyEP//ozrRx3PjMeNKs6jSwqZN3EguxvDVNQG7cKW1hyLhau3MWfcAJas3c640mK6ZPkozPHbfqRUtHZj93tczB7bn0yfm4ZQFL+nbeva6lbNmtbQooARkbOUUq+KyAXNvuorIiilnknj2jRHOKk29uZP4kX5Ab6uaWRDWQ3zV23l/kmDuWbpu0mJlPdeMsjxOvsq5Gg56psLrZrGMONKi23hMqg4j6kjenPpw2/Z114waTCPv/EFo0q62eewEi+7Zvv4+ajjmf74vvvXWLRmY6+qD9lriF9vW27+uj+MpjXs67HmdPPnjx1eY1pzARFxi8gGEXnB/NxZRF4Rka3mz/y4Y38lIp+KyBYROTtuvFREPjC/my9mSQER8YvIMnP8TRHpFTdnqnmNrSIytTVr1bQvUoXsFppVk63Pc8YNYN7LnwBGqZi6YITZY/uzbNpwZo0p4e6XttiVj1ORqqdKLKaoa4ok9H4pyg8wb8JAFq7eRl7Aa2+0M87oYxfaBGPDnbn0XUaVdGPl+jIWTSlNSLz8vKrBFi7W8fuK9mrNxn4wNn/dH0bTGlrUYJRSt4uIC3hRKbX8AK/xc+BjINf8fCuwSil1p4jcan6+RURKgIuAk4AewD9F5HilVBS4H6PY5jqMJM9zgBcxkj+rlVLHichFwBzgQhHpDNwODMEoabNeRJ5XSlUf4D1o0oyTXyE/4GXRlNKEDpWLppSS4XOxfNpwIjGFiPDN7qaEcy1es92xs+WB+CAsbaAg28+sMSXkBbw0hKLElGJDWQ01jWFbM4kXNhaWxnX5yN7kZni4/ccn2YU5Ux3fkiBojQP/YDj5D4afR3P4s08fjFIqJiLXAfstYESkCDgPuAP4pTk8FjjDfL8EWA3cYo4/pZQKAttF5FNgqIh8DuQqpd4wz/ko8FMMATMW+K15rhXAvaZ2czbwilJqlznnFQyh9OT+3oPGmQOJIEo1p7lfYXRJIb8+r4QY0BCMcucF3yMnw0teppc7/raJytoQN5/Tz+4gGV+LrLIuyPWjjqdfYdv0LrG0Acv/Y7FixqkArFxfxsLJpcx4fH2CsLEoyg/QKeDlwdc/49zvdefYgr2NyVId35IgaM3GfjA2f90fRtMaWhtF9oqI3AgsA+qtQWsDb4H/BW4GcuLGuimldpjzd4iIVZW5J4aGYlFujoXN983HrTll5rkiIrIb6BI/7jDHRkSmYWhGHH300fu4FY3FgUQQtTQn3q9g+TGs8iqWOSov08skc2zRlNKk9sQ3rdjIY1cM5fMqI2LL43F9J3+DJQwBRyHQIy9gl6XJD3h5duZIYrFYksY1Z9wAHnz9M8YO6sktKzcya0yJfT7L0d+8f01LgqA1G/vB2vy/azi1puPTWgFzhfnz2rgxBRybaoKIjAEqlFLrReSMVlzD6a9ftTB+oHP2Dij1APAAGImWrVijhgOLIGppTrzPwPJjNDdJuV3YAqhPgXN74qr6EMWdDb/AV9UNSRtra7WueGFYkO1n7vgBCdrSg5cO4ajcDPu+dtY2mZ0tMyjIyeDZmSNpDEf5uqYRpRTTTj+Wsl2NFGT7E4TKhrIalqzdztKrhuFxSasFQWs2dr35a9oDrQ1T7n0A5x4J/EREfgRkALki8jiwU0S6m9pLd8CKNy0HiuPmFwFfm+NFDuPxc8rNFgKdgF3m+BnN5qw+gHvQOHAgTuSW5sT7DPICXgqy/dx4dr+EJ/uFk0uZ/h+9OK1fN8p2NabUKnY3hO0M+XgtCdhnR0pL+IgI97yyxTaN3fWPLcwe258+hdkEvG5bw0jVwRLA7xay/R47wiw+mu3ul7Ywa0wJJxyVg9ftojDb0LgOJTppUpMOWvyrFpFhIvK+iNSJyBsicmJrT6yU+pVSqkgp1QvDef+qUmoy8DxgRXVNBZ4z3z8PXGRGhvUG+gJvmea0WhEZbvpXLm02xzrXePMaCngJGC0i+WaU2mhzTNMGHEgEUcDnZvFlp7Bs2nAWTSllUHGePSc/4OWRy0/hqWnD6ZabwfyLB7Fk7fYEbWfG4+uZMqI3oUiMrtk+7rtkcEJU16LJpbgErn7MuQZXS/W5mheHnLjoDaaO6G03IttQVsPlj7yNW7C1gm/2NDme7+vdjZy/YA3vle+2hYv1vdUWYENZDbNf2ESmz0OPvEC7EC66OKYmHexLg7kPuBF4HfgJhk/l7JYmtII7geUiciXwJTABQCn1kYgsBzYBEeBaM4IM4BrgEYwimy+aL4CHgMfMgIBdGIIMpdQuEZkNvG0e9/tW+Is0rWR/ncixmGLnniCznvswwSnfLTcDl0uxs7aJxnDMsfxLfEZ8OBqzzzG6pJBHrxjK7sYwXbP9zH7hI678/rEtalapvvu2PujYI2bWmBLbsW8Jw0gkxte7G4nElGPWfkVtsMUIscIcP4svO4XeXbNQKLug5qFEJ01q0sW+BIxLKfWK+f5pEfnVgVxEKbUa00SllKoCRqU47g6MiLPm4+8A/R3GmzAFlMN3D2O0FdC0MfvrRE7VIOzJq4cz9t61zBpTwuwXNu1zg//82701w17eVMGmHbU8cvlQ9pgJj12yfSy+7BTmr9qa0NnS0qxSRWylKi9jCUxLgOYHvGypqE1y4sd3w7QCA1JFiHXO8vGzJze0q/IqOmlSky72pZvnicgF1svhs+YIJVViohOpN7AY8yYMTOm0j9/gF04uZf6qrUnHBCNGTsrsFzbxg/95nVnPfcjN5/SzTXCWZtVSXxe3iKPJr3unjIQS/dWNYVu4WNeP74a5aEopK9cbwYuWM795S4E7/rYpSVM41GX0ddKkJl3sS4P5F0bWvtNnBehSMZp9kirx78tdDVz+yNssvuyUpO9HlxRSmJvBv246A49LyPK7qawLJvS3bwhFCXjdCWVR4rUjn1sQl7BjdyM+j5u+BdmOWlfA506KFFswaTBZfjd5mXtNRE6CsiDbT0n3XJZeNYyA182t557Iph21doSYZcarqA0iQkINNWu9h1pT0EmTmnTRqn4wRwK6H0z6cMqBsRIjN5TVMKg4j9k/7Z9QMPK6s/oyM66e2IOXDiHb77YFiDX+6BVDOWvev5Ku+ffrv4+CpGx+J3NULKb4qqaBcFSxywwIWLm+jF/8sF9ClNk3e5r4usYIh164ehtAUsLng5cOoVuun7qmCB9/U5vgo1k0pTTBFAhtXyPsQKPBdBSZ5kBpqR9MaxuOdQP+CPRQSp1rlnU5VSn1UNsu9dChBUx6id/AAK57YoO98QLc8IO+/GRQTyprg3QKeLn8kbeTNuLl009l4qI3EsYXX3aK7fiPP/apacO5yCzJEj+eajOvqG3iggWpG4Bt+abWjlCzfC9ul9iBCc3nAJy/YE2SVuZUwqatfDC6hL7mUNCSgGltfOQjGGG+PczPn2D0iNFoWkW8z8bnMcxd8SxbX45CcVSnDHwel6NPJhKNJY3PX7U1qX3yoimleFyS0nHt1E45HNl77kHFeSyaUsq8CQONKLO6YFL48y0rN9K9U0bKa8T7fAYV57H4slP4rx+VUJDt5/nrWtd+eX/RrZI17Y3WZvJ3VUott6LIzLIsOsREc0A42fznTRjIjpomeuQF+Kyy3tFn43Enl82vrAsSU0bIsNVTpXtuBt/UBh3PEY0pW7OIf8K38nTyMr10CnhZ9tYXDO7VBbdL6JabQUG2P+Fc5dWNuMzgAKfINCvS7rnrRrCjJpiQcJkurUJHg2naG/vTMrkLZrkVERkO7E7bqjQdGmvzfWbmCBqCRhvhO1/cTGVdkMevHMb8VVuTanQtmlxKYbY/STDNHT+A25/7KCEs+ZlrRrCrPpjkuF80pZQ/mFFcVrBAfTDCzj1NNIajCTk2zX1A8T4j6zqC2mfTsPqmaFLCZWtyTA7EJ3KwWiVrNK2ltQLmlxhZ831EZA1QgJE5r9EcEC6X0DXLT40rxPHdsvnTxYPI8BoW28q6oF1OxYoW656XgcfjSsi/gWRfzohjuxCOxghHFdGYYu74AbhEaAgZZquXN1UwqDgvqRTN3PEDbC1lXGmxLVxgb2Ta7LH9ufyRtynKD3DPxIFU1gXJ9rsTOlo2L+JpJV7Gsy+t4kB9KToaTNPeaG0tsndF5HT2tkzeolsmaw6E+CfzmILqhhCZPjdulxCNQtesvVrK9MfW25pHOBLj65pG3AIul4vunYykxnhfzsTSIiafeozdb8Vyxt/1j81sKKvh9ZvOoCg/4NgY7KYVexM7U2XhH905k2XThuMSoVsnP7GYIhKDu/7xcZKG8szMEYQjMarqQym1ilRayoFm1usS+pr2xr5aJqdKpjxet0zWNGdfZh2nJ/MFkwbzwL8+Y+1nVdwzcSD1oWhCvko0pvjD3zbx8qYKW2AsWbudX/ywH30LshOe2Ked3ofLFifmxFgVAWa/sIld9SHmjBuAP0UQQV7Ay6DiPDpn+RyFwqeVdSxcvY0bz+7HJQ++mSDEmpe1aQhGyQ14WLm+LNncN6XUqAqQQkv5Lr4UXUVZ055oMUxZRBa3MFcppa5o4fvDCh2m/N1ojVmnsjaYFLpblB9g6VXD+LqmkcVrtnPF94+lT0E2BTl+dtUHeb9sN5k+NzWNYRau3kZlXdAWGFYIsSXUIjHF6XNXJ61txYxTUUrxx79vBuCu8QMcw6DvmXgyoWiMJWu3M3VE7wShYJWEmXFGH8dcluZlbWaP7c9JPXJpCkepDUbwuV00hKLkZ3rp0SlAdWPY8XeRKsS5rfNlNJq2oqUw5X21TL48PUvSdDRaY9ZJ9WReWRvkqE4ZzDzzOLpk+QhFouzc3UhlXSihQKa1yVsmrFAkmvDE/nWNcxn/7p0yqKgNMuOMPixcvY2bV2xM0ioWTi6lc6aXiaZ5rbI2ZEemde+Uwe/+agQStNQW2brevAkDufPFzfzPhScz5aE3EzSX7rkZVDeGaQg51z8LRaJ07xTQvhRNh6C1Tn5E5DzgJIzeLgAopX6fjkVpDj9aMuvEYopv64Mo5Vxwsqo+ROcsHwJ43S5+89yH3HT2CY7l7h+7cigel/DUtOFElaKyNmib4gqz/Xb7Ymtjvn9yKb/760cJJra7X9rCkrXbWXzZKeyqD1HTGGb+qk+47bwS+3obympsjeTVG07nhtHHc/HQYziqU4bjPXQKeHn1htP5oqoBv9dFQY6Pz7+tT1j/n/75iZ1oGd/ZMv488SHOB+pL0Vn5mvZCqwSMiCwEMoEzgb9gRJC9lcZ1aQ4zUoXIukT4eMcepj++noJsPwsmDU4I/7V8KrPGnES+uRFe+f1j2d0YdhRYdU0R6oKRxPDjyaUcX5iN2+2iuHMGS68aZlcEmPvSZrv+lyWkZo/tj8/j4uYVG22/yaDiPDwu57yWL6oaOLYgiyff+oSZZx6XFP48b8JA5r60mXGlxXZgwlPThjP/n0ZxzomlRVx92rF43S4iMcWFpUXkZni4f9JgrmlWDsfSUg7Ul6Kz+TXtidaWitmolBoQ9zMbeEYpNTr9Szw4aB/Md8NpY1s4uZTGUJRfLH/P3rQnlhZx7VnHUVkbtGt+TR3RmyVrt3P1fxxLUedMNu+opbhzgLJdjUml9xdfdoqj/2TpVcOIxhRfVDXYZrVl04Zz4QPrktb6+k1n2IEDYAiXm8/px+I12/nZWX0TNn1L47ntvBOpqg8x+4VNFGT7EwpuZvs9hKIxu2w/GH6fcDTGO9t3cfoJhbZQHV1SyM9GHc81psC9flRfenXNIsvvpmtWy1WpW0MqP5f232jSxQH7YOKw/lobRKQHRnOvA2mjrOmguFxC34Jslk8/lUg0htsl/O6vyU3AtlbU4XYJXXP8dMn2cfnI3gkJjNePOj6pMdld/9hCZV2Q+y4ZTFMkuVyM5cfJy/SRl7nXR9K8J8ug4jyuH9UXBdx6rtGc9eVNFVw/qi83rdhIQbafvEwvs8f2twML7n5pCwU5PjoFvHTO8lFe3UhBdlyF5WiMozr5uf7J9xIEoSWMnrx6OBc/uLcm2rjSYq4xTXjl1Y12Xs2zM0e2iYahs/k17YnWCpgXRCQPuAtYb479JS0r0hyWxGKKrZV1tgbzt+u/z7jSYgpz/PYmbyU4XtQsT8ViXGlxkt/lphWG3yUSVcx9aTO3nntiSj+O1+2iU8Brf2/1ZLllpSE8mlc+Xji5lFljSgCxtZLZL2xi6oje3GAWsbQ0jssfeZtZY0oYXVKYFGG2cHIpQ3sZlQG6ZPnonOVj4eptlFc3ElMqYa2pggTaSgDobH5Ne2JfeTCnAGVKqdnm52zgA2AzcM8+5mZgtFr2m9dZoZS6XUQ6A8uAXsDnwESlVLU551fAlUAUuF4p9ZI5Xsrelsl/B36ulFIi4gceBUqBKuBCpdTn5pypwK/N5fxBKbWkVb8RzQFhRZEVZPuZO34ASmGbkyyfxc3n9KMpbDQZs8KO4ztXdjE1hHjKqxtRigR/SXNHvuXHue28ElwCS68axh2mCczqyeIS4Y9/38SsMSX06JRBhtdNQyhKUzjG3Jc2c/M5/cjwunl5U4UdQZZnai2WSW7h6m3Mmzgwqf/MjMfXs/SqYUz6S2JuTE1jKMmvk6rTpdfT2rqzLTvxdTa/pj2xr7/qRUAIQEROA+40x3YDD+xjbhA4Syk1EDgZOMesYXYrsEop1RdYZX7GbAFwEUak2jnAAhGxHrvuB6YBfc3XOeb4lUC1Uuo4DIE3xzxXZ+B2YBgwFLhdRPL3sV7NdyAUiVKQ7efGsw0hYgmADWU13PWPLdwz8WRyMrzMeu5DLnxgHbNf2MSNZ/czzFKm1lFgajvxWIECFi9vqqBLtpelVw1jxYxTmTWmxM5bueNvm9i0o5ZJf3mTn53Vl7W3nsnFQ4/hhuXvU9sUZuqI3qxcX8aepgiXP/I2Y+9bw+WPvM3UEb1ZvGY7ORnGOjaU1bBw9TZqGsO4XcKsMSUMKs5jQ1lNyuCDyriSMHsTPE/C5YL746o9r1xflvDZMgPWNUXsys4tYfm6zl+whpFzXuP8BWvYsrPWnhsfgZaOis0azf6wLxOZWym1y3x/IfCAUmolsFJE3mtpojKiB+rMj17zpYCxwBnm+BJgNXCLOf6UUioIbBeRT4GhIvI5kKuUegNARB4Ffgq8aM75rXmuFcC9IiLA2cAr1tpF5BUMofTkPu5Xc4D4PG6uH9WXW1Zu5P5Jg20NIKaMmmBds31MeTg5y3722P70yAvwyOWnkOFxsWhKaVLP+z/+fRMzzuhjR2iFIoq6YISq+hB5AS/jSottB7vl87lm6bs8c80ICnP9hg8l08c1S99l1piSpDIxt6zcyOLLTsHrFpZeNYwn1n3Oaf26OSZaVqSo0ty8JH55dSMKxeYd9azbVsniy04xyuHEFGu2VvDI5UOpqgtS0xi2fUzPzBxBYU4GLdGafKPvms2vw5w1bcU+BYyIeJRSEWAUhhbR2rmYGsh64DjgPqXUmyLSTSm1A0AptUNECs3DewLxIT/l5ljYfN983JpTZp4rIiK7gS7x4w5z4tc3zbqno48+el+3o4mj+SaUH/DSu2sWBdl+YnHmsZvP6cetz3zAvAkDHZ/8e3fNYvYLe/NUFl82hDsv+B5et8t2sluCw3raz/K5CUVijhn1NY1h+9zhaIx+hTn8/AfHU10fssvBOK1jd2OY8QvfoCg/wONXDmOymSBpfW+Z8lauL0sKL140uZQ/rfok4ZyW5pXpc7Po/33Oov/3ecL3y6blJUW4NQSjxLJUi5t5up34OsxZ05bsS0g8CfxLRL7FiCT7fwAichytKNevlIoCJ5sBAs+KSP8WDnf661UtjB/onPj1PYBp6hsyZIjuHe2A09Ms4LgJFeb6uH5UX659wth8Z40psZ3qqXwPCkVlrfH0b0RVvcPssf2Z/NC6hOMKc/zMGlPCXf/Ywp8uHsSKd77k0SuGJrQ4njqiN3e/tMWe4/O42dUYSkhsTLWOpnCURVNKyQt4USjH/i8nHJXD1f/RB8CONGsIRfF6hJvPOYFNO2oTHP87appoCEUdr9cQ2isQrOi2mFLs2N2IxyW4XC5HzSHdTvwDLbSZCq0NHdnsq1TMHSKyCugOvKz2Js24gJ+19iJKqRoRWY1hptopIt1N7aU7UGEeVg4Ux00rAr42x4scxuPnlIuIB+iEEUJdzl4znDVndWvXqzFI9TTbJdtnj8X3VQmGvfTtlmWP9ynIsjeq+IiueLPTf//9Y9v8BcaGdmxBFqNLCm2tZt6Egfxy+fsAXD+qLxle4ScnF9nOdmtD/+t75WwoqzHWOcVwbJfXNCRElC1Zuz1pHfMmDMTvdXHrMx8khUfHhx773C6652U4tmK+e8JAlk83ap6JCL99/kMqa0Pc/pOSpMTMB6cMwesxnP9O0W3xBT2baw7pduK3pYaktSFNqxItD+jEIgVA2BQuAeBlDCf86UCVUupOEbkV6KyUullETgKewHDK98AIAOirlIqKyNsYAu1NjCiyPyul/i4i1wLfU0rNEJGLgAuUUhNNJ/96YLC5nHeB0jh/UhI60TKZVEl7T1w9jNPuWp3QV2XEsV2YdnofMn0uYgpCkRgxZZSyj09ovH5UX4o7B9hWWc/C1dvYUFaTkBBpFZ0szPWb/huIxqK8/OFOfjq4J9EYiMBnlfVJSZhPXj2cYCTKt3UheuZl4PcapjRLIFjCsCg/QG6Gl2/rgmT7PdQ0hBOSQa3zWf1fRpcUctt5JSgFCsUvl72f0IMGYNm04RTlB+iZn8lX1Q2MnPOafc83n9OPo3IzcLuEDJ+RUFnTGOL9st306pKZ4Juyrp2qoKdljqxuDKdFK2jLRE2d9Hlk0BaJlgdCd2CJ6YdxAcuVUi+IyBvAchG5EvgSmACglPpIRJYDm4AIcK1pYgO4hr1hyi+aL4CHgMfMgIBdGFFoKKV2ichs4G3zuN+3JFw0zqR6mnWbrYKtviojju3C5FOP4a5/fJyUI7JgkiHjX95UQWVdkAyvi5ue3pggGCxzkbWRR2PKFiCVdUHumTiQsYN72nkxzR3vG8pqjKfsaMwonJnrR0SoD0bwuFysmDGcj3fU8eIHO/C5XWT63Ozc08Qdf/sYgLsnOvuHijsH+MfPv08kRkIIspN2U2heMxZTCWasDWU1XPzgm0kba17Ax1GdMqgyfUPNrx1f0PNgagFtqSHppE9N2gSMUmojMMhhvAojYMBpzh3AHQ7j7wBJ/hulVBOmgHL47mHg4f1btSaeVPb+gM/Ng5cOoT5oVAS++rRjmfvSZm46+4SEMi7l1Y3MXPouiy87xXbSN4QidpOwovwA908aTFM4xl+vG4mCpFySu1/awi+Wv88jlw+1hYt17vgcmqL8AF9WNdAjz09dMMo1D7+dIOS++LaWKSN62Vn08effkaIK8ze7m+iRF+CqZtFvzbtbzh0/gF8ue5/KuiAPXjrE7lNzzytbUrZStsKJv9nT5Hhty1ckIm3qE9kXbdm0TCd9atKpwWgOc1I9zeYFfOT4POysM0J2M7wupo7onTJHZFd9iBuefp/ZY/szf9VWZo/tz9FdMhHgzhcNE9qiKaUJUWHNBYhLSDBz5QW81DSG6WFWN77vksH89vmPmDthINcsfStJyD1x9XD+8MJHdvh0TWPYTM48kUhM8diVQ/n82wZba5o73qgwsCuFhlHcOcCKGafSLTeD65/c27b56kffYfn0UynI9vHzUcczvVkr5T5dMqmsDxGJKTwuoWumL2Xi6IOXDsFt3nfz66dTC2irpmU66VOjBYwmJameZgF27GniD3/bZJZ6EVsYOD2xdsn2c98lg/jt85vYUFZjP/k/fuUw2z+TKnzYSsKMKVKWaXny6mH8edWnbCirsQVR8/OglGMTsaM6ZSSUrlk4uZSCbB/hWIxoDDZ/U+t4T2W7Gsnwuiivbkjwx5RXN/J1TaNdi6y55tE843/pVcOYv+oTZo0poTDHbxfO/O1P+nNUbkaLLZfbO7qFs6b19Sk0RyTW02zP/EwKcvx2z/iK2iAvb6rg7pe2EI3FEiK14rPU54wbwF3/+BiXCDeMPp5l04azaEopBdl+lFKMLilk0ZRSu2ZZPJZ/ZtGUUqKxKLeee2JSkuSMx9fzRVUDy9cbqVIxs+dM8/Mg4phg2RSOJZ2vNhilqi6E1y10zvRx/6TBCfd0/6TBZPs9dM7ysXjN9oRrjS4ppFPAS9/CbLsCgEV59d6M/0HFecwaY/ibxpUWs3D1Ns5fsJYf3vM6583/N0oZ+TCWFhB//cNJC3D6+9EcOWgNRrPfhCJR+8l6Q1kN4aiy39/90ha7E2SngJebV2wEoC4YSQoDDkWjXHdWX2YufTehZlm8NtE124fbJeyOS6C0sMxlPfKMbpEr15cRjUWTEiHnjBtAQ9DZfFfbFE4yu0VjMbNlsxFdVpDtZ/bY/hzTJROfx0VdU5j8rAwyPC5+/oPj7fyX0SWFXHdWX9sP1TwQwcr4n1haxKThx9j5Qk7HWRqK1gI0hzNpC1M+3NBhys44ZexX1AWprA2Sk+Hhzhc/5mdn9WVPU8SxtMqGspok/woYT+KPXjE0oXCkFcbc3D9TlB/g3ksGkZvhtY+PD5GOF0gC+DxChtdDTCk2f1PLwtXb7ErJzddwz8STCUVjSedp3sfGOv7OC77Hrc98wLJpw+mZn5nw+wG40CFHxgo5XjS5lOffK+fCocck3Hfz43SuiOZwoqUwZS1gTLSASaZ5otz0/+jFj08uSnBIL5g0mIIcP7c/9yHjSovtCsTxnSStPJfmmsL3euYy4s7Xkq77z1+exmWLk5uKzR0/AI9L+MXy9+3NuLlw6t01C6UU3+xpQkS40Sy77ySQ5k0YSLfcjISyMNa1HrtiKGfO+1fS2lbMOBWvWyjMySCmVIJG8WVVPafNXe14nx63izy/h28bQgQjMSr2BO2K0pYP5/WbziDg82gNRXNYcajyYDSHMbGYsUnXByPMHT8Ar9tFt9yMhOZZVoTW8mnD+fmo43n+vXLGDzkaj1u47bwSwMh/aQhFHR30iyaX2hn7FkX5Abxul6M5yyVCJKZsc1W8cGkuPOaOH0Bepof7J5dyzePr2VBWY5fuF4FPdtZx54ub+c2PSxyvFVXK0bneMz9AVV2ICYvecMxLSXWfAZ+bzgEf1fXhhMgyS9OrrAsS8Hl0AqKmQ6E1GBOtwewlXnOJL2Uyb8JAxxbEr95wOj6PUN0QScgzWTi5lPxML1/uaqBHXsCOnrJobiazBEOvrll8VW1EYq3atJNRJd1sn05DKMpvn/+Iu8YPsH0dqUxws8f256TuOewJRrEUgmy/m4ZQzNZaUs2984LvIXGBAdbajumcyUQHM9gzM0eAgsZw1PE+Z4/tT7+jcphoCqbm3xXm+jnxqNwEzUXX8dIcDmgNRrNfxBc8bE3Byi+qGji+W7YtXGBvRNayacNZvGY7t557oqOmsLsxnJCbctc/tnD3hIHc8bePKcjx2UEA1ia/fPpwbj6nH3Nf2sy9lwyiuj6coM3EnzvT56Y+HKO6PsQf//6xXRXg6XfKmTdhIDc8/T4LV29zDC5oDEV5+p0yO2ensjZIl2wfNSlyfRqCUf74903ckuI+M31uwlHnds/HFmTRs1MgSbjoOl6awx0tYDQJxGKKUCRqd50szNlbVXjh6m2OEVp3v7SF+yYNSugWWReMUFEbRARmjTmJTyvqnDPWG8J2oUtr7MtdDdz+kxJyMrxU1xvdJReu3gZAUziG1+3i0lN74RZh1nMfpsy/aQhF+fzbegBuPLufXRXAKLtfzpNXDyeqFH6PiyeuHkYkqviiqoFZ//chlXVB7p9USm7Aw57GMLsbwzz878+46ewTnLP+9zQxdURvvqxqSLkWr9uVsjKCp1lHy9ZUNdYajqa9owWMBjA2q5rGEDtqmmwfweiSQmaNOYkVM06lqj7EwtXbqAtG7FL1Vr+WghzDt7ByfRkzzzzOfmL3uV00hKKEIjHmr9qaVMX4/smlRGMxe9O1osUiUUVOhpcvq/Zm1i++bAiN4ViSOa0g2+9YqXnu+AEEfG5+9/wmfvPjEiJRxfyLBxGMRMn0uckLeLn4wXUUZPv5rx+dSPe8DC59ONG0dc3S9XYlAYuf/+D4pGstnFxKbVOYW1ZupCDb77iWbrkZFGb7HTPbu2Yl+132VcdLaziawwEtYDT2ZvXN7iZmPfehHXU1dURv26lvaSurN+/kvIE9E8xW/zNxIFMefou54wfQGIra57A216M7Z1JZF7RzZPICXlwi5Gd62dMYZtm04eyqD1EXjBAMx7jBjPyK15DKq/euDfbWBLMEgHXuvoXZZpBAA797fhMFOT4KcvwEIzE+rahj/qqtXD+qL7Oe+5CCbD//Pa4/oYjim91NKSsJWBTlB8jxe/jTPz+xc30Kc/xkZbhpqjQEQnl1Y8J99swPmALNt185Lfuq49XWfVs0mnSgM/k19maV6XPbG5ZVKbl55vuFQ4/hX5srePLq4fz1upEsuWIoLrNvffdOgaSClDet2IjLJSyYNJjKuiDTH1vPQ//+DL/XxUUPrONH8//NhQ+soykcpUdewBYuVqa73+PirvED6Jrta1EAbCirYfYLm9haUceu+hAXP/gmBTk+fnbW8Vz0wDpGzfsXs577kBvP7mef6+Zz+uFzu5m59F07cTSe+ErPlobQo1OAO84fQP8euRzTJYui/EzyA/6ESgQbymqY/th6bnj6ffweN52z9mawtzazfV8Z/LpSseZwQGswGnuzinfip6oN5nYJ40qLqG4I0xSOJvhjHr9qWEoHuAB3XvA9uucF8HtcCU27yqsb+cXy93nsyqEpc1YWTBrsGNIcLwCsIpG3//gkXr3hdGIKLlv8VpKQXHzZKRTlB+xaX+XVjY5mtkVTSjmqk581t5y5zz4sPToZx09/LLG45YGWdNmXtqMrFWsOB7SA0dib1Sc79rD0qmFU1gbpZBaZjN/ARpcUEorEqGwMU5Dj5/n3yu3vC7L9xGLOuSMI5Gd6aQpHmfrwW8yb4Nx/xdWsz0zzfJulVw1LaEs8b8JAYkrx6g2ns7WijiVrtzN1RG9qGsPsaQzTIy/geJ06M7cnqkgoedO8zE1uhocuWRmQtW+fh8fj4sSjctu0pEtLVY11pWLN4YAWMBq6ZPl49Iqh1AUjdg7H6JJCO0nR+ty8ztZ9lwzmrc9rACNK684XP3Z0gO+qCyECv1j+fovhzmW7Grhn4kBExFEwxJRKCDC488XNZgDAKfTolMFNZ59AQyhKboaXThkeu/ClU+RafpYXjwtWri+z12yZ2RZMGszclzZzx/kD7Hmt8Xm0VZn71qBrlGkOB7SA0eByCVl+T0J9LMsU9cjlQ3EJRGMqqZnYtU+8y50XfI/6UJQla7czrrSYTJ+bJVcMxecWPq2ot0N+l1wxNCHcubkgmjNuAM9t+IpLhh9Ntj9Ze7JK9vs8roQggPsnDWbFO19yWr9uCea6ueMH0DM/wD0TB9qCLT7H5XfPbwLg1nNP4KF/f2ZrLgU5fp5Y9zm/+GG/BG3AyedRkO0nFInyVXXDIdngD6ZA02gOBC1gNEQiMZrCyRvoy5squPXcE4nGVMpmYj3yAkRjsaTyKHPHD2D+qq12na34/BDLHDV7bH+O7pyJ1+Nid0OIq087lssfedsx1NeoiBxJMGN1y83gz6u22vOaBxcY2fPZttbTEIqSGzD+5CvrjLL5D/37M24990R2N4bJy/QS8Lq56rTjkoRFc5/HoOI8bj6nn13cUocJazTJpC2KTESKReQ1EflYRD4SkZ+b451F5BUR2Wr+zI+b8ysR+VREtojI2XHjpSLygfndfBERc9wvIsvM8TdFpFfcnKnmNbaKyNR03Wd7JhZTVNYG+aq6gcraILGYc1mgirogMbP2VjxF+QEEqGkwfC5O38cU5GZ4k3wmN63YyIwz+gDGZpzhdbFwcql9jsq6IBleFzEUf3jhI3587xpbiMX7Q5ZNG85jVwxlydrt1IeithkrHI3hdsHaz6pSdp3M9LmprA1R3DmTrtl+ijtnUl0folPAw+yx/Vk2bTjjSou5Yfn7/OzJDSgFe5rC5Ae8SUIiP+BNWP/1o/omRcxd/eg7fFsfbNXv/LvQ2n/Xg3UejSYVaatFJiLdge5KqXdFJAdYD/wUuAzYpZS6U0RuBfKVUreISAnwJDAU6AH8EzheKRUVkbeAnwPrgL8D85VSL4rITGCAUmqGiFwEnK+UulBEOgPvAEMAZV67VClVnWq9Ha0W2f4k4n1RVU9NQwgRSchvWTBpMI+/8QU1jSF+N7Y/VXUhx9a+1486nln/92FCZ0cwapTt3NNkNBt7+n0Ksv1cP6ovvbpm4XMLIhCJKSJRRTSmaAxH7etbFOUHuHvCQNwCPfMDxJRCKSGmFHVNEfY0hTkqN4MpDuXvZ4/tTyga4/hu2fxy2fvcPXEglbVBXvv4m6RcnvsuGczSdV+w9rMqlk8/lR55icK0oraJXz/7AZee2ovueQGUgh/8T3K15dU3nmHXOUuHVtNWCZY6UVPTVrRUiyxtGoxSaodS6l3zfS3wMdATGAssMQ9bgiF0MMefUkoFlVLbgU+BoaagylVKvaEMafhosznWuVYAo0zt5mzgFaXULlOovAKck657bY+kckpX1YeSjs3wuPC4hFhM8fhVw3jtxtOZO34Af3v/K3426jh+dlZfJix8g1n/9yGPXD6UFTNOZdaYEu5+aQsvb6pgxuPruX5U34RzFuUH2FpRR1Nc4qTVLnnKQ29S0xCmPhRlW0W93eXxqFyj6Vh87sfc8QPomZeB1+Pit89/xBdVjVz84DpOn7ua6Y8bGfb3m/XEms/Lz/Kycn0ZMWVoTAGvix55GQzu1YV7X93KY1cMZdm04cwaU8Jvn/+I5euNqLhINJZwL7GYoiEY5eVNFdSHjEi4bZV1jhrd9m/rW/U7P1C+rQ86/rt+Wx/cr/Psz9+HRnOgHBQfjGm6GgS8CXRTSu0AQwiJSKF5WE8MDcWi3BwLm++bj1tzysxzRURkN9AlftxhTvy6pgHTAI4++ugDv8F2iOWUbt6bJBaL2WVhGkNRIjFFhsco6RLvDH9o6hB+eFJ3w1FvZtCXVzdSVRdMqqhcXt1Ir66ZCSVfrAz8W889wdF8lZPh4auaRp586wvGlRbTJctHYzjGO9t3JRW/nDthANc9sYFZY0oSTHEF2X6awjGuPes43C7h4cuG4HG58LpduATKqhuZeeZxNIWjzB0/wPTBeOmS5ePlTRWMKy12rKTscSfXBdv+bT1F+QE7P8gpUGHh5FI6Z3n55y9P45vdTcx7+RM2lNW0afKjk6+svLqRpnAsxQxndKKm5mCQdgEjItnASuA/lVJ7TPeJ46EOY6qF8QOds3dAqQeAB8AwkaVa2OGIz+N27k0ypZSQWdX3phVG7ay5E/ZGWoGx0XxdY5RmaZ6zkirEeOeeIE9ePdzuIml1s0x1vNslLF6zPWl9CyYN5vbnPrLNbUX5Adxm2HJ88qdTMub9kwYT86iEemX3TDSaii1Z8zlrP6vi+etG2ln3TkLi/smlFGYnRmbFYjG8bmHJFUMRc03N82bys3zUNYWZuGiv03/ehIE89O/P2jT50W3mCiX9PvfTqqUTNVtPuouKduSipWktFSMiXgzhslQp9Yw5vNM0e1l+Gis1uxwojpteBHxtjhc5jCfMEREP0AnY1cK5jghiMYVCcdt5JYQiMQrMDbO8upHpj60nGFG2cLnx7H7UByNJT7NW2ZiaxjCjSwpZNKWUZdOGk5vh4Z6JAxPMUXPGDWDOi5vZuaeJzd/UMvuFTbaAWJjCfCUC40qLHRMqLXObdew3e5qM/BVTWIFzKZtrlr6Lx+VOGPvF8vf5oqqBsYN6UpDtpy5oFLtcNLnUro82e2x/XrvxdJZNG84JhdkJlY1jMcW39SFufeYDRs37F3e++DELJg22hczsFzaZHSqbuPaJDRRk+1k0pZR5EwbSEIryu5+c1KbJjwGf2/H3GfDtn2DYVykajYHlqzp/wRpGznmN8xesYcvO2jYLiEj3+Q81adNgTF/IQ8DHSqn/ifvqeWAqcKf587m48SdE5H8wnPx9gbdMJ3+tiAzHMLFdCvy52bneAMYDryqllIi8BPwxLkJtNPCrNN1qu8LJeWuZqzaU1ZgZ8zDi2C5cN6ovkWgMn8eVVIalIRSlKD/Aqk07k3qyLL78FO69eBDBSMyuqFxZF7QrLsdrBZV1Rh+VuycMRMzzds3xgzI2OSczTXHnACtmnEqXbB/f7G4i2+9hwaTB3Pvq3orMqUrZNH/wK69uRIAla7czd8JAQpEYH+/Yw7ufV/Hk1cNRKNwiBOIKUsZTVR+yy7/A3vygJ64eTsWeJqrqQ7Yp0BLYzbtZdstN9NV8F/ICRnh2fOh1t9wM8gL7Jxh0ombrSHdR0Y5etDSdJrKRwBTgAxF5zxz7LwzBslxErgS+BCYAKKU+EpHlwCYgAlyrlLIMwtcAjwAB4EXzBYYAe0xEPsXQXC4yz7VLRGYDb5vH/V4ptStN99mucPqDvWXl3qrDhhlEmHzqMVwSVyl5waTBgLGBFuUH6J7nZ+lVw4gpxZSHEut5Xb747aROlFai5Iwz+pCb4WHxZafgcbv4ZGctf3l9u92VsrhzJpW1TdS4XHTN8aXI6G8kw+vim91NXPzgm4BhEvvTRSeDwLJpw1E4Z+k3f/Aryg8Qjhp5OlZdMmu9s1/4iDvOH9Dif2QnX8XLmyq45ozjCEZitg+nIRTl+lF9k7Sq6Y+vb9PNwuUSenXJIifD+50Fg07U3Dfp9lV1dF9Y2gSMUurfOPtCAEalmHMHcIfD+DtAf4fxJkwB5fDdw8DDrV1vRyHVH2yeWVts0eRSXEhCOLBlmlp82Slc+f1jiSlFbWOEq5asT/LBWEEDAnbGfjCiePrtLxg7qGeS03vl+jJe3lTB2s+qmDNuADc9/T63nnsCMaW4/7Vt3HfJYK594t2EOXkBD2XVjWT79/55FuQY3SRnLn3X7uGycHJpQtj0wsmleN17BY8lSFxCUs6KJXTj+6s42cFT+SoqaoMsXL2N2WP7c0yXTDK8LprCzh0r23qz0ILh4JFuX1VH94XpTP4ORqo/2J75AZZeNYw7/rapxfbFFz6wjsWXnWJHjoWjexuCOTrWJ5dyVK6fS4b3SuhFX15ttEx+5PKhttCKxhS3nXcinQJevq0LsvazKrZW1DFrTAmFOX46Bbzc+eLHtha1YNJgBhXnUVkX5L9+VMLkh960zVC/WP4eBdl+e4P/uqaR+as+4bqz+rJocimZfg879zQx58XN3Hae8/12yfLh87hbzAmxEizjBdnc8QO46x+GWbBrto9sv4fOmT4q64IderM4Ekl3UdGOXrQ0bYmWhxsdJdEyEomxZWet3ZXSSiL0uMQee/WG0xPqjoFRKfk3Pz6JUMRoSXz9kxsAuP0nJTSGonZzL6ewXsMcJnyys46Fq7clJFyuuuF05rz4cVK02LwJA8nwurj2iQ2UVzcmCLX4cz9x9TAEI7Hy9LmrWTSl1HEN8SbApVcN47WPv+G0ft3Y3Rgm2+9h7kubGVdabIc/r1xfxq/PK6EoP5Oq+hDnL1iTdM5nZ44E4LZnNzKutJjCHD/5mT4j610pGkJRjumSSa8uWbjMPCKdvNjx0FFkLdNSoqXWYDoQsZjiy+oGvB6xy+5X1Ye477WtXD/qeP588ck0hWNk+dw8esVQdtWHqKoP8e7nVYw5ucju0WI9pQNcZ0ZGWd0inTSBhlCUb/Y00SXLx7yJA1m4ehvL15cbOSUu4Tc/Pimp/8sNT7/PU9OGs2hyKTkZHpQ53vzcoUiMqroQPfICjC4ppE9BVkoToPW+sjZI9/wsLn34LWaP7c+Tb36ZFKhw/+RSuudm4HJJSrNiLBYjGlPcfM4JuEX4Zk8TC177lFEl3TjxqByyMzxEYooduxvtjaFvQTZPTz+VcDSGyyUEfLqn3+FOuk2Sh9LkmW7hpgXMYUL8H0LA5yYSU4QjMbsR1q7GEJFojIZQlMZQlLpgE5lm6GplrVHiZfbY/hR3DvBtXGSU5buYv+qTBAFw04qNdgOwEcd24diuWXjdLkcTUE6Gh2uf2JSgMdU0hvjZqOOpaQiRl+kcLfbN7ib8Xjc79wSJKudeMl63kQQa8Lq47qy+lO1qdC7B3xi234ejMY4vzGbehIH0zAvQ9wfHcdEDiea7ax5fzzMzRyBNEXtec42u+e9p7vgBXDL8aBa89il3jhvAzj3BJG0l2++2f39ai9G0Zw6Gxq0frw4D4mPl//TPrZRXN3LBgrV23PzmnbVU1gapD0b563vlZHhdzHruQy58YB2zX9jEjWf3oyDbT6bPTdmuxoSwW8tXMq60OOGaRsivMP0/ejH51GO4/JG3uf7JDUk5GPddMpg7X/w44XzXPvEuvxlzEtl+I4x2x+4mx7IqVfUh/rzqE7rnZdAt189jVw5ldEmh/f3c8QMIR2Oc0D2bBrNO2fxVRqhy/BrunzSYVZt2UpQf4N5LBuFxCVMefosLH1jHRQ+uo7o+bOcCxd9fQzDK+QvWcN0Tyff16/NKkn5PN63YSHV9mN/95CSawjHqgxFmjSlhYmkRs8aUUB+MAEYCafOw0/0pwaKLUGoOBgejXJDWYA4DrD+Egmw/M87ok+A/sQSE5R9x0kZuWWmUrq9pDKfMH2nuVLTMW5NP7c3FZjhzeXUjd/3DSEzs3TWLcDRGTCk7NyS+LA0CGV4XWX4PAa+LueMHsHjNdrssTJdsH6FIlJ+d1TfBNLdg0mB+dlZfvt7dZDvSrXBpaw1WBn1ewEthrp9va0Ncc2YfRpV0oykc48anE6sSXLP0XWaP7c/lj7ydcH8KRUG2nw1lNfZ99SnMJuB1pzSbdc32sas+nODjsnJ0rOCEOeMGUFkbsn1R+xNJpv04moPFwQiR1hrMYYD1hzDjjD4pa1FZgsPSRgYV59nZ97PGlHBSzxyOK8ymR17AUZuIL8dv+Sg8LrE3dgurYGVMKW5esZFMn4ei/IAdYTb7hU2G5vDAOiprQwTDMS5/5B2effcrrj2zL7Nf2MT4hW8w5aG3yPB67CZh1n3MXPouX+9uYvpj6+3E0MraID7TPGetYfpj67nh6ff5ZGcdMaVwiXDCUTkU5vgdfz/HdMlMyn6PRGPMv/hkJpYW2fflFiOyR8ySLM1/TwGfxxYu8Wu2NEBLoFutCqx5rY0k00UoNQcLK+I0nraOetQazGGA9YfQo1MGnQLO3R4tH4T11OsUTvznVZ9QWRti7vgBCT6CBZMG88S6zxOKTP551SdcPPQYQmaYsqU95QW8NISiuF1CZV0Ql8CccQMIRWKOZV+WXjWMWWNKOOGonKQwZquKcjzl1Y30Lcxm0ZRSFq7eZlcI6JGXkZQzY7ULuHjoMXxWWU+G10X3TgHH34/f47Kz362Q6d2NEURcXPkfvdlaUWdUXPa52bKzlnte2ZJUp2zu+AF43M7tnK0gA+uzpRHub9hpR0+807QfDkaItBYwhwFdsnw8efUwdjdG+N1fP3Ls9nj3S1sAY0PLzfAm+Q+uMc1o0x9bb5uDji3IYsfuJrpm+1j0/z6H//d5wnWv/P6xzH9xK/deMsgOVbauuWhKKU/POJWvqhtZsnY7N5/jXDUZ4N3PqzjOIQKtqj7kKAy2VtQx+4VN3DNxIEd1ymBXfZhoDDZ8UcVjVwylyox+W7J2O5eP7E3A5+Z3z2+isi7IgkmDWTS5NMGEtdDUxuav2grAjWf349ZnPkgQHP/1oxPtqDDrP1xlbcguZtm9U4YdXNGSgLc+d++Uwes3n0mG10XXLH+rzVsdPfFO0344GOWCdB6MSXvNg4nFFNUNQeqCUVsDmFhaxHWj+qKUwu8xSqoEI0YEWa+umXjdLr6qNgpVxuelPDtzBOcvWGufe+2tZ7L92wZCkRiznvswSUvJ8Lq4+ME3efzKofaGbGHlv9y8YiN/+OlJ+L0eahpCdj2yDWU1FOUH7ETIL6oakvJcRpcUcu2ZfRO0kvsuGcxvn/8oYf7lj7xta2EvvFfOfxxfSPdOAXweF3uawvz62b3Nzqy8m0tP7UWPvABfVDUwf9VWKuuCduh1fFa/dS9PTRuOz+NCxRTD/vvVpH+HNbecSc/8TCKRGJt31iYkXlraoeWD+S4+E+2D0Rxu6DyYw5RYTLHlm1rqghHys3xcWFrE+FOKqaoLccmD6yjI9nPzOf3sDXN0SSE/O6uv7deI124q64IJpVesul23rDSqKjtpKfMmDGRQcR5et8tRO6kLRijI8eH3uinb1UCmz43P7eL2n5Sw4LVPmTqiN3e/tIXbzjuR+au2Jpm4po7ozd83fsXiy05hV32ImsYwLiHBOW6FWltamHXsp5V1rFxfZvs+Fk0ppUuWD6WMsOz6UDQpmTQ+9Lr5vXxl5uYsmlzKP395GkpBXTBCRW2QlevLbA2iujHM/FWfJJgTX3ivnN/+pD+3/1h956dAXYRS05HQAqYdU1Uf4p5/buFnZ/UFFGec2I3NO2ptTeDOC76X8DQ+rrQ4yWluRZD5PC5CZqfG+BBgKzKrrimSoKWUmxvurDEldmXl5k/9Xc0EzPLqRntN1rlvO6+E/3zqPTaU1VBVHzKiwdZ9kZDguWSt0Q/m5hUbbY1l1piShGvEm57KqxvZVR9KaHh27Zl9k/xNc8YNsNsNxFNe3Ziyn0pNY5jyaqM45aNXDOXSuMKYCyeXkm/6WEIRo7NlfOVpgEtH9KZnfuYB/1vHo2uNaToKOoqsHROKRLn01F5cs/Rd/B431zy+3t44BxXn0T0vcaNMFYJc3DnAkrXb8XvcLJs2nNlj+9sbmBVFkkpLOeGoHLL9HuZNSOwBs2DSYMqrG4jGkgtJ3rRiI7GYsjWRlevLuO+Swaz9rIoblr9PbVOEvoXZ/NePSliydrstXO43i2Na15g7fgALV2+z1+Pk6+ia7UsKLrhl5Uay/R7HCBmv28WDUxL7oMwZt/c6lhBrHgZebV73YETeaDQdBa3BtEOMJldBFHBUJ0OIWOHCVtOtGWf04cuqhoSn8VTdI8t2NfKzUcdTWdtEt9wMfB4Xv//rR+QFfCyYNJiZS99NOTcWU0xY9AaDivMSzEKCIZTAucSLlRtYlB/g56OOpyDHxyOXD6W2ycjF+eXy9wGjcdiV3z+WmsYwXbO93P7jk5h2Wh86Z/mobQpTWRe0z2Plm1if759camseza9vtUlubvLbVR8iL9PL0quG4XYJO2qa+OPfP07ooNk8JDg+iqujFyfUaNoSLWDaEbGYoqYxxI6aJqY/vp6CbD/zLx5EUb7RYrgo32jzu3ByKdGY4rfPJ0aUrVxfxv2TS7km3gE9aTBN4Rh1TWFuWrGRxZedQjRmJEcumlLKva9uZdaYEnp0yrCFTbzD3e0WFl92CvNXbWX6Y+sBwzl/8zknkhsQPC5nk1PA62LFjFOpqg/x/HvljBti+I6sopmVdUaIsnXOovwAd08YSEGOn/EL3+DVG06nMDeDx68cxrdmqPLjb3zBuNJipp3Wh6M6ZfD7v37EuNJix+tn+z00hWMsn34qX9cYAqdTpofqZkmS908aTEHO3pDieCEWfz5LQ9E+Eo2m9egoMpNDHUVmRQ99s7spIdpq3a/OYvu39RTk+GkKx5jx+Hoeu+IURFx2+fr4yK/OWV521Yc5unOm7QifOqK37e9YsnY7t//4JC58YB3zJgxM8Gc8efUwmsIxenXNRCkSSudbJeoLcnwJhSOdAgvumTgQECYsegPAroBsrbVHpwwQSRCEc8cPoGu2jwdf387az6p4/MphPPnm50wcegyj5v3LXqNVLaDfUTls+aaWVZt2JvWhsfJjfvHDfnTO9DLsv19l0ZRSfG6XY8XmxZedQl0wQtdsP39etTXpfIsml3Ji91wtRDQaB3QU2WFAVX2Ie17Zwq/OPZF5EwbaIcZ7msJ2JJXfayQLetxuZr+wV3uxytQvmDSY+mCETJ8bv9fF93rmclKPk9jdGGZcabEtZB5du50FkwZTVZeYh5LhdXPxg2/yyi9O4/JH3rb7yxfm+MnJ8HLfpEHEFAmVkS1n9yOXD6WqLkhDKEpeppebV3xg35vlGyqvbmTh6m3MOKMPhTl+npo2HJdATEFMKe5d9SlrP6ti0eRSIrEYU0ceSzQu78SpH43VSTM+X8XjEu44fwD5AS8VZo+W+GrL8cQHDjx+5dCEHjWW0D4qr/V5LBqNZi9awLQTYjGjre+UZm2Is/0ewpEYAa+bC82N/dmZI3h5U4WdCJgX8BKOxgh4XcxcmthRsluuz840H1dazN0vbWFDWQ1jBvYkw+vi/kmDucbsEmlVCagLRhz7y88ZN4C8zORAgpc3VXDLOSdSmJtBTUMQt8vFbeedaOfEWP4dp3POHT+AY7pkEo4opp1+LBeUFhGKRvn1/33Iny8ZhM8tduLkjDP6ODr0rTpsD146hO6dAgm9WayM/IaQ4UNxMqdZ3z36xud2czFLaM8dP4DdDRHyA0oLGY1mP0mbiUxEHgbGABVKqf7mWGdgGdAL+ByYqJSqNr/7FXAlEAWuV0q9ZI6XAo8AAeDvwM+VUkpE/MCjQClQBVyolPrcnDMV+LW5lD8opZbsa72H2kT2dU0jExe9YT+pzzijD12yfBzVKYP5/9zKtWcdx+lzVwM4Nt1K1bDL6mIZH1ZrhQNPf2w9g4rz+N+LTsYlwuwXPmLqiN6EIkY4cypz0uWPvO04vuKdLzlvYM8EP86ccQN4fctOzhvYk6q6kOM5H7tyKGfe/S/bcR+LxQhFYvxi+fu2Ge6284zwZet3EM/rN51BwOdJ8IVU1gbtJmKDivO4+Zx+dM32821dMMHxv3ByKUX5GdQHDX+Ky6XYWLaHTJ/b1iIr64I8O3OkDh3WaBxoyUSWzjDlR4Bzmo3dCqxSSvUFVpmfEZES4CLgJHPOAhGx4j7vB6YBfc2Xdc4rgWql1HHAPcAc81ydgduBYcBQ4HYRyU/D/bUpyowSiy8aOX7hG1z0wDqmjjgG2BtSvHD1Nu6fNDgh1LZX10xH809lbZBbzz0xZVhuZV2QcFSxc08TL2+q4O6XtpDhdaU8X10wklQuf864Acx9aTMXDj3GFi7W8bes3MilI3pzVKcMeqdoFuYW4bUbT2f22P785v8+ZFd92BYuYGhIk/7yJuGocgwR9rhdFOQkmrHia3ptKKvh4gff5Nu6IB6Xi8euGMo/f3kad17wPeav+oRwFHrmZxp+rlCMyx95mwsfWJdQcFPXAtNo9p+0mciUUq+LSK9mw2OBM8z3S4DVwC3m+FNKqSCwXUQ+BYaKyOdArlLqDQAReRT4KfCiOee35rlWAPeKiABnA68opXaZc17BEEpPtvU9thWxmEJEWDHjVPKzfMxp1l+lojbEn8w+KLesNJISm8Ixu3hj5ywfO/c494Ovqg/h97qZO34AR3fORAHhaIy7Jw6kKRwl2+9B2FsXzNqMH79yqOP5rMz2ZdOGs2N3E1X1IdvsNu20Po4CpKI2yO//uon7LhnkeE6XS7gkriFYqiTJxlAkKfR4zrgBuB0sV041vbxuF+MXvpF07O0/jib9OzQveaPzXDSa/edg+2C6KaV2ACildohIoTneE1gXd1y5ORY23zcft+aUmeeKiMhuoEv8uMOcBERkGoZ2xNFHH33gd3WAxGKKPU0hvqpuSgidnTNuAHkBH6NKupEX8NIl25fkc4nEYnTN8XPN4+uZN2Egc17cnLLa8G3nlZDtd/N1TaOtGVh5IX/65yf86kcnsnJ9WULI86NvfJ4U8myd77qz+hKKRJM261TFKytqg1TWBfF49vp84sOEG0MRuy1zXsBL5ywfo0sKk8x6X+9uYuHqbTx2xVAqaoPUNIZZsnY7d5w/IOl365SvUmi2JGi+Pp/H7VgDLD4aTee5aDT7T1rDlE0N5oU4H0yNUiov7vtqpVS+iNwHvKGUetwcfwjD3/Il8N9KqR+Y4/8B3KyU+rGIfAScrZQqN7/bhmESuwLwK6X+YI7PAhqUUvNaWuvB8ME0b3tc02BkhzevmVWUHzDKlTz8FgXZfuZOGOhYSHLFjFOJmBmNFz2wjhHHdmHGGX3sUizxIcq/+tGJTHko+TpLrxpmV1S+6x+b7YZgXbP9rN78DSP7FtIQipLpc9MUjvL17iZWri/jtvNK+KyyPsFX0TyE2RJiD/37M37+g+PpmuXj4X9/xhTTzxONKR58/TPOH2zI/+YtBJo38bJqqsUXwGypEGTzfuP5AS9bK+scC0lW1Ydsn03872f59FM5KjdDO/g1mhS0pzDlnSLS3dReugPWI2o5EN+ztwj42hwvchiPn1MuIh6gE7DLHD+j2ZzVbXsb+0/zJ+TFl50CQOcs5371tU17I7kuW/xW0lP1z87qy+J/f8bgXl3o1SXTjrTaWlHH9aP6clxhNpee2ss2X912XonjdcJRowPkwsml/H5sf6rMMil+j4vfvrCZZdPyEnJlwMhF2dMYTqo/FvC5efyNL3jsyqFU7DFClrvnZfC7sf0JR2NEYoqpI3sTiakEoTrxlGL+c9l7CWbBmUvfZdm04dx67ol8UdVgC5f/vfBklILXbz6TgLflJEenml6pkiRT9WFRSkePaTQHysGuRfY8MNV8PxV4Lm78IhHxi0hvDGf+W6Y5rVZEhpv+lUubzbHONR54VRnq2EvAaBHJN537o82xQ0p8p8JBxXkUdw6Q6XOT6XOubZXpc6cMy739xyfxwvtfcVq/bsx+YRNn/+//4/n3ynny6uHcc9HJFHfOZNHqbUx+6C1b27EqATS/js+sQTbj8fV8vKOWWMyoHfb17ia79lfzedeP6ptUVPOmFRupa4qw9rMqPtlZx4UPrGP+qq3s3N3EhIVvcNpdq7nwgXV8UWW0B1h8+SkU5RudMPNTCNlv60LsbgxxXGE2d08YyJ0XfI8HXt9GdoaHorxAkmO/NVhCp3sn45527DYCIbwel/PvR/teNJoDJm0ajIg8iaFJdBWRcozIrjuB5SJyJYb5awKAUuojEVkObAIiwLVKKSts5xr2him/aL4AHgIeMwMCdmFEoaGU2iUiswGrAfvvLYf/ocR6QraixMp2GRtqdkaMeRMGcsPTib6RDK+LHp0yHDfeSEwxfsjRdrjwoOI8TuvXjYsfXJegUVhdGudNGIjXJdx3ySB21RuJm1bWv9ezt55Yps/NNUvX88jlQ/F7hPsnDebPr25NanCWKsIsw+u2+7mAUWcsPhrMEkSzx/bnpB65LJs2nJiCTyvqHH0j3+xpskOprx/Vlz6F2dxx/oDvXJrFyd/y6BVDdY0xjaaNSWcU2cUpvhqV4vg7gDscxt8B+juMN2EKKIfvHgYebvViDwIBn5vFl51CcWej+OSLH+zgkuFHk5/pJRLdGxFmNfr6ZncQv9fl6Oze/E0tXeKe+p00nZtWbOTJq4fzaUUdd764mXsvGURTOJZg1po3YSDR2N4S/lbhSAGuf/I9CnJ8/NePSvB5hKeuHk5UKXbsbuIrc35zgdAp4CUUjdmFI1NVd870uQlHY/g8br6oqmf+qmQhdv+kwfzmOUNQbSir4fJH3mbNLWe2SS6KU9/7Sx9+i+evG6lrjGk0bYjO5D8IxGKKnXuCCZv7nHEDeGLdl/zih8dz7RMbkjbrOy/4Hr9Y/h5LrxrGph21CfPufmkLM87oY2/yqTbyaExxTJdM7rnoZABbS7K+v+Hp93ni6uEJ5y3KD6CA/73oZDZ/U8svlr0HGG2Gl6zdbheXbN6W2MqFue28EntdqfrINIT2buCNoQiVdUHufmlLQnmWpvBeQWXNaytzVSp/S2Mo2mY9XTQajRYwaSM+gsnrNtoax9cYs0qcfBvXe8SivLrR7s+yq94ITe5bmM3Wijrbab9w9Tb7qT9VqX0RuNSMHPv79d93vA5KMXtsf9uJbtQzCxstid0ubj33BGoawzy34SsuHnoMfQqzyfC68HuEpVcNo7I2aDcP+8UP+9GzU8DWArL8brv0Srzprltuhq0d9OgUYNGUUqY/trc8y4OXDsFv+kS+i7mqeRSZdU3d916jOThoAZMG4m38Bdl+fj/2pCTt5e6XtpAX8PJtrXOCpCU0KmqDTH9sfVJ5mA1lNSxZu53Fl51CTKkkjWLh5FLu+Nve48tSmLW+3t1EKBrjtvNOpCDHTzgawyVCXTCSFCXWLTeDoryAbTbK8fvI9Hno3imDwUfv9Y1YZqzK2iDzV33CnRd8j6M6ZeAW4ds6ox+LdQ6Px8WJR+UmmaaA72Suaqm3ve7potEcHHS5fpO2zIOJr4PlVDesKD/A7LH96VuYzY7dTWT43I4Jjdee2Zel675g+fpyRpcUcv2o4xO0AUtQbSir4fWbz8AlwlfVRlOyHp0y+PG9a+xrOlUiXji5lNqmMC4RahrDrFxfxm9/0p8Mr4uf3JucE/LMzBEU5mS0+vfwVXUDI+e8ljS+5pYz026Kiv83sCjKD9g1xVJpNxqNZv9oT3kwRwTxNv5U/pFeXbP4/Qsf8fKmCp68ehhPTRvOnsYwORle9jSFuXxkbxrDUaaf0YcLSovoku1jxdtfsuSKoVSbiZSWcCnKD/B1TRMFOX7bz7JoSmmCxhKv8exuDFOQ4+e+Vz9l+fryhLXd/mNFY8jZRxE2i2A64bRhH0pTVCo/i1VTTPe912jSz8HOg+nwxNezWjSllHA05phf4XcLL2+qYFBxHgAuUWT5PVz84Dp+/eyHxBTc+PT7jJr3L258+n1q6kO89XkNN5qthme/sMkWLvdMHEiWz82i1duYO94oRLkw7r11zevO6ktTOEpVfYivaxpZ+1lV0rp8Hvd+9523zFHnL1jDyDmvcf6CNWzZWUt+wMuDlw5JWMPBMkXt7z1oNJq2R5vITNrCROZk97/3kkGE40rPW5tst1w/t67cyNQRvbll5UYKsv3cc+HJnHH36pRmtfgS+zeMPp7izpkJfeUr64Lce8kg6poieN0uXCJ0zfFRsSdIYY6fXy5/347McjKZWT4KIKX/wsmM1JI5qkuW75CYolrywWhTmEbTdmgTWRqJNw2JCPe8siUhFPi6JzbwzDUjeGbmCMKRWIIT+9fnlXDJX4wqwuXVjWzZWUtRfiClWc3qyrihrIZbn/mApVcNIxqLkZ/l408XnczH39TyxLovzSKZLnY1hOiRl8ENT7/PrDElVNYF7fNZJrNl04YDJG3+VkmVWCxGVBntBKrqQ44CoiVz1KEyRblckrIsjEajOThoAfMdSFWBt7I2ZGsKBdl+ItEYEaVwi+A2jZIul+B2ScLGbIUet5Q/Yr1fMGkw2X43TeEYmT43CmUXt0zoJz+llOevG0k4EksKGb5+1PF0y8nA40m2lLpcQpcsX6u0gPYa9qv9LBrNoUWbyEwOxESWyjQUb8r6rx+dkGAes8J9e3XJcqzgO7qkkD/89HtU1AYThMH/XniyHUJs5dLce8kgOxorFlOUVzfYGlH8eqzIqUgkRkVdkEg0hsftojDb7yhc9nV/zbs7anOURnPkok1kaSKVacgygV0/qm/KWlw5GV7HfIxf/LAfCMxf9UlCf5S5L21OKhkTryE4aUTWNa3IKY/HRY+8RMf3gdxf8+6O2hyl0Wic0ALmO5DKNNQjL8CaW84karZBjseqxWX5J5w25h27G3l5U4UtUCyHfHzJGKdorLY2Ve3P+bQ5SqPRNEeHKX8HLA2keRjuUbkZ9MzPJOD1OIbKWrW4YO/GbPWEjy9lYmE55JdPP5U1t5zJszNHOpqfUq3nQMOC2/p8Go3myEL7YEwONEy5pYxwJ99EvA8mlQnpu/g02jpDXWe8azSalmjJB6MFjEm6WibHYopv64M0hWO4xSjbnxfY9yYdiylqGkM0hqJElSLD66ZrlmGC0hu+RqNpL2gn/yHE5ZL9qt8Vz849wSQtxu9x2e2GdbSWRqNpz2gfTDvFqSnW1Y++wxdVDUljVfWhQ7lUjUajcaRDCxgROUdEtojIpyJy66Fez/6QKkQ40+dOGmseNqzRaDTtgQ4rYETEDdwHnAuUABeLSMmhXVXrSVWs0crmjx871BnzGo1G40SHFTDAUOBTpdRnSqkQ8BQw9hCvqdWkChE+pkumDhvWaDSHBR3Zyd8TKIv7XA4Miz9ARKYB0wCOPvrog7eyVpAqCRO+W6dHjUajOVh0ZAHjtOsmxGQrpR4AHgAjTPlgLGp/SJUdrzPmNRrN4UBHNpGVA8Vxn4uArw/RWjQajeaIoyMLmLeBviLSW0R8wEXA84d4TRqNRnPE0GFNZEqpiIhcB7wEuIGHlVIfHeJlaTQazRFDhxUwAEqpvwN/P9Tr0Gg0miORjmwi02g0Gs0hRBe7NBGRSuCLQ72ONNAV+PZQLyLNHAn3CEfGfep7PPw4RilV4PSFFjAdHBF5J1Wl047CkXCPcGTcp77HjoU2kWk0Go0mLWgBo9FoNJq0oAVMx+eBQ72Ag8CRcI9wZNynvscOhPbBaDQajSYtaA1Go9FoNGlBCxiNRqPRpAUtYA4zRORhEakQkQ/jxjqLyCsistX8mR/33a/Mjp5bROTsuPFSEfnA/G6+iLSbmv8iUiwir4nIxyLykYj83BzvaPeZISJvicj75n3+zhzvUPcJRgNAEdkgIi+YnzvUPYrI5+ba3hORd8yxDnWPB4RSSr8OoxdwGjAY+DBu7C7gVvP9rcAc830J8D7gB3oD2wC3+d1bwKkYbQ1eBM491PcWdz/dgcHm+xzgE/NeOtp9CpBtvvcCbwLDO9p9muv7JfAE8EIH/Zv9HOjabKxD3eOBvLQGc5ihlHod2NVseCywxHy/BPhp3PhTSqmgUmo78CkwVES6A7lKqTeU8Vf9aNycQ45SaodS6l3zfS3wMUYDuY52n0opVWd+9JovRQe7TxEpAs4D/hI33KHuMQVHwj22iBYwHYNuSqkdYGzOQKE57tTVs6f5KncYb3eISC9gEMbTfYe7T9N09B5QAbyilOqI9/m/wM1ALG6so92jAl4WkfVmp1zoePe433ToasqalF0999ntsz0gItnASuA/lVJ7WjBHH7b3qZSKAieLSB7wrIj0b+Hww+4+RWQMUKGUWi8iZ7RmisNYu75Hk5FKqa9FpBB4RUQ2t3Ds4XqP+43WYDoGO031GvNnhTmeqqtnufm++Xi7QUS8GMJlqVLqGXO4w92nhVKqBlgNnEPHus+RwE9E5HPgKeAsEXmcjnWPKKW+Nn9WAM8CQ+lg93ggaAHTMXgemGq+nwo8Fzd+kYj4RaQ30Bd4y1TXa0VkuBmlcmncnEOOuaaHgI+VUv8T91VHu88CU3NBRALAD4DNdKD7VEr9SilVpJTqhdFV9lWl1GQ60D2KSJaI5FjvgdHAh3SgezxgDnWUgX7t3wt4EtgBhDGeeK4EugCrgK3mz85xx9+GEaWyhbiIFGAIxn+CbcC9mFUd2sML+D6GaWAj8J75+lEHvM8BwAbzPj8EfmOOd6j7jFvjGeyNIusw9wgcixEV9j7wEXBbR7vHA33pUjEajUajSQvaRKbRaDSatKAFjEaj0WjSghYwGo1Go0kLWsBoNBqNJi1oAaPRaDSatKAFjEZzgIhINxF5QkQ+M0uEvCEi57dybp3D2AwRuXQ/1+ARkW9F5L/3Z55GczDQAkajOQDMRLj/A15XSh2rlCrFSCQscji2VSWZlFILlVKP7udSRmPkUkxMVdpdRNz7eU6Npk3QAkajOTDOAkJKqYXWgFLqC6XUnwFE5DIReVpE/gq83JoTishvReRGETlRRN6KG+8lIhtTTLsY+BPwJUapf2vO5yLyGxH5NzBBREabGta75rqyzeN+IyJvi8iHIvLAYd9/RNOu0AJGozkwTgLe3ccxpwJTlVJn7c+JlVIfAz4ROdYcuhBY3vw4s7zMKOAFjAoPFzc7pEkp9X3gn8CvgR8opQYD72D0ZwG4Vyl1ilKqPxAAxuzPWjWaltACRqNpA0TkPjE6U74dN/yKUqp5757WshyYaL6/EFjmcMwY4DWlVANGYdDzm5nDrDnDMZpcrTFbA0wFjjG/O1NE3hSRDzC0spMOcL0aTRK6XL9Gc2B8BIyzPiilrhWRrhjagUX9dzj/MuBpEXnGOL3a6nDMxcBIs1IxGLWvzsTQWOKvLxjCLkHDEZEMYAEwRClVJiK/BTK+w5o1mgS0BqPRHBivAhkick3cWGZbnVwptQ2IArNw0F5EJBejKOjRSqleyqhWfC3JZjKAdRiC6DhzbqaIHM9eYfKt6ZMZ31br12hAazAazQGhlFIi8lPgHhG5GajE0BhuaeUpMkUkvnvh/zgcswyYi9G3vTkXYJS+D8aNPQfcJSL+ZmutFJHLgCfjvvu1UuoTEXkQ+ACjp3y8eU+j+c7oasoajUajSQvaRKbRaDSatKAFjEaj0WjSghYwGo1Go0kLWsBoNBqNJi1oAaPRaDSatKAFjEaj0WjSghYwGo1Go0kL/x8Uwd49EKHGdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Gr Liv Area',y='SalePrice',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>908154235</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>480</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>908154195</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>128.0</td>\n",
       "      <td>39290</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elev</td>\n",
       "      <td>17000</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>183850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>908154205</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>40094</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>184750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "1498  908154235           60        RL         313.0     63887   Pave   NaN   \n",
       "2180  908154195           20        RL         128.0     39290   Pave   NaN   \n",
       "2181  908154205           60        RL         130.0     40094   Pave   NaN   \n",
       "\n",
       "     Lot Shape Land Contour Utilities  ... Pool Area Pool QC Fence  \\\n",
       "1498       IR3          Bnk    AllPub  ...       480      Gd   NaN   \n",
       "2180       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "2181       IR1          Bnk    AllPub  ...         0     NaN   NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "1498          NaN        0       1    2008        New         Partial   \n",
       "2180         Elev    17000      10    2007        New         Partial   \n",
       "2181          NaN        0      10    2007        New         Partial   \n",
       "\n",
       "      SalePrice  \n",
       "1498     160000  \n",
       "2180     183850  \n",
       "2181     184750  \n",
       "\n",
       "[3 rows x 81 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gr Liv Area']>4000) & (df['SalePrice']<400000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1498, 2180, 2181], dtype='int64')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gr Liv Area']>4000) & (df['SalePrice']<400000)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_drop = df[(df['Gr Liv Area']>4000) & (df['SalePrice']<400000)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(ind_drop,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gr Liv Area', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Gr Liv Area',y='SalePrice',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Overall Qual', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eF590KBc8sGJ3uPS0JhLx8im6khSMiJyPX7NrAPB5/Mz424GTyiaJYRhGLyCtyrMrN/Dsyg1543O+5tYHszOZ4clX1nHPN79IxBPSGWVxy3uce+xIBtSX5xilqqqLgInApwCquhoY0u0MwzCMkBGGPiyeFPbBiGMfTDqjvLR2K29/vION29p4++MdvLR2a1nL9ZdqImtT1WT2AgR1vyxD0zCM/YZsH5Zv55iEbp/WxOFD+xJ16P8QUW79xhfYsqOduniEnck0DfUxHOsXErEIPzz1MH6weHc+zvWTx1Ibc1/s8j9E5EdAQkS+AjwC/HvZpDAMw6gwG7a3dSgX8EODv/3ACud9WDyE9lSGKx9/gynzX+DKx9+gPZWpSsRVVrmAfz1+sPi1su6/1HO6HNgIvA7MBn4F/HNZJTEMw6gg7ekMg/vUcMe5TSycNYE7zm1icJ8aUmnHnSQVvr/o1bwb+/cXveq8XH97OlMwH6e9jNejVBNZArhbVe8ECFoZJ4CdZZPEMAyjgiRiEa46fQybd7QDEI94XHX6mLKahEoho4UTLTOOEy2z/WByZalWP5il+AolSwL4ddmkMAzDqDCRIne7YuOVIhb1Cjr5Y47zYBoSMW6f1pSXj3P7tCYaErGyHaPUFUytqm7PflDV7SJSVzYpDMMwKkwqrexMprny8TfynNqptNuVQ9QTbjz7qA4zWWNDghvPPopoOVPoS2BLazv/XiBMeehxo8pW7LJUBbNDRMap6ssAItIEtO5hjmEYISCTUTbtSFasqdT+QntGCzq1H541wa0cqQwN9XEWzByPJwS+F6Xdcch0JpPhuMOGMnPB8ryqzpkyVt0sVcF8D3hERLJthw8EppRNCsMwKkK5en70BIoVmSxn3kcpiCd80trOJQ+/0vE7uWnq0fQro2mqFNJauKrzotnHlO0YJSkYVV0uIocDh+EX4fyjqraXTQrDMCqCi54f+wuxoMhkZ6e2a9NUKp3h2Tc+6mKamjFxpFM5tEiwQTmbUHarYETkRFVdJiL/s9NXo0UEVf1l2SQxDKPsJB30/NhfiAdFJru0KnbsXI96wlePGpZnmrrtnHHOFV0Yosi+HLx+rcDPaaUcQEQiIvLfIvJk8HmAiCwRkdXBa0POtleIyBoRWSUip+SMN4nI68F3N0tQUkBEakRkYTD+ooiMyJkzIzjGahGZUYqshtHTyN5Ecin3TWR/QQT61cVYMHM8yy79MgtmjqdfnfsM+va0cmGg5MBX+Bc++DLtjoMNXESRdatgVHWOiHjAU6o6s9PPt0o8xiXAH3I+Xw4sVdXR+OHPlwOIyBhgKnAEcCpwW5BvAzAPv9jm6ODn1GD8PGCLqo4CbgSuDfY1AJgDfAkYD8zJVWSG0VsYWB/nzunNeTeRO6c3M7A+XmXJ3LOrPcNvVv6ZeNTD84R41OM3K//Mrna3zvVUEV9QyrEvaEtrOzcvfSuvXP/NS99iS2v5vB979MGoakZEvgMs+qw7F5FG4KvA1cA/BsOTgOOD9/cCzwGXBeMPq2ob8I6IrAHGi8haoJ+qPh/s8z7g68BTwZyrgn0tBm4NVjenAEtUdXMwZwm+Unros56DYezPeJ5w2NC+PHbhxF4fRRaLCM0jB/GNO1/Iq0UWi7i9FtGQ+IKSqTQbtyXzxjZuS1alZfISEfknERkemLgGBKuEPfFz4IdA7iPCUFX9CCB4zVZlHga8n7PdumBsWPC+83jeHFVNAZ8AA7vZVx4iMktEWkSkZePGjSWcjmHsf3ieMLhvDcMa6hjct6ZXKheAVIaCtchcF1QOS8OxRDzCj/7ucOY+uZIp819g7pMr+dHfHU4iXj7zaalhyllz2EU5YwocUmyCiJwGbFDVFSJyfAnHKPRXr92M7+2c3QOq84H5AM3NzVYd2jAqSLXzcbK1yHIbfd3+3J+c1yJLpjP0qY3w0PkTSKsSESGVSZe1BlgppNJasCbaLy84tmzHKDVMeW/i5yYCp4vI3wG1QD8ReQBYLyIHqupHInIgkO26sw4YnjO/EfgwGG8sMJ47Z13QQuAAYHMwfnynOc/txTkYhlEGwpCPUxP1Cpandx1F5omweUc7lzy8PC8PZlj/khsMl4W2IhGGbWVc0nV7ZUXkSyLyqohsF5HnReQvSt2xql6hqo2qOgLfeb9MVacBTwDZqK4ZwOPB+yeAqUFk2Eh8Z/5LgRltm4hMCPwr0zvNye5rcnAMBZ4BThaRhsC5f3IwZhhGFSiWj7NpR3IPM8tHJqM898f13PPNL7Ls0i9zzze/yHN/XO880VKVjiRL8K/FJQ+/guNal0Ubn5VT3+9JZf4r8E/Ab4HT8X0qp3Q3oQSuARaJyHnAe8BZAKr6pogsAlYCKeAiVc16my4AFuAX2Xwq+AG4C7g/CAjYjK/IUNXNIjIXWB5s95Osw98wDPeEIR8nGhVO65R/Mu+ccUSjbn1SLsrkl4LnwbVnju3I5s+WiinninJPCsZT1SXB+0dE5Iq9OYiqPkdgolLVTcBJRba7Gj/irPN4C3BkgfFdBAqqwHd3A3fvjbyGYZQXkcKRUy7bBLenlFuWrc7zwdyybDVzvnaEMxkAopEiUWSOo9kinse9//VO3vW497/e4V/O+MuyHWNPCqZ/pyz+vM+WyW8YRilEpPDTsst7qgjMOHZkFxlcJ1p6Ilw/eWwXX5DnWJBB9TV8/yuHdfGLDaovXwkh6a7ujIjc081c/QzJlqGnublZW1paqi2GYfRINm5r43899hpnNg3veFp+dMX7XH3GWGc10T7YspMp81/osnJYOGsCwxrcdR95d9MO/nXZGs4/7pCOWmR3/vZtLjpxFJ8bWO9MDihPZJ+IrFDV5kLfdbuCUdWZn+lIhmEYBRhYHy/4tOyyokBYOkkmoh5njMv3BV0/eSwJx9FssDtHqlKUFBcnIkOBnwIHqerfBmVdjlHVuyommWEYPYYwVBTwiviBXJum0krBvjSPlLFMflgoVWUuwA/zPSj4/BZ+jxjDMIySqHZFARG44ayj8jLobzjrKOc+mGSRKLKk4ygyF5Sa2TNIVRdlo8hUNSUiva/et2EY+y2eCH1qIsyddCR18Qg7k2n61EScr2DCUosMKl9d4bO0TB5IUG5FRCbg1/0yDMPYL1CF2Q+83OXG7to0lYh7zJvWxAVBXbTGhgTzpjWRiLv1wbiorlCqgvlH/Kz5z4vI74HB+JnzhmEYJVHtWmTFTFOuExx3tStPvrKuQEfLoqUdK4KLbqel1iJ7WUS+zO6WyausZbJhGKWSyShrN+3g3U07O8xTnxtYx4iB9c6UTMQTZv/1CCY3H5x3Y3ftC0qlM9zxn2u54z/X5o2fc8wIp3K4qK6wp5bJnVslZznUWiYbhlEqW1rb2LS9jSsff6PDHHPj2UdxQF2UgfW1TmSoi3ucdnRjfqmYaU3UOTZNRTzh5DFDuuQERRz7glxUV9jTCuZr3XyngCkYwzD2yK5kpmBp+IWzJoCj3MLWZKbD75GV4YIHVjiVAfzGZ985cXRH2+TGhgS3nTPOeeMzF9UVLNHSMIyKU6xNcNphJeOwtCpuT2uHcsnKcOGDL/uKziGe5/HbVeu7+IIOHTqqbMcouQGBiHwVOAK/twsAqvqTskliGEaPJVIkNNel/yNaxDTlOjw4HQJlC9CQiPG1TibD26c10ZCIle0YJRkfReR2YApwMb6T/yzgc2WTwjCMHk0i5nFbpzbBt50zjkTMnf+jvsbj4pMOzWsRfPFJh1Jf47jhmFekD4tjRbeltb1gC+ktreWL3yr1yh6rqtOBLar6Y+AY8rtPGoZhFCWZUm4NSuUvnDWBK08bw63LVpNMuXtq39lW2Aezs81tmHI84nH95LF5yvb6yWOJR9wquqpHkeWQlWKniByE39xrb9ooG4bRC2lPZ9i4Lb975cZtSac5KGHxwagqA+pjLJg5Hk8go5DOpOmusn0liEcjBc2W8WikbMcoVcE8KSL9geuAFcHYv5VNCsMwejQ1UY8fnnpYlx4oNQ4rCBfzA0Vc+2BU2bozxaWP7M7kv+Gso+hbWz7fRyk0JGLcPq2pw0zm3AcjIl8Ukf+hqnNVdSvQB3gdeAS4cQ9za0XkJRF5VUTeFJEfB+MDRGSJiKwOXhty5lwhImtEZJWInJIz3iQirwff3SxBoLaI1IjIwmD8RREZkTNnRnCM1SIy47NfGsPYNzIZZeO2Nj7YspON29qc934PE6mMFqwg7HL1IEFYbq5p6tozx5a1B30pqMKlj+SHbF/6yKs4XsCwpbWdm5e+lWe2vHnpW2X1wexpBXMH8DcAInIccA2+o/9oYD7dl4tpA05U1e0iEgN+JyJPAf8TWKqq14jI5cDlwGVBC4Cp+JFqBwG/FpFDVTUNzANmAS8AvwJOBZ4CzsP3C40SkanAtcAUERkAzAGa8fN1VojIE6q65TNcG8PYa1zUedqfCEMvFlUKtgh23TI5DNcCfB/Msys38OzKDXnjc77mzgcTUdXNwfspwHxVfRR4VERe6W6i+gbF7cHHWPCjwCTg+GD8XuA54LJg/GFVbQPeEZE1wHgRWQv0U9XnAUTkPuDr+ApmEnBVsK/FwK3B6uYUYElWdhFZgq+UHtrD+RpGWXBR52l/wpMiZVocZq9HPWHmxJFdzHSuw5TD0pfGhQ9mTwbQiIhkldBJwLKc7/bovxGRSKCINuDf8F8EhqrqRwDB65Bg82HA+znT1wVjw4L3ncfz5qhqCr/C88Bu9tVZvlki0iIiLRs3btzT6RhGybiI0CmVMJjqamO7y7SceMN/MHPBck47upFah2HKbakM1z29Ks8kdN3Tq2hzXOyymKnOdV+agfVx7pzenCdHubuM7klJPAT8h4h8jB9J9p8AIjKKEsr1B+ato4MAgcdE5MhuNi90ebWb8b2dkyvffHxTH83Nzb3XQG6UHRdPh6UQFlPdrvYMtwT2/qx56palbzk1T0U8YeP2Nmbfv6JjrLEh4b4GGFLQVPfj07u7PZYfF11Gu318UNWrgUvxO1r+le6Oo/PwfTElEQQIPIdvplovIgcCBK9ZA+A68nNrGoEPg/HGAuN5c4KV1gH4IdTF9mUYTshG6OQ+HZY7QqcUipnqNu1I7mFmeRGBGceOzEtynHHsSKdP7WFx8sdjwg9OOawj7yUe8fzPMfe+uUp3Gd2jmUtVXygw9tae5onIYKBdVbeKSAI/WOBa/L4yM/ADBmYAjwdTngB+ISI/w3fyjwZeUtW0iGwLmpy9CEwHbsmZMwN4Hj/gYJmqqog8A/w0J0LtZOCKPclsGOUiN0In+5R689K3uPqMsU59MGEx1anC6j9/wi/On0BGFU+EZSs/YsTAOmcyZIo4+f+3Yyd/e0rZurO9S2Vp12HKLii5FtlecCBwr4hE8Fc8i1T1SRF5HlgkIucB7+GXnUFV3xSRRcBKIAVcFJjYAC7AX0Ul8J37TwXjdwH3BwEBm/Gj0FDVzSIyF1gebPeTnGAFw6g4LiJ0SiEejRR0rrs21dXGPSYeOoQ/bdje0Q9m4qFDqHVYKj/mCRedMIrNO/ww3HjE46ITRhFz3Q8mowUrSz/suNilCyqmYFT1NeALBcY34QcMFJpzNXB1gfEWoIuBUlV3ESioAt/dDdz92aQ2jPIQFh9M/9powR4o/Wsr+WzZlVRK+Xhbfj+Y6yePpV+NOzliUSHieXky3D6tiVg0HMUue2KelNviN4bRS3ARoVMKG3ckC9bf2ujYB9NeJNGy3eFNtTWZKZhY2Jp0G0UWLVLs0nVFARe4fYwxjF6CiwidUmgPSR/6MDy1ZwMNOjfYcn1fj3rCjWcf1WEmy/pgXOfjuMAUjGH0YKJF6m+5vpnFo15BOWIOa5Gp0qFcwFdwlz36mvNGX9GIcEBdjLmTjuzwRx1QFyPquKOlC8xEZvQ4wpBYmMkoazft4I0PPmHdllbe+OAT1m7a4VyWurhXMFzadR/6qCcF5XCp6Io2+nJcoiWVVlSV4QP80ODhA+pQVVLpnueDsRWM0aMIS2Lh1tYk6z/d1cWp3b8uxoB6d2HK6Qz0qY3klYaPRvxxl7SlMgXDtl2GCEcjhVdRUc+tsm1LZ7j80Tf49vGf77gWtz/3J276+y4xUfs9toIxehRhSSxsTaYLOrVbk27DlNMZ/2m5JurheUJN1ENVnbfnTWeU/ok4hwyqZ3DfGg4ZVE//RNzpis4TCjb6cu36qI1FOioKTJn/ArPvX8HG7W1Oy+a4wlYwRo8iLImF6SIVc11bQWJRYfv2DBc8kB+mfEDC7V21TzzCtGM+lxcufds546iPuwvbzq1Fll05XPf0Km6aerQzGQAaaov0YbFES8MIN2HJP6mNFZbD9VNqa7Jwm+CFsyZAvTs52lIZLnzw5Tw5LnzwZRY5dLDHIoVrkbl2rm/ckSxoLrzq9CM5qH9izzvYj+h5azKjVxOW/JNB9TXceW4nOc5tZpBD/wsUbxPs2kTWXkQOl3kwUc/jpqlH5/1Obpp6tHMfTLH20SnXjjEH2ArG6FGEJf8EoCbm5YWi1lTBxl4sTNl1Ul8YwqU9geEDEjw8awLpjBLxhHhUnD9lJ2KRgu2ja2NuV9kusBWMYVSATTuSXPPUH0gGT6XJdIZrnvqD82ADLyQVhGuiHvM6hSnPm9ZEjcM8GPHgk9YUq9dv58+f7GL1+u180ppCqnAXLBQA0hOxFYzRowhLmHImk+HCE0axJaew4oUnjCKTcWsGKVZBuBptgmujkreiq42K0zbBqZSysy2VN7azLeW0HhqEp7qCC0zBGD2KsLQqFhFak+kueTDiuLlV1BPO/+tDql6WpD2tzFzQ0sVE5jKLXoGdBX4nrtMbwxKI4gJTMEaPIixhyu3pDPf8Pn/lcM/vq7NyiEXzfUGxqOd05QDFgw1SDp38qSIFN12Xyc8GonReZbsORHGBKRijRxGWp0OvWGFFx/b+jMJ3fvHfVV05gN+L5eQxQzizaXiHwn10xftOe7FkQlBwE8IViFJpzMlv9CjCEqacKVJY0bELpmjCp+sVTE3M4zsnjs5rmfydE0c7jawrVia/GlWMK92qOCyYgjF6FLlPh7+/7AQeu3Cicwc/dFNY0fHTclSK9B5x7AtqbS+caNna7lDjCvx8Sn4ezM+nHA09894eCsxEZvQ4sk+H1SQSkvwTzxOunzy2S85FWBSuS/NURIRYJD+SLRYR58q2N1ExBSMiw4H7gP8BZID5qnqTiAwAFgIjgLXA2aq6JZhzBXAekAa+q6rPBONNwAIgAfwKuERVVURqgmM0AZuAKaq6NpgzA/jnQJx/UdV7K3WuhtGZaJEbu2tzTFsqw2Mvf8A93/wiEU9IZ5Q7f/s23zlxlFM5wpDwmVa4qIA/atHsY5zJ0NuopIksBVyqqn8BTAAuEpExwOXAUlUdDSwNPhN8NxU4AjgVuE1Esp7ZecAsYHTwc2owfh6wRVVHATcC1wb7GgDMAb4EjAfmiEhDBc/VMPJIpncXVsy2573u6VUdiZeuSMQinDFuGDMXLOfEG/6DmQuWc8a4Yc6zxhPxwomWCYd9adKZwvknadeOsV5ExX67qvqRqr4cvN8G/AEYBkwCsquJe4GvB+8nAQ+rapuqvgOsAcaLyIFAP1V9XlUVf8WSOye7r8XASeInGpwCLFHVzcHqaAm7lZJhVJyIJwzumx9YMLhv3LmJrFhorsvwYIBdyQy3BAUeswr3lqVvsSvp7uYeCYk/qjfh5PFBREYAXwBeBIaq6kfgKyFgSLDZMOD9nGnrgrFhwfvO43lzVDUFfAIM7GZfneWaJSItItKycePGfThDI0yEoaNlbczjh6ceTjzi/4vFI/5n19WUw5I13p7RggUeXRa79DwpWOyyp0ZwhYGKO/lFpA/wKPA9Vf20m0zmQl9oN+N7O2f3gOp8YD5Ac3Nzz+tX2gsJS6mYVMpXcp2zxl2XJQlLsEFt1Ctc4NFhLTJPoG9tNM/J37c26rwuW2+ior9dEYnhK5cHVfWXwfD6wOxF8LohGF8HDM+Z3gh8GIw3FhjPmyMiUeAAYHM3+zJ6OGHpaJksYppKOl5NxSNewS6O2ZWVK9JauMCjywZs7WnlWwtamLlgOVPmv8DMBcv51oIW2l13getFVDKKTIC7gD+o6s9yvnoCmAFcE7w+njP+CxH5GXAQvjP/JVVNi8g2EZmAb2KbDtzSaV/PA5OBZUF02TPAT3Mc+ycDV1ToVI0QEZZSMWHJgwHf97Ng5ng88RNA/aBOt4TBwR6GcjW9jUqu1ycC5wKvi8grwdiP8BXLIhE5D3gPOAtAVd8UkUXASvwItItUNXtXuIDdYcpPBT/gK7D7RWQN/splarCvzSIyF1gebPcTVd1cofM0QkQs6hU0CcUcmmLAL41SUA7nTv4Mn7am+e7Du02GN0/9AvVxt0rGk8LXw3PoYA9DT5rehqjjkhFhpbm5WVtaWqothrGPbN3ZxoZtbXywZVeHnX1YQy1D+tbQv85d8uWGT1v5pLWddTlyNDbUckAixpB+7trifri1lbPveL5g7ofL9rwfbt3JOx/v7FKbbeSgOg7qX+dEho3bdrFlZ7LL30ZDXZzBfWudyNATEZEVqtpc6DvL5Dd6FG3tGdpT+U/n7akMbS5LkgAofLorlefkv2nq0RxQG3MqRrEoMufteYv0pbnKYXXp9nSGT1vzfyc/n3I0fR0HXvQm7MoaPQql6439+sljGdjHbemYlMIlD7+S59S+5OFXnGeNR4pUMXYdmhuNeMycOLJrZQOHwQaq8L2F+b+T7y18xXll6d6EKRijRxGWnh+pkKwc6uIeF590KBc8sKLjxj5vWhN1DjPoAVrb0x2VDbKK7rqnV3HT1KOdyRCmwIvegikYo0cRlp4f0UjhYAOXT+yQn0GfvbHfsvQt3zRV706OiCds3N7G7PtXdIw1NiScrqTCkhPUm7By/UaPInsTyaUaN5GoUDD/JOr6XibwjycfyucH92Fw3xo+P7gP/3jyoc5L1MeC4p+dr4fLqLqoJ9x49lF5MlSjfTSEo9qEC2wFY/Qo6uIej3x7Aqm032wrIkI0gtOMcYDWVKagSejnDk1C4CdaJlPKPzz4UoeJ7LZzxjlPtMyokohH8rLoE/GI08ZnIkJ9p0z++too3VQXqQhhqTbhAlMwRo9CgU3b2/l2js/h9mlNHNTfrZM/WsQkVI1y/bcuW52n6G5dtpo5DqO3wE/wvO03azizaTh1REimM9z2mzVO5YhGINZJscYiHo67aRetNvHYhROr3seo3JiCMcpGJqNs2pGsap/xXclMh3IB/5/32w+sYNGsCU59DllzzPcXvdqh6KphjhGBGceO7JJ/4rqAcNQTLjphFJt3tAP+yuqiE0Y5vR47kxlm3rO8iw/m4VkTGODwbyMs1SZcYArGKAthWfa3Z5TBfWrynthvf+5PTqv2AmRQYlEvv3ti1CPTteZqRVGlQ7mAfyO77NHXnIfmZlDa09olB8Xl9chokQAQx8nm8WikYLBB3PVSygHm5DfKQliKTNYEVXvnPrmSKfNfYO6TK/nhqYdR49gHk8nAK+9u5vND+jD0gFo+P6QPr7y7Gde9rdJFbqppxzdVzRTOQVGH16M2FikYAOK6+drA+jh3Tm/OCza4c3ozA+vje5i5/2ErGKMsJFPpgisH18v+jBbOg1k02+0Te23Mo2nkIL5x5wt5+Se1jvNPokVqgEUd28jaQ1BoclB9Dfd9azzvbtrZsar83MA6BtW79Xt4nnDY0L48duHEqpqTXWArGKMsJOIRrjp9TF6DratOH0Mi7vbpMJWu/o0MYFd7piO5MSvDBQ+scNrBEXwfzA1n5Yfm3nDWUYjj//xoSMLH21IZrnz8DabMf4ErH3+DtlR12iV7njC4bw3DGuoY3LemRyoXMAVjlAlVZWcynffPuzOZxnUx1WI3MtdP7GEpDa8Kd/3u7bxWxXf97m2npinwfy8F84Ic3ljDYsbtTZiCMcpCa3umoGmq1XGRyVjUY9454/JuZPPOGee8XH9RRef6SVXgvL86JM8ndd5fHeI80bItJy8oq+iue3qV0xVEb4reCgvmgzHKQrE6T64zlJOpDLd0yvu4pQp5H7Uxj3nTmrrUAKuNuVV0uSuY7PW463dvO78exUrFuDSRSRF/lOtEy96EKRijLBRr5uTaxp7KKM+u3MCzKzfkjf/zV8c4lSOZUla88zG/OH8CqoqIsGzlRww68iCnctTGPL5z4mgufPDlvEx+14ouFhHmnTOOC3LkmHfOOGIRh7XIBK49c2yXnCCHIvQ6TMEYZaE27nHT1KM7StRn+5+4jpqKeMLsvx7B5OaDiXhCOqMsbnnPuRPVE/jL4Q15UWQ3TT0a1xay7W1pHnj+Xe755hc7rsedv32bi04cxcA+7uRoTysr1m7qonC/csSBzmTwPK9gT5qrzxjrTIbehikYoyz0i8fol4jlJRb2S8ToF3fbYKs+7nHa0Y3MXLA8zzRV71jRpVWJeJJ3PSKeOM8/iXnCf729iUUr1nWMNTYk+N7fjHYqR9/awmHbfWvd/V4G1sf5/lcO65IM3BPzT8JCxVomi8jdwGnABlU9MhgbACwERgBrgbNVdUvw3RXAeUAa+K6qPhOMNwELgATwK+ASVVURqQHuA5qATcAUVV0bzJkB/HMgyr+o6r17ktdaJu8bYWnN+8GWnUyZ/0IXORbOmsCwBjetecMkx4ZPW1n/aVsX09TQfjW9snVzGMoZ9TSq1TJ5AXArvhLIcjmwVFWvEZHLg8+XicgYYCpwBHAQ8GsROVRV08A8YBbwAr6CORV4Cl8ZbVHVUSIyFbgWmBIosTlAM37twxUi8kRWkRmVIZXJcOwhAzn/uEPyTDEpx6nrYQkPDktzq1RGefLVD/JMZItb3mPGxJFO5QhL6+Zs/onhhoopGFX9rYiM6DQ8CTg+eH8v8BxwWTD+sKq2Ae+IyBpgvIisBfqp6vMAInIf8HV8BTMJuCrY12LgVvHDQU4Blqjq5mDOEnyl9FC5z9HYTV0swj8cN5J1W1o7TEL/cNxI6hyX4YgWaRHsOjzYC0urYs/j7PEHo+ofV0Q4e/zBRL3qhG1XOwjEcItrH8xQVf0IQFU/EpEhwfgw/BVKlnXBWHvwvvN4ds77wb5SIvIJMDB3vMCcPERkFv7qiIMPPnjvz8ognVE2bU/mFTO8fvJYDqh164Opi3t896RDu5Trd90iuDZaJHrLcT5OLOJXEe4cLj3AnZUO8GvE3XbOuC7Xw3WNOMMtYfntFnqM0W7G93ZO/qDqfFVtVtXmwYMHlyRoGAlDd7z2TOEaYK6rGLcWKdff6rhESzKtHTfTrBwXPvgyybTb65GrXLJyXPDACnY6vh6K8P8CU92yS7/MPd/8Iv/v1Q9Q1xmfhlNcr2DWi8iBwerlQCCbrLAOGJ6zXSPwYTDeWGA8d846EYkCBwCbg/HjO815rrynsZtqOw3DUiY/LImWYfHBhMXnEJbr0ZCI8bVO0X23T2uiIeF2hWu4xfUK5glgRvB+BvB4zvhUEakRkZHAaOClwJy2TUQmBP6V6Z3mZPc1GVimfkjcM8DJItIgIg3AycFY2cne3M+47fdMvPY3nHHb71m1fpvTm2pY6ivFIr7P4Y5zm1g4awJ3nNvEyWOGEHWcxRYJSVHFsBR3DEvJmi2t7dy89K28UjE3L32LLa3tTuUw3FKxFYyIPIS/khgkIuvwI7uuARaJyHnAe8BZAKr6pogsAlYCKeCiIIIM4AJ2hyk/FfwA3AXcHwQEbMaPQkNVN4vIXGB5sN1Psg7/chOG1qdhqa9UG/O4+KRDq14aJeoJ//qNL7B5R3tHsMGA+lhVOkkWyhp37dOORaSg78NlBj34f6eFKizM+ZrVAevJVDKK7O+LfHVSke2vBq4uMN4CHFlgfBeBgirw3d3A3SULu5eE4eYej0YKRiu57o7XWsTWv9Bxq+KMKrvaM3nBBjecdZTzroUZpWDW+P92XAMsldYO30demPKxbsOUe1MXR2M3lsm/D4Thn6YhESsYNeXath0WW39G4dJHXs1TdJc+8qrzFsExT5g5cWRH4EM2qi7meAkTj3qcdtSw/MoG54wj7jh6K9vF0bLoexemYPaBMPzTbG5NFoya+uWFxzKkb60zOYrlObg2TRVNcHS8ghGBgX3ieaViBvaJ47pwbzQi9EtEWTBzPJ74CjgWwblvrDd1cTR2YwpmH6mJenk3Eddx/bvaC5vp2trd2rbDkucQKZbQ5/jOnkwrj7a836Xo5rmOTVP9auNs25UiN1LfE6FfrfuVg2XR9z5MwewDm3YkmX73S11uZi6d/MV6rru+oe5IFq/aO8ihHIl44T4sCceJllFPOP7woXmmKdcdHMG/qQ/rX2f1t4yqYApmHwiDkz9WZOVQjQ6OW1uTvP3xjg6n9tbWpPMb6q72DH1qPB46fwIZVTwRUpk0bY47awLUxSN5q9u6eHUc2rZyMKqFKZh9IAxO/rZUhls7dXC8ddlq59FKdfHCYcquS7Sowrl3LS9YxdglxdSqrRuM3kRYSsXsl2Sd/Ln93107+TPqd3Ccff8Kpsx/gdn3r+DZlRuch+WGpSRJWKoYJ2o86mryn9/qaqIkauxfzug92ApmH/A8YfTgPiyafQzt6QyxiMeQPjVO7duRkPhgwhK95RVx8rv2OfSJx9mVyJAY2od0xm8+Fo8KfeIWlmv0HkzB7AOZjLJ64/aq1gETgX+b0UTUi3SEoaYy6SpkjHsFb+wxx2Xh41GP6yeP7ZJ/4jrvw/OEQfW15lw3ejWmYPaBMJSKqYl6tKeUf3jwpaom0iHKDWcd1ZHkmM2gd+10iEhh57rjtA/AnOuGYQbhfSAMUWRtKe1oh5s9/gUPvkxbynEGfQbu+t3becUM7/rd286rKe9IprnqiZUkg6rFyXSGq55YyQ7HviDDMGwFs0+EIYosLGXhE/FIwdIoCcehuRERNm5vY/b9KzrGfJ+UUzEMw8BWMPtEGKLIsr6PXBobEkQjbn+1/RNxhvarZe6kI1k4awJzJx3J0H619E+4dWon4hGunzw273dSDUVnGAaIOo7yCSvNzc3a0tLymedVu+FYKpXhj+u3dSl2efjQvkQd+2GqfS2yMqzdtIN3N+3s8MF8bmAdIwbWm4PdMCqAiKxQ1eZC35mJbD8nGvU4bEgfFs6aQCqjRD1hSJ8a58oFwuHU9jxhxMB6+tbGLHrLMKqMKZh9IAztijMZZc3HO6reMjlMhEHRGYZhPph9IgztisMgg2EYRiF6tIIRkVNFZJWIrBGRy8u9/zCEKYdBBsMwjEL0WAUjIhHgX4G/BcYAfy8iY8p5jGyYci6uw5TDIINhGEYheqyCAcYDa1T1bVVNAg8Dk8p5gDCEKYdBBsMwjEL0ZCf/MOD9nM/rgC/lbiAis4BZAAcffPBnPkAY2sCGQQbDMIxC9GQFU+gOm5f0o6rzgfng58HszUHCELEUBhkMwzA605NNZOuA4TmfG4EPqySLYRhGr6MnK5jlwGgRGSkicWAq8ESVZTIMw+g19FgTmaqmROQ7wDNABLhbVd+ssliGYRi9hh6rYABU9VfAr6oth2EYRm+kJ5vIDMMwjCpi1ZQDRGQj8G615dhHBgEfV1uIEGHXIx+7Hruxa5HPvlyPz6nq4EJfmILpQYhIS7Gy2b0Rux752PXYjV2LfCp1PcxEZhiGYVQEUzCGYRhGRTAF07OYX20BQoZdj3zseuzGrkU+Fbke5oMxDMMwKoKtYAzDMIyKYArGMAzDqAimYHoAIjJcRH4jIn8QkTdF5JJqy1RtRCQiIv8tIk9WW5ZqIyL9RWSxiPwx+Bs5ptoyVRMR+X7wf/KGiDwkIrXVlsklInK3iGwQkTdyxgaIyBIRWR28NpTjWKZgegYp4FJV/QtgAnBRubt37odcAvyh2kKEhJuAp1X1cOAoevF1EZFhwHeBZlU9Er9O4dTqSuWcBcCpncYuB5aq6mhgafB5nzEF0wNQ1Y9U9eXg/Tb8G8iw6kpVPUSkEfgq8G/VlqXaiEg/4DjgLgBVTarq1qoKVX2iQEJEokAdvayNh6r+FtjcaXgScG/w/l7g6+U4limYHoaIjAC+ALxYZVGqyc+BHwKZKssRBg4BNgL3BCbDfxOR+moLVS1U9QPg/wDvAR8Bn6jqs9WVKhQMVdWPwH9gBYaUY6emYHoQItIHeBT4nqp+Wm15qoGInAZsUNUV1ZYlJESBccA8Vf0CsIMymT/2RwLfwiRgJHAQUC8i06orVc/FFEwPQURi+MrlQVX9ZbXlqSITgdNFZC3wMHCiiDxQXZGqyjpgnapmV7SL8RVOb+VvgHdUdaOqtgO/BI6tskxhYL2IHAgQvG4ox05NwfQARETwbex/UNWfVVueaqKqV6hqo6qOwHfeLlPVXvuEqqp/Bt4XkcOCoZOAlVUUqdq8B0wQkbrg/+YkenHQQw5PADOC9zOAx8ux0x7dcKwXMRE4F3hdRF4Jxn4UNFwzjIuBB4PW4W8DM6ssT9VQ1RdFZDHwMn705X/Ty8rGiMhDwPHAIBFZB8wBrgEWich5+Er4rLIcy0rFGIZhGJXATGSGYRhGRTAFYxiGYVQEUzCGYRhGRTAFYxiGYVQEUzCGYRhGRTAFYxifARFpFJHHg6qzfxKRm4Lw30ofd3vwOiK3Cm6nbY4QkWUi8lYg249FZK//x0VkrYgM2tv5hmEKxjBKJEjM+yXwf4Oqs4cCfYCry7DvfcpJE5EEfrLcNap6KPCXwHj8qtKGURVMwRhG6ZwI7FLVewBUNQ18H/hWkBn+oogckd1YRJ4TkSYRqQ96cCwPCk5OCr7/pog8IiL/DjwrIn1EZKmIvCwir2e3K5FvAL/PFm5U1Z3Ad4AfBMe6SkT+KUe2N4LCqIjI/xWRFUGPlFn7cH0MIw/L5DeM0jkCyCuiqaqfish7wCj82mdnA3OCek4HqeoKEfkpfsmab4lIf+AlEfl1sItjgLGqujlYxZwR7HMQ8IKIPKGlZUMXku1PIpIIjtkd3wqOnwCWi8ijqrqphGMaRrfYCsYwSkeAQjf77PgidpfYOBt4JHh/MnB5UMbnOaAWODj4bomqbs7Zz09F5DXg1/g9fYaWQbY98V0ReRV4ARgOjC7xmIbRLbaCMYzSeRM4M3cgaOg1HPiTqu4UkU0iMhaYAszObgacqaqrOs39En75/CznAIOBJlVtDypCl9rO9038xmK5+z8E+FhVt4pIivwHytpgm+PxKwwfE8j/3Gc4pmF0i61gDKN0lgJ1IjIdQEQiwA3AgsDnAb6Z7IfAAar6ejD2DHBxECSAiHyhyP4PwO9l0y4iJwCf+wyyPQj8lYj8TXCMBHAzfiFDgLUEZfpFZBx+P5TsMbcEyuVw/JbbhlEWTMEYRokEvpAzgLNEZDXwFrAL+FHOZovx2wQsyhmbC8SA14IQ47lFDvEg0CwiLfirmT9+BtlagdOB/yUibwEf4zv9Hww2eRQYEJjpLghkB3gaiAZmubn4ZjLDKAtWTdkweiAi8nXgZ8AJqvpulcUxeimmYAzDMIyKYCYywzAMoyKYgjEMwzAqgikYwzAMoyKYgjEMwzAqgikYwzAMoyKYgjEMwzAqwv8Hbgd7hYEXhdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Overall Qual',y='SalePrice',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../DATA/Ames_outliers_removed.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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