House Pricing Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ID-AI-0019\n",
    "## Submission Date: 11.01.2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\"House Prices: Advanced Regression Techniques\"\n",
    "This Project represents House Price prediction using ANN With 79 explanatory variables describing (almost) every aspect of \n",
    "residential homes in Ames, Iowa, this project challenges is  to predict the final price of each home. To predict the house price, the approach was several segments such as Data Analysis, Data Preprocessing, create Model, Hyperparatmeter tuning etc. The model was created by careful analysis of data visualization like data correlation, feature engineering, skewness reduction and other preprocessing to shape the model for the prediction. Final best score comes from Hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purpose of the Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The purpose of this Assignment is to predict the Sales Price for each house. The Assignment explicitly focuses\n",
    "on the Machine Learning Approach to the prediction, Data Preprocessing, Data Cleansing, Missing Data\n",
    "Handling, Outliers Detection and Dropping, Hyper Parameter Tuning and Accuracy Improvement.\n",
    "The target of this project is to implement ANN and predict the prices of the houses using the given dataset. \n",
    "The way i tackled the problem is :\n",
    "1.Analyze the dataset\n",
    "2.Apply various preprossesing methods\n",
    "3.Run MLPRegressor(ANN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn \n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "from PyNomaly import loop\n",
    "\n",
    "import warnings\n",
    "def ignore_warn(*args, **kwargs):\n",
    "    pass\n",
    "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Custom Metric Function for the competition.\n",
    "def rmsle(y_pred, y_true): \n",
    "    \"\"\"\n",
    "    Compute the Root Mean Squared Log Error for hypthesis y_pred and targets y_true\n",
    "\n",
    "    Args:\n",
    "        y_pred - numpy array containing predictions with shape (n_samples, n_targets)\n",
    "        y_true - numpy array containing targets with shape (n_samples, n_targets)\n",
    "    \"\"\"\n",
    "    return np.sqrt(np.square(np.log(y_pred + 1) - np.log(y_true + 1)).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "(1459, 80)\n"
     ]
    }
   ],
   "source": [
    "#load the csv file(Both train & test)\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>99.520548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>96.301370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>93.767123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>80.753425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>47.260274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>17.739726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>2.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>2.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>0.068493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing Ratio\n",
       "PoolQC            99.520548\n",
       "MiscFeature       96.301370\n",
       "Alley             93.767123\n",
       "Fence             80.753425\n",
       "FireplaceQu       47.260274\n",
       "LotFrontage       17.739726\n",
       "GarageYrBlt        5.547945\n",
       "GarageType         5.547945\n",
       "GarageFinish       5.547945\n",
       "GarageQual         5.547945\n",
       "GarageCond         5.547945\n",
       "BsmtFinType2       2.602740\n",
       "BsmtExposure       2.602740\n",
       "BsmtFinType1       2.534247\n",
       "BsmtCond           2.534247\n",
       "BsmtQual           2.534247\n",
       "MasVnrArea         0.547945\n",
       "MasVnrType         0.547945\n",
       "Electrical         0.068493"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#handling missing value in the dataset\n",
    "data_na = (train.isnull().sum() / len(train)) * 100\n",
    "data_na = data_na.drop(data_na[data_na == 0].index).sort_values(ascending=False)[:30]\n",
    "missing_data = pd.DataFrame({'Missing Ratio' : data_na})\n",
    "missing_data.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3c2d182550>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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k+1TVGUmem+RFSY6rqsclOT3JQ8fd35Pk3km+lOTiJI+5Ip95zlH//w5WvVybfu831rsE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c2d161550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of missing data \n",
    "f, ax = plt.subplots(figsize=(15, 12))\n",
    "plt.xticks(rotation='90')\n",
    "sns.barplot(x=data_na.index, y=data_na)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.ylabel('Percent of missing values', fontsize=15)\n",
    "plt.title('Percent missing data by feature', fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of         Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "5        6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6        7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7        8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8        9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9       10         190       RL         50.0     7420   Pave   NaN      Reg   \n",
       "10      11          20       RL         70.0    11200   Pave   NaN      Reg   \n",
       "11      12          60       RL         85.0    11924   Pave   NaN      IR1   \n",
       "12      13          20       RL          NaN    12968   Pave   NaN      IR2   \n",
       "13      14          20       RL         91.0    10652   Pave   NaN      IR1   \n",
       "14      15          20       RL          NaN    10920   Pave   NaN      IR1   \n",
       "15      16          45       RM         51.0     6120   Pave   NaN      Reg   \n",
       "16      17          20       RL          NaN    11241   Pave   NaN      IR1   \n",
       "17      18          90       RL         72.0    10791   Pave   NaN      Reg   \n",
       "18      19          20       RL         66.0    13695   Pave   NaN      Reg   \n",
       "19      20          20       RL         70.0     7560   Pave   NaN      Reg   \n",
       "20      21          60       RL        101.0    14215   Pave   NaN      IR1   \n",
       "21      22          45       RM         57.0     7449   Pave  Grvl      Reg   \n",
       "22      23          20       RL         75.0     9742   Pave   NaN      Reg   \n",
       "23      24         120       RM         44.0     4224   Pave   NaN      Reg   \n",
       "24      25          20       RL          NaN     8246   Pave   NaN      IR1   \n",
       "25      26          20       RL        110.0    14230   Pave   NaN      Reg   \n",
       "26      27          20       RL         60.0     7200   Pave   NaN      Reg   \n",
       "27      28          20       RL         98.0    11478   Pave   NaN      Reg   \n",
       "28      29          20       RL         47.0    16321   Pave   NaN      IR1   \n",
       "29      30          30       RM         60.0     6324   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1430  1431          60       RL         60.0    21930   Pave   NaN      IR3   \n",
       "1431  1432         120       RL          NaN     4928   Pave   NaN      IR1   \n",
       "1432  1433          30       RL         60.0    10800   Pave  Grvl      Reg   \n",
       "1433  1434          60       RL         93.0    10261   Pave   NaN      IR1   \n",
       "1434  1435          20       RL         80.0    17400   Pave   NaN      Reg   \n",
       "1435  1436          20       RL         80.0     8400   Pave   NaN      Reg   \n",
       "1436  1437          20       RL         60.0     9000   Pave   NaN      Reg   \n",
       "1437  1438          20       RL         96.0    12444   Pave   NaN      Reg   \n",
       "1438  1439          20       RM         90.0     7407   Pave   NaN      Reg   \n",
       "1439  1440          60       RL         80.0    11584   Pave   NaN      Reg   \n",
       "1440  1441          70       RL         79.0    11526   Pave   NaN      IR1   \n",
       "1441  1442         120       RM          NaN     4426   Pave   NaN      Reg   \n",
       "1442  1443          60       FV         85.0    11003   Pave   NaN      Reg   \n",
       "1443  1444          30       RL          NaN     8854   Pave   NaN      Reg   \n",
       "1444  1445          20       RL         63.0     8500   Pave   NaN      Reg   \n",
       "1445  1446          85       RL         70.0     8400   Pave   NaN      Reg   \n",
       "1446  1447          20       RL          NaN    26142   Pave   NaN      IR1   \n",
       "1447  1448          60       RL         80.0    10000   Pave   NaN      Reg   \n",
       "1448  1449          50       RL         70.0    11767   Pave   NaN      Reg   \n",
       "1449  1450         180       RM         21.0     1533   Pave   NaN      Reg   \n",
       "1450  1451          90       RL         60.0     9000   Pave   NaN      Reg   \n",
       "1451  1452          20       RL         78.0     9262   Pave   NaN      Reg   \n",
       "1452  1453         180       RM         35.0     3675   Pave   NaN      Reg   \n",
       "1453  1454          20       RL         90.0    17217   Pave   NaN      Reg   \n",
       "1454  1455          20       FV         62.0     7500   Pave  Pave      Reg   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "2            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "3            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "4            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "5            Lvl    AllPub    ...            0    NaN  MnPrv        Shed   \n",
       "6            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "7            Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
       "8            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "9            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "10           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "11           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "12           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "13           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "14           Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
       "15           Lvl    AllPub    ...            0    NaN  GdPrv         NaN   \n",
       "16           Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
       "17           Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
       "18           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "19           Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "20           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "21           Bnk    AllPub    ...            0    NaN  GdPrv         NaN   \n",
       "22           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "23           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "24           Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "25           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "26           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "27           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "28           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "29           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "...          ...       ...    ...          ...    ...    ...         ...   \n",
       "1430         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1431         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1432         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1433         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1434         Low    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1435         Lvl    AllPub    ...            0    NaN  GdPrv         NaN   \n",
       "1436         Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
       "1437         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1438         Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "1439         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1440         Bnk    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1441         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1442         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1443         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1444         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1445         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1446         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1447         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1448         Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
       "1449         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1450         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1451         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1452         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1453         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1454         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1455         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1456         Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "1457         Lvl    AllPub    ...            0    NaN  GdPrv        Shed   \n",
       "1458         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1459         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "\n",
       "     MiscVal MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0          0      2   2008        WD         Normal     208500  \n",
       "1          0      5   2007        WD         Normal     181500  \n",
       "2          0      9   2008        WD         Normal     223500  \n",
       "3          0      2   2006        WD        Abnorml     140000  \n",
       "4          0     12   2008        WD         Normal     250000  \n",
       "5        700     10   2009        WD         Normal     143000  \n",
       "6          0      8   2007        WD         Normal     307000  \n",
       "7        350     11   2009        WD         Normal     200000  \n",
       "8          0      4   2008        WD        Abnorml     129900  \n",
       "9          0      1   2008        WD         Normal     118000  \n",
       "10         0      2   2008        WD         Normal     129500  \n",
       "11         0      7   2006       New        Partial     345000  \n",
       "12         0      9   2008        WD         Normal     144000  \n",
       "13         0      8   2007       New        Partial     279500  \n",
       "14         0      5   2008        WD         Normal     157000  \n",
       "15         0      7   2007        WD         Normal     132000  \n",
       "16       700      3   2010        WD         Normal     149000  \n",
       "17       500     10   2006        WD         Normal      90000  \n",
       "18         0      6   2008        WD         Normal     159000  \n",
       "19         0      5   2009       COD        Abnorml     139000  \n",
       "20         0     11   2006       New        Partial     325300  \n",
       "21         0      6   2007        WD         Normal     139400  \n",
       "22         0      9   2008        WD         Normal     230000  \n",
       "23         0      6   2007        WD         Normal     129900  \n",
       "24         0      5   2010        WD         Normal     154000  \n",
       "25         0      7   2009        WD         Normal     256300  \n",
       "26         0      5   2010        WD         Normal     134800  \n",
       "27         0      5   2010        WD         Normal     306000  \n",
       "28         0     12   2006        WD         Normal     207500  \n",
       "29         0      5   2008        WD         Normal      68500  \n",
       "...      ...    ...    ...       ...            ...        ...  \n",
       "1430       0      7   2006        WD         Normal     192140  \n",
       "1431       0     10   2009        WD         Normal     143750  \n",
       "1432       0      8   2007        WD         Normal      64500  \n",
       "1433       0      5   2008        WD         Normal     186500  \n",
       "1434       0      5   2006        WD         Normal     160000  \n",
       "1435       0      7   2008       COD        Abnorml     174000  \n",
       "1436       0      5   2007        WD         Normal     120500  \n",
       "1437       0     11   2008       New        Partial     394617  \n",
       "1438       0      4   2010        WD         Normal     149700  \n",
       "1439       0     11   2007        WD         Normal     197000  \n",
       "1440       0      9   2008        WD         Normal     191000  \n",
       "1441       0      5   2008        WD         Normal     149300  \n",
       "1442       0      4   2009        WD         Normal     310000  \n",
       "1443       0      5   2009        WD         Normal     121000  \n",
       "1444       0     11   2007        WD         Normal     179600  \n",
       "1445       0      5   2007        WD         Normal     129000  \n",
       "1446       0      4   2010        WD         Normal     157900  \n",
       "1447       0     12   2007        WD         Normal     240000  \n",
       "1448       0      5   2007        WD         Normal     112000  \n",
       "1449       0      8   2006        WD        Abnorml      92000  \n",
       "1450       0      9   2009        WD         Normal     136000  \n",
       "1451       0      5   2009       New        Partial     287090  \n",
       "1452       0      5   2006        WD         Normal     145000  \n",
       "1453       0      7   2006        WD        Abnorml      84500  \n",
       "1454       0     10   2009        WD         Normal     185000  \n",
       "1455       0      8   2007        WD         Normal     175000  \n",
       "1456       0      2   2010        WD         Normal     210000  \n",
       "1457    2500      5   2010        WD         Normal     266500  \n",
       "1458       0      4   2010        WD         Normal     142125  \n",
       "1459       0      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of         Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0     1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1     1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2     1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3     1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4     1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "5     1466          60       RL         75.0    10000   Pave   NaN      IR1   \n",
       "6     1467          20       RL          NaN     7980   Pave   NaN      IR1   \n",
       "7     1468          60       RL         63.0     8402   Pave   NaN      IR1   \n",
       "8     1469          20       RL         85.0    10176   Pave   NaN      Reg   \n",
       "9     1470          20       RL         70.0     8400   Pave   NaN      Reg   \n",
       "10    1471         120       RH         26.0     5858   Pave   NaN      IR1   \n",
       "11    1472         160       RM         21.0     1680   Pave   NaN      Reg   \n",
       "12    1473         160       RM         21.0     1680   Pave   NaN      Reg   \n",
       "13    1474         160       RL         24.0     2280   Pave   NaN      Reg   \n",
       "14    1475         120       RL         24.0     2280   Pave   NaN      Reg   \n",
       "15    1476          60       RL        102.0    12858   Pave   NaN      IR1   \n",
       "16    1477          20       RL         94.0    12883   Pave   NaN      IR1   \n",
       "17    1478          20       RL         90.0    11520   Pave   NaN      Reg   \n",
       "18    1479          20       RL         79.0    14122   Pave   NaN      IR1   \n",
       "19    1480          20       RL        110.0    14300   Pave   NaN      Reg   \n",
       "20    1481          60       RL        105.0    13650   Pave   NaN      Reg   \n",
       "21    1482         120       RL         41.0     7132   Pave   NaN      IR1   \n",
       "22    1483          20       RL        100.0    18494   Pave   NaN      IR1   \n",
       "23    1484         120       RL         43.0     3203   Pave   NaN      Reg   \n",
       "24    1485          80       RL         67.0    13300   Pave   NaN      IR1   \n",
       "25    1486          60       RL         63.0     8577   Pave   NaN      IR1   \n",
       "26    1487          60       RL         60.0    17433   Pave   NaN      IR2   \n",
       "27    1488          20       RL         73.0     8987   Pave   NaN      Reg   \n",
       "28    1489          20       FV         92.0     9215   Pave   NaN      Reg   \n",
       "29    1490          20       FV         84.0    10440   Pave   NaN      Reg   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1429  2890          30       RM         50.0     7030   Pave   NaN      Reg   \n",
       "1430  2891          50       RM         75.0     9060   Pave   NaN      Reg   \n",
       "1431  2892          30  C (all)         69.0    12366   Pave   NaN      Reg   \n",
       "1432  2893         190  C (all)         50.0     9000   Pave   NaN      Reg   \n",
       "1433  2894          50  C (all)         60.0     8520   Grvl   NaN      Reg   \n",
       "1434  2895         120       RM         41.0     5748   Pave   NaN      IR1   \n",
       "1435  2896         120       RM         44.0     3842   Pave   NaN      IR1   \n",
       "1436  2897          20       RL         69.0    23580   Pave   NaN      IR1   \n",
       "1437  2898          90       RL         65.0     8385   Pave   NaN      Reg   \n",
       "1438  2899          20       RL         70.0     9116   Pave   NaN      Reg   \n",
       "1439  2900          80       RL        140.0    11080   Pave   NaN      Reg   \n",
       "1440  2901          20       RL          NaN    50102   Pave   NaN      IR1   \n",
       "1441  2902          20       RL          NaN     8098   Pave   NaN      IR1   \n",
       "1442  2903          20       RL         95.0    13618   Pave   NaN      Reg   \n",
       "1443  2904          20       RL         88.0    11577   Pave   NaN      Reg   \n",
       "1444  2905          20      NaN        125.0    31250   Pave   NaN      Reg   \n",
       "1445  2906          90       RM         78.0     7020   Pave   NaN      Reg   \n",
       "1446  2907         160       RM         41.0     2665   Pave   NaN      Reg   \n",
       "1447  2908          20       RL         58.0    10172   Pave   NaN      IR1   \n",
       "1448  2909          90       RL          NaN    11836   Pave   NaN      IR1   \n",
       "1449  2910         180       RM         21.0     1470   Pave   NaN      Reg   \n",
       "1450  2911         160       RM         21.0     1484   Pave   NaN      Reg   \n",
       "1451  2912          20       RL         80.0    13384   Pave   NaN      Reg   \n",
       "1452  2913         160       RM         21.0     1533   Pave   NaN      Reg   \n",
       "1453  2914         160       RM         21.0     1526   Pave   NaN      Reg   \n",
       "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1            Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2            Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3            Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4            HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "5            Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "6            Lvl    AllPub      ...                 0        0    NaN  GdPrv   \n",
       "7            Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "8            Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "9            Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "10           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "11           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "12           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "13           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "14           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "15           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "16           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "17           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "18           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "19           HLS    AllPub      ...                 0        0    NaN    NaN   \n",
       "20           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "21           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "22           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "23           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "24           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "25           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "26           Lvl    AllPub      ...               256        0    NaN    NaN   \n",
       "27           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "28           Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "29           Lvl    AllPub      ...               216        0    NaN    NaN   \n",
       "...          ...       ...      ...               ...      ...    ...    ...   \n",
       "1429         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "1430         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1431         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1432         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1433         Bnk    AllPub      ...                 0        0    NaN    NaN   \n",
       "1434         HLS    AllPub      ...               153        0    NaN    NaN   \n",
       "1435         HLS    AllPub      ...               155        0    NaN    NaN   \n",
       "1436         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1437         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1438         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1439         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1440         Low    AllPub      ...               138        0    NaN    NaN   \n",
       "1441         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1442         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1443         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1444         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1445         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1446         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1447         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1448         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1449         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1450         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1451         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1452         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1453         Lvl    AllPub      ...                 0        0    NaN  GdPrv   \n",
       "1454         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1455         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "1457         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "1458         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0      6    2010        WD         Normal  \n",
       "1           Gar2   12500      6    2010        WD         Normal  \n",
       "2            NaN       0      3    2010        WD         Normal  \n",
       "3            NaN       0      6    2010        WD         Normal  \n",
       "4            NaN       0      1    2010        WD         Normal  \n",
       "5            NaN       0      4    2010        WD         Normal  \n",
       "6           Shed     500      3    2010        WD         Normal  \n",
       "7            NaN       0      5    2010        WD         Normal  \n",
       "8            NaN       0      2    2010        WD         Normal  \n",
       "9            NaN       0      4    2010        WD         Normal  \n",
       "10           NaN       0      6    2010        WD         Normal  \n",
       "11           NaN       0      2    2010       COD         Normal  \n",
       "12           NaN       0      3    2010        WD         Normal  \n",
       "13           NaN       0      6    2010        WD         Normal  \n",
       "14           NaN       0      6    2010        WD         Normal  \n",
       "15           NaN       0      1    2010       New        Partial  \n",
       "16           NaN       0      6    2010       New        Partial  \n",
       "17           NaN       0      6    2010        WD         Normal  \n",
       "18           NaN       0      2    2010        WD         Normal  \n",
       "19           NaN       0      6    2010        WD         Normal  \n",
       "20           NaN       0      6    2010        WD         Normal  \n",
       "21           NaN       0      4    2010        WD         Normal  \n",
       "22           NaN       0      1    2010        WD         Normal  \n",
       "23           NaN       0      1    2010        WD         Normal  \n",
       "24           NaN       0      6    2010        WD         Normal  \n",
       "25           NaN       0      4    2010        WD         Normal  \n",
       "26           NaN       0      1    2010        WD         Normal  \n",
       "27           NaN       0      5    2010        WD         Normal  \n",
       "28           NaN       0      4    2010       New        Partial  \n",
       "29           NaN       0      5    2010        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1429         NaN       0      3    2006        WD         Normal  \n",
       "1430         NaN       0      4    2006        WD         Normal  \n",
       "1431         NaN       0     10    2006        WD        Abnorml  \n",
       "1432         NaN       0     10    2006        WD        Abnorml  \n",
       "1433         NaN       0      4    2006        WD         Normal  \n",
       "1434         NaN       0      2    2006       New        Partial  \n",
       "1435         NaN       0     12    2006        WD         Normal  \n",
       "1436         NaN       0      9    2006        WD         Normal  \n",
       "1437         NaN       0     10    2006        WD         Normal  \n",
       "1438         NaN       0      5    2006        WD         Normal  \n",
       "1439         NaN       0      5    2006        WD         Normal  \n",
       "1440         NaN       0      3    2006        WD         Alloca  \n",
       "1441         NaN       0     10    2006        WD         Normal  \n",
       "1442         NaN       0     11    2006       New        Partial  \n",
       "1443         NaN       0      9    2006       New        Partial  \n",
       "1444         NaN       0      5    2006        WD         Normal  \n",
       "1445         NaN       0     11    2006        WD         Normal  \n",
       "1446         NaN       0      5    2006        WD         Normal  \n",
       "1447         NaN       0     10    2006        WD         Normal  \n",
       "1448         NaN       0      3    2006        WD         Normal  \n",
       "1449         NaN       0      4    2006        WD         Normal  \n",
       "1450         NaN       0      5    2006        WD         Normal  \n",
       "1451         NaN       0      5    2006        WD         Normal  \n",
       "1452         NaN       0     12    2006        WD        Abnorml  \n",
       "1453         NaN       0      6    2006        WD         Normal  \n",
       "1454         NaN       0      6    2006        WD         Normal  \n",
       "1455         NaN       0      4    2006        WD        Abnorml  \n",
       "1456         NaN       0      9    2006        WD        Abnorml  \n",
       "1457        Shed     700      7    2006        WD         Normal  \n",
       "1458         NaN       0     11    2006        WD         Normal  \n",
       "\n",
       "[1459 rows x 80 columns]>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Cell Report\n",
    "\n",
    "1. To see the all parameter and their value \n",
    "2. It helps to visualize the data type and missing value\n",
    "3. In the next step data will preprocess and fill by making a function.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dataPreprocess(df):\n",
    "    df = df.fillna(df.mean())\n",
    "    obj_data = df.loc[: , df.dtypes==object]\n",
    "    Str_cols = obj_data.columns.tolist()\n",
    "    for col in Str_cols:\n",
    "        df[col] = df[col].astype('category')\n",
    "        df[col] = df[col].cat.codes\n",
    "    df['Age'] = df.YrSold - df.YearBuilt\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell Report\n",
    "\n",
    "1. Now, I preprocess the data using fillna function by mean. \n",
    "2. Then I convert all the file types into object to avoid future type error. Then making a list parameter are string.\n",
    "3. By using a for loop I have to convert category to numeric data.\n",
    "4. After  that, I made a new colum 'Age' to find out the year difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = dataPreprocess(train)\n",
    "test = dataPreprocess(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  LotShape  \\\n",
       "0  1461          20         2         80.0    11622       1     -1         3   \n",
       "1  1462          20         3         81.0    14267       1     -1         0   \n",
       "\n",
       "   LandContour  Utilities ...   PoolArea  PoolQC  Fence  MiscFeature  MiscVal  \\\n",
       "0            3          0 ...          0      -1      2           -1        0   \n",
       "1            3          0 ...          0      -1     -1            0    12500   \n",
       "\n",
       "   MoSold  YrSold  SaleType  SaleCondition  Age  \n",
       "0       6    2010         8              4   49  \n",
       "1       6    2010         8              4   52  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Outlier Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eqUjaOJ0r1m3h8nVb6A2d5+2WYFBOYZKx7+39Y1Y/LbXiaRyc02YGGDngTJta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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c2b50bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's explore outliers wrt GrLivArea in the training dataset\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x = train['GrLivArea'], y = train['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c2d151908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Deleting outliers\n",
    "train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n",
    "\n",
    "#Check the plot again\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(train['GrLivArea'], train['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell Report\n",
    "\n",
    "1. Documentation for the Ames Housing Data indicates that there are Outliers present in the Training Data.\n",
    "2. In the first Scatter Plot, at the bottom right, there are two instances with extremely large GrLivArea(>4000)\n",
    "   that are of a very low SalePrice of less than 300000. These are huge Oultliers. So, they were deleted.\n",
    "3. There are probably other Outliers in the Training Data. However, removing all of them may badly affect the\n",
    "   models if ever there were also Outliers in the Test Data. That's why , instead of removing them all, the models\n",
    "   should be made robust on them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Target Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 180932.92 and sigma = 79467.79\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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xNye45sUCT208bXdXQL219zZGCHAZMAIoAD4UkY2q+uFZDVVfAF4ASElJafUX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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c26581be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uXTWFjJQ2JRuRfIpKlheOaWQQj/EenZnFGTRQz3zyX7Ksu/qlXCjZiOyCLl3g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\nNHvv7m2IKd0WopGLqOfSJWo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c1f4d8b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SalePrice is the target variable we need to predict\n",
    "#let's do some analysis on it\n",
    "sns.distplot(train['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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U+ElNJxKRR0Rkp4jszMzMrKmIUrU6lGFt7gnz96jcdvzbZC7nZTeLDvCqIoK8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\n95EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c2d242eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jiZn9S9I1ECw/LslneruM69w5fZIA2Jif6MsQLuMuDHEz1zCEvvzCxqvPad8e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\n4gnDOedcJP8PITa6AcmuMicAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c2d2ec668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "sns.distplot(train['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Cell Report\n",
    "\n",
    "Most of the algorithms by default assume that the target follows a normal distribution. If that is not the case, then, the accuracy falls. In this section, we will take a look at the 'SalePrice' from a probabilistic viewpoint and transform it if necessary.\n",
    "\n",
    "1. the left skewness is removed.Log Transformation was applied to the Target Variable because of its high skewness.\n",
    "\n",
    "2. Log Transformation greatly reduced the Skewness of the Target Variable.\n",
    "\n",
    "3. My target feature 'saleprice''s probability graph is in a straight line which will give me the good accuracy.\n",
    "\n",
    "4. Skewness of the Target Variable was graphically represented and it was found to be right(positive) skewed.\n",
    "   This is verified by the fact that the Mean of the Distribution is greater than the Mode.\n",
    "   \n",
    "   \n",
    "   The Data now appears more Normally Distributed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Data Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find most important features relative to target\n",
      "SalePrice        1.000000\n",
      "OverallQual      0.821405\n",
      "GrLivArea        0.725211\n",
      "GarageCars       0.681033\n",
      "GarageArea       0.656129\n",
      "TotalBsmtSF      0.647563\n",
      "1stFlrSF         0.620500\n",
      "FullBath         0.595899\n",
      "YearBuilt        0.587043\n",
      "YearRemodAdd     0.565992\n",
      "TotRmsAbvGrd     0.537702\n",
      "GarageYrBlt      0.500842\n",
      "Fireplaces       0.491998\n",
      "MasVnrArea       0.433353\n",
      "FireplaceQu      0.430091\n",
      "Foundation       0.412203\n",
      "BsmtFinSF1       0.392283\n",
      "GarageCond       0.369805\n",
      "GarageQual       0.353708\n",
      "LotFrontage      0.352432\n",
      "CentralAir       0.351598\n",
      "WoodDeckSF       0.334251\n",
      "OpenPorchSF      0.325215\n",
      "2ndFlrSF         0.319953\n",
      "HalfBath         0.314186\n",
      "PavedDrive       0.304682\n",
      "Electrical       0.292385\n",
      "LotArea          0.260544\n",
      "SaleCondition    0.248921\n",
      "BsmtFullBath     0.237099\n",
      "                   ...   \n",
      "Condition2      -0.000031\n",
      "BsmtHalfBath    -0.005124\n",
      "Utilities       -0.012630\n",
      "Id              -0.017774\n",
      "MiscVal         -0.020012\n",
      "BsmtFinType1    -0.022069\n",
      "MasVnrType      -0.034435\n",
      "SaleType        -0.036668\n",
      "OverallCond     -0.036821\n",
      "YrSold          -0.037151\n",
      "LowQualFinSF    -0.037951\n",
      "BldgType        -0.072525\n",
      "LotConfig       -0.073222\n",
      "MiscFeature     -0.073805\n",
      "MSSubClass      -0.073969\n",
      "Alley           -0.091126\n",
      "Heating         -0.139911\n",
      "KitchenAbvGr    -0.147534\n",
      "EnclosedPorch   -0.149029\n",
      "BsmtExposure    -0.159264\n",
      "MSZoning        -0.188861\n",
      "Fence           -0.192788\n",
      "GarageType      -0.240765\n",
      "GarageFinish    -0.261309\n",
      "LotShape        -0.278034\n",
      "BsmtQual        -0.376385\n",
      "HeatingQC       -0.426052\n",
      "KitchenQual     -0.537038\n",
      "ExterQual       -0.587214\n",
      "Age             -0.587767\n",
      "Name: SalePrice, Length: 82, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Find most important features relative to target\")\n",
    "corr = train.corr()\n",
    "corr.sort_values([\"SalePrice\"], ascending = False, inplace = True)\n",
    "print(corr.SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3c2d507f98>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0/cOcc669TZ+7+xyuj+GCp8PpOxfJuUf2JHcm+TDJl9LfO6XxDb7z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PkTxCmuSP4p3jST5I3xdg+Jch3HcRzHcZydsNc8sseO+U4BoBSRwxIAPAXgfWZmJD8L4JsA3s4G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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c265e9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation map to see how features are correlated with SalePrice\n",
    "corrmat = train.corr()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(corrmat, vmax=0.9, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Cell Report \n",
    "\n",
    "1. Heatmap and the most Correlated Features with the Target variable were represented with the Correlation\n",
    "   Matrix.\n",
    "2. Some Features that have a high Correlation(>0.8):\n",
    "4. These are some important observations which can be exploited later on.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing Ratio]\n",
       "Index: []"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#handling missing value in the dataset\n",
    "data_na = (train.isnull().sum() / len(train)) * 100\n",
    "data_na = data_na.drop(data_na[data_na == 0].index).sort_values(ascending=False)[:30]\n",
    "missing_data = pd.DataFrame({'Missing Ratio' : data_na})\n",
    "missing_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Cell report\n",
    "\n",
    "1. counted null value of train data and divided by the length of train and multiply with 100 to get % value.\n",
    "2. In Descending order, create a dataframe to show 30 missing ratio.\n",
    "3. There is no missing ratio.Now I can go to the further step.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def outlier_data(df):\n",
    "    \n",
    "    m = loop.LocalOutlierProbability(df).fit()\n",
    "    scores = m.local_outlier_probabilities\n",
    "    #print(scores)\n",
    "    df['OutlierScores'] = scores\n",
    "    df = df[df.OutlierScores<0.05]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = outlier_data(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1362, 83)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = train['SalePrice']\n",
    "x = train.loc[:,(train.columns !='SalePrice')&(train.columns !='Id')&(train.columns !='OutlierScores')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Cell Report\n",
    "\n",
    "1. Making a outlier_data function to check the probability of outlier value\n",
    "2. create a new column 'OutlierScore' which can contain outlier value.\n",
    "3. If the outlier value over 0.05 , It will be removed.\n",
    "1. Based on the investigation shown above,'Id', 'saleprice', 'outlierscore' columns were dropped as it would be 'x' data.\n",
    "2. To take only 'SalePrice' column it create 'y'.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Scaling and Normalize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x)\n",
    "x = scaler.transform(x)\n",
    "#x = normalize(x)\n",
    "y = np.log(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1021, 80)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_x = test.loc[:,(test.columns !='Id')]\n",
    "test_x = scaler.transform(test_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1021, 80)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_size = X_train.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Cell Report\n",
    "\n",
    "1. Fit x and normalize y\n",
    "2. split the value into the  train and test\n",
    "3. Check the shape of train and test to confirm the equality for build a model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Model (Neural Network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 20 10 5 3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "last_layer =1\n",
    "\n",
    "FH = (feature_size+last_layer)//2\n",
    "SH = (FH+last_layer)//2\n",
    "TH = (SH+last_layer)//2\n",
    "FoH = (TH+last_layer)//2\n",
    "FiH = (FoH+last_layer)//2\n",
    "print(FH, SH, TH, FoH, FiH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell report\n",
    "\n",
    "\n",
    "#### Determining the number of node in a hidden layer: Thumb Rule\n",
    "\n",
    "\n",
    "A Thumb Rule recommended by Geoff Hinton was applied for choosing the best number of Hidden Layers. Number of Neurons on each Layer was tuned according to the Thumb Rule that says, 'Number of\n",
    "Neurons on each Layer should be somewhere between the size of the input and output layer, potentially the mean'.\n",
    "\n",
    "There is no hard and fast rule.we have some rule of thumb for approximation.\n",
    "First Hidden (FH) layer number is calculating by the sum of feature size and last layer, then divided by 2 to get the hidden layer number.\n",
    "\n",
    "Second Hidden layer Number is calculating by the sum of First Hidden layer number  and last layer number , then divide by 2. \n",
    "\n",
    "In this way calculated 3rd, 4th and 5th hidden layer number.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Reg = MLPRegressor(hidden_layer_sizes=(FH,SH,TH),max_iter=1000,verbose=False)\n",
    "activation_options = ['identity','tanh','logistic','relu']\n",
    "solver_options = ['lbfgs','adam']\n",
    "alpha_options = [0.0001]\n",
    "tol_options = [0.0001]\n",
    "learning_rate_options = ['constant','adaptive']\n",
    "param_grid_MLPReg = dict(activation = activation_options,\n",
    "                       solver = solver_options,\n",
    "                       alpha = alpha_options,\n",
    "                       learning_rate = learning_rate_options,\n",
    "                        tol=tol_options)\n",
    "gridMLPReg = GridSearchCV(Reg,param_grid=param_grid_MLPReg,cv=10,scoring ='r2',verbose=2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Cell Report\n",
    "\n",
    "1. A Model was represented with Neural Network. Scikit Learn's ANN was used for this Traditional\n",
    "   Model. Hyper Parameters were tuned.\n",
    "   \n",
    "2. A 10 Fold Cross Validation was run on the Train Data and the Average Accuracy Score(R2 Score) was shown.\n",
    "2. 'mean_squared_logarithmic_error' was used as loss function.\n",
    "\n",
    "3. This is a Basic Model. So, Hyperparameters and Layers were chosen by rule of thumb.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 16 candidates, totalling 160 fits\n",
      "[CV] activation=identity, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 \n",
      "[CV]  activation=identity, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 -   0.4s\n",
      "[CV] activation=identity, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  activation=identity, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 -   0.2s\n",
      "[CV] activation=identity, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  activation=tanh, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 -   0.3s\n",
      "[CV] activation=tanh, alpha=0.0001, learning_rate=constant, solver=lbfgs, tol=0.0001 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  activation=logistic, alpha=0.0001, learning_rate=constant, solver=adam, tol=0.0001 -   0.5s\n",
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     ]
    },
    {
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     "text": [
      "[CV]  activation=relu, alpha=0.0001, learning_rate=constant, solver=adam, tol=0.0001 -   1.0s\n",
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      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=lbfgs, tol=0.0001 -   0.3s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   0.9s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   0.9s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   1.0s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   0.6s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   1.1s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   1.0s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   0.9s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   1.1s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   1.4s\n",
      "[CV] activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 \n",
      "[CV]  activation=relu, alpha=0.0001, learning_rate=adaptive, solver=adam, tol=0.0001 -   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 160 out of 160 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(40, 20, 10), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'activation': ['identity', 'tanh', 'logistic', 'relu'], 'solver': ['lbfgs', 'adam'], 'alpha': [0.0001], 'learning_rate': ['constant', 'adaptive'], 'tol': [0.0001]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='r2', verbose=2)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridMLPReg.fit(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Summary of Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation': 'relu',\n",
       " 'alpha': 0.0001,\n",
       " 'learning_rate': 'adaptive',\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridMLPReg.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#  Cell report\n",
    "\n",
    "1. Grid Search was run and best Optimizer was found.\n",
    "2. First the Train Data was fitted to the Model and get the best score.\n",
    "3. find out the best combination of the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8047987008833195"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridMLPReg.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_y = pd.DataFrame()\n",
    "test_y['Id'] = test['Id']\n",
    "test_y_grid = pd.DataFrame()\n",
    "test_y_grid['Id'] = test['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_y_grid['SalePrice'] = np.exp(gridMLPReg.predict(test_x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Cell Report\n",
    "\n",
    "1. By using the best model get the best score.\n",
    "1. create a new data frame.there is two parameter 'Id' and 'saleprice'\n",
    "2. Then all the Train Data were fitted to the Model and Predictions were Inverse log1p Transformed\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Sample Submission for kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_y_grid.to_csv('submission_grid-1.csv',index=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "## 4.2 kaggle Score\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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i/Fv4neOdS0uTwxpfjq7sRYaHMUa4g+ywGJ7uYISkck5I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Nz+/Vrwx/wTkGp67oPhe9uPFEHizX9LN/DdQ6Z5mj2cjpvSCSf/AHKpcQYJw5+cn/VH\nNObl5P6+E+VzHsG6m7cHb619AX37LPg34f8AwL8C+LvGHiLWre68ZXOoWI06zgj3wfZbvyJH3v8A\nwf8AxyuD/aI+BDfs7/GO68O3l79u01UgvrW8h/5eLWf50f8A66bK7sPmVGtPkp/3v/JfiPPxmR4z\nDx5q393/AMm+E872g9D0pOQK+uP2ifg14D8ZeLPgD4d0P+1NLuvGWiaRavNsT/UTTSJ57/8ATeuU\n8QfsgeE7i8+Jmi+HPEms6p4i+HEE9xNHJDHHb3/kzbJI0/j/AHdcdHPqDjzT/r3uU9DEcK4yEuWH\nvfD/AOTR5j55sNPm1Kfy7eG4nk+/sRPMqH7xr68/Z1/ZSm8MfEfwlb6d4p1DSPEfiTwXe67ep9lj\nk+xwbPkh/wCBpXA6V+y74P0z4JfDfxl4i17WI5PiFe3VilvZwp/o7wXXkb/n/go/t7D+25P6+1/8\niH+qeN9j7b+vs/8AyUTwHLY/3afaWst/cJDDDJNPL9xETzN9fQ+s/sMx/DzxH8SJ/Fet3Fv4Z+Hk\n0dp9os4/9I1Cef8A1Eab/wDYruvgz+zj4Z+GXxy/Z48WeG9avta0v4h6hdyCO7hSP7J9lZE2D/to\n7/8Afuitn2GVPnp7/wD2vN/6STHhXHxf76PL/wDtcv8A6UfHs6SLK6MmySP76PUZNdZ8aBn41+Mf\n+w1ff+j5K5Uffr1MPU9tRhM8HEYd0q0qX8o2iiiug5QooooAKKKKACiiigAooooAKKKKACiiigAo\noooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP25\n/wCCR3/KPrwD/u6j/wCnC6r6Pr5w/wCCR3/KPrwD/u6j/wCnC6r6Pr+Wc+/5GWI/xz/9KP774R/5\nEmE/69Q/9JPIP+Cg3/Ji/wAYv+xO1b/0kmoo/wCCg3/Ji/xi/wCxO1b/ANJJqK/K+LP48P8ACf2D\n4G/8izEf9fF/6Sg/4J8/8mM/B/8A7E7SP/SSCvxj/bI/5O/+J3/Y06n/AOlT1+zn/BPn/kxn4P8A\n/YnaR/6SQV+Mf7ZH/J3/AMTv+xp1P/0qev6D8Jf48/8ABH9D/P8A+kt8X/cWoeZUUUV+7n8hhQDg\n0UUAexfBn46aP4A/Zq+KnhO8S4/tLxolillsT93+4eR331vW3xp8D/F74L+AfDvjS41TSr74fzPB\nHNZ2v2iPULF337P9iSvAMYOKfK2VVf7vevMqZXQnPn+3z83/AJLyn0NLiDFQoxw/2eXl/wC3ebm/\n9KPp7Wv2yPD/AMZ9R+N1v4oS88P2PxQfS3srq2g+0fYP7Of9wjp/00St/Qv2yvh74H+J3wZutM/t\nu40f4c6NfadePNafvJ3nD/Oif8Dr5CV2VWz+tBVnVcCuSXD+GlHk+x/9ry/+knW+K8Y5Xly8/wDN\n/wBve0/9KJtavlvddurlfuXEzyf+P19P+A/2nvAPwvttVv8AR9Q8YSWuqeG30l/CV48k9n9qdNm/\nzpP+Wf8AHXyt1NKPlrvxeXU8RTVOZ5eW5xWwdZ16PxM+pPCP7R3w517VPgrr3iS61ux1D4U2kNpN\nYW9p5gvzBN58ciPSn9pj4f8AjrwT8aPD+uXWuadF8QPF8mv6Zcw2nmeQnnzSR70/4En518uNIx2+\n1K0jMGzXFPIKE9f6+Lm/9KPS/wBaMVeL5Y/1H2f/AKSfW3gv9uTwn4FvtG0G1/tSbwr4X8HahoVr\ncvB+8vLq6T7+z+COvJ7X45aTF+xBqXw8dLz+3rjxnH4gRvL/AHHkfZfI+/8A399eQ8Nb+6mhj+63\nfxNxQsiwid3vp+Eub9TSXF2YShyL+9H/AMCjGP8A6TE+um/bi8IDxf8Aa/L1Tyf+FaJ4U/1H/L15\nOz/viuf+An7R/gP4N6p4T8RWureMNNn0XTpINZ8ORvJPZ63P+8+TzH/dxo/yb08uvmNZGX8KNrP+\nFQuHcLySp/19r/5In/WrGqr7aXL/AFy//Io+nfgl+0r4B+H+v+HfE9vqHizwxqWl3V1Pq2iWbyXG\nn6ujv5iIn/LNP3fyVLp37d2l+EvDHw5uNH06SHVvCPi/UNfnshH/AKOIJ5DmFJP+uZevl8yKw+7T\nduelXUyHCzlzVP6/rmCnxfmFKPLT90+kL/49eAfhZD8UNS8Ezaxfaj8SLWfTILa5tfs8ej2s7+ZP\n8/8AH/cjrbP7ZfhU/tg/DHx4E1D+x/B+j21henyP3m9LWSN9n518qfMF29m7UplYLt7L2olkGFf5\nfLl5Slxbj0vd5fi5v/Jub/0o6nTfFmin4yrrGqWd3e6DJqn2u5t4pvIuHh3/AMD/AMD179dftgeG\n/hj8O7rSfDmq+IPGUknie11rTE1uP/kDpA/mbN7/AH5JPuV8ssNkjLSJjy2PftXRiMroV+T2n2Dg\nw+fYqhKc4fa/9uPe/i98QPhP498UeN/GK/8ACQX+v+KIXn07SXg8iPTL6f77vN/Gkf8Acrsr79rb\nwH4i+OmifF6+GsL4003TIRdaMtp/o95qMMPlpN538Ef3Hr5S3tHIezUvzb1/vGuf+wcPy8vNL/7T\n+X8DpXEmJjKT5I+973/b3xcx7d8dP2mbH4lfA/4c6VZx3EfiDwve31/e70/d755/MTZXbfFX9qvw\nH8X/AB5qHjrVNT8cJqGraXHFN4Zs7qSC3j1DZ5G9J0f/AFHyb9lfLIiJbHpSs6nnFP8AsPC/8u/7\n3/k3xGFDiLFRk7+9fl/8l+E+nP8AhpD4e/EvW/hZ4m8VXOuWOufD+CC0vbO2tfMj1RIH8yCRH/gr\nyL9pj4m2Pxd/aM8WeKtH+0LY65qc99a+cnlybHevPwNxoVtpyP4a2w+UUKNT2lPtb/wL3h4/iHE4\nuh7Cp3j/AOSx5Yn1Hd/tL/Dn4ifFXwf8SvFB1mPxP4dsoU1DS4bXzI9TurVP3Enn/wAG/wDjryn4\nzeNvDPxSt5PFxvNY/wCE98Qa1Nd6vZtCn9n28Dv8mx/79eaSTMy7TQ+VQLjmlh8poUZ88PT5DrZ9\nXrQ5K0Y+9+f8x9DftMfts6r4o/aT8UeKvh3rWraLpfiC2tYZVMaJJOkcCR7HT/fQ1J8Sf23pviF8\nDvhv4fvprjWLzw/qE13r1teQJ9n1CPz0kjT/AMcr5zIMZo+7g1nTyPBQhCCj8A63EuPqTqycv4p9\nLeCvjn8Lfgt8VNe+InhNdZbUtQsp49J0B7Xy7fTJ7pNkied/HGm99n/AKyJPjP4E+Nnwh8C6J44u\ndU0fVfArvaJc2dr9o/tCxd9/l/7EleBSyMyhW/hojdojuXHy045JQUufn9/+v/kio8TYpKUOWPJL\nm937Pvcv/wAij6Q8VftX+Dvjn8d/FGpeOPDrDwzrWipo+kOkf2i88O+Qh8iZP9v+/j+/UfhPxv4N\n1j4X6H8G9G1S8uNO8UeIYdS1rWr+H7HHaIn9xHr5xeTefrRJH5dT/YtGEPZw/rlIlxBiZTnOr8Uu\nb/yb4jd+J1hpHh34j6xa+Gby4utHs750srmb/WSIj/fr6z8d/wDBSfR9c8bfDTVNPtbuFLO4OpeM\n0EXl/b7mS1jtJtn+x5aPXxfGwUU3oa1xWU0MVyOv7/J+pGB4gxOClU+qe7zn1tcftw+F7j9qjxp4\nmW31C08L3ng658MaFbxx/vLffCET5P8AlnXmvgz9oPRdA/ZFh8Eyrd/2xH46j8R/JH8n2WOBE/77\n+SvF0IiRW/vZzTQQNw/KopZJhIODj/d/8l/4c7K3F2YVJOc/73/k3uyPqr4r/teeDfjVqXxQ0HUm\n1bTvDvjPWYNc0rUEg8yS0ngjCbHh/uSfPXN+BPjZ8NvhDqPji08N2+sfYdc+Hl74ZS8mH7zUNRnC\nDz9n/LNPkr56LtIvX7tDbkLD86zjkGHjD2cfg/4aJnU4lxk6vtp8vN/9tzf+3HqfiH4zaXqn7G3h\n3wJEtx/a+j+JLnVZm8v935LwIiVofDX49aL4S/Y08beBbyG4fWPEGs2WpWoSP92Uhxv3vXjYbgr6\n0Z3YWu2eW0JU/Zf3+f8A9uOGnnOJjU9r/c5f+3eXlPqR/wBp7wL4J8FeOrbw/qHivUtL8YaKlha+\nGdUEk9vpc+yP5/Ok+/5fz7K8u/ZQ+M+l/BjV/GkurLcMmv8AhHUNCtfJj8z99MmxP+AV5aw8ptvW\nldNq7vWuenkuHjTnS6TNqnEGKnWpVn9j4T374E/td2PwP+BfhfTLa3uLjXNB+IY8VSRvH/o8lr9i\nMGzf/wA9Oa0tQ/aF8D/DXTPifeeC7rWL7VfiWj2iQ3Nr5cekQO+9/n/j/uV83LJsXbikVvKO7rU/\n2FhHN1JLrf8AU2o8UY2nCEIv4PhPYv2iPin4Y+MHw8+Ht1Z3l/D4i8J+HLXw/dWD2v7uTyHf98j/\nAPA6674SftIeE9G/Z70Lwpf6jrnh290nXX1bUG0yD59bh52QvJ/B9z9K+bz83NA+XmtJZPh5UvZL\n+bmOf+3q/wBZ+tfb5eU+vPjV+214L8ey/G6bTV1TzPiXoul2mnwzQf6ie1f543evI/i78cdI8ffs\ntfCvwfZrcf2r4M/tH7bvT92PPn8xNleQlgIPcmgDMO7+Jazw+SYbDRiofZf/ALby/wDpJ3YrizH4\nhSU/tKUf/Apc3/pR7t8Ov2j9B8I/szaH4SulvP7U0/xza+IZNkf7vyI//Z67jTv21PClr+2l8U/i\nA66n/YnjDSLqws18j955jwRom8f8Ar5QB3f8CoR/l29morZBhqs5VJ/a5v8Aybl/+RObC8SYvD04\n0o/ZlGX/AIDzf/JM+lf2e/j7osPwV8C/D1vtEeuR/EKy1Z38v/R/I3p/HXa/FL41+BP2eP2wfip4\n10W51i98VSPqdlZae8H+j293Nvgefzv44/vvsr44E7edG0beW0X3HSi4u2nneSR5JJX++7/8tKxl\nkNGVSVT+bm51/i5f/kTtp8VYiGHjRlH3o8vL/d5ebl/9KPffGX7W9jbT/A7UtChuJNQ+GOn/AGe9\nSaPy43fzt/yf8Arb1f8AaT8AeB/EnxM8WeEH1ifXPiHpl1pUFhc2vlx6Ol1/r33/AMf+xXzGOR/u\n0Fu9a/2DhLWf97/yaXMcdLibG0/h/u/+Sx5Y/wDkp9QWn7bGkw/sML4Ja2vP+E+gtZvD0F95fyf2\nQ88c+zf+UdSar+1X4P8Aijovgm+8U6v4v0i/8KaYdN1DStKd449YSH/UbHjf9z/cr5cdsbWHWnTs\nJZFI7io/sDCc3ND+ZzNpcVZg4RjN+7y8v/gJ7T45/aD0PxN+yppfg+1iv4dTs/GV3rzRzP5kcdrK\njIieZ/y0f5q9Ctv2zvCcX7cOg/ETbqh0PS9Dh06ZPI/0jelqYfuf79fKMhw2P7tOkO9d1aSyPCyj\nf/H/AOTnGs+xSlGT/u/+SS5on09J+1T4T+LXg3wDZ+KNb8WeHZ/BG+wuodK3+XrGnb98f+rf5H/g\nrlvEX7R/hzWf2ZvG/hK1t9UtL7xB4ng1ayhuZnu/LtU/ged/vvXhBbnI7UA859alZDhYL+v5uY3x\nHEmMrP2k/wCXl/8AJeX/ANJPrbw9+214Psfj78M9duIdU/sfw34Lfw3qmyD95G7wyJvT+/8Afr5q\ns9Xtvh38UbXUtBvJNStdHuoLu1uZoPs/mbPn+5XPYyP939aPuGujB5TQw8nOHp/5NKX/ALccOKze\nvXpKlP8Ar3Yx/wDSYxPqu6/a18CeG/i/4w+KWiSax/wl3izSJrSHS3tf3FhdTpsefzv40jrzr9pz\n4/6P8YvhN8I9E05bz7d4J0J9N1HzY/LjeZ3D/JXjYHncd1FCyr9nK4+bOc1jRyPC0qkKtPeH/wAj\ny/8Atx6NbiTGVaE8PPafxf8Ab0oy5v8AyWJ9Tf8ABMPxEfAfirxVqWvWH2rwP/ZDz3rzf6vz7V/P\ngT/f8yuc8NftV6R8RPAPxO8JePpLy3tPHmrJ4ltdRtoPtEmn3qP/AHP40kj+SvBrbWLqLT5LUXNw\nlrL9+FH/AHb1VibyXWQ84PSprZLTr1515/3f/JSsNxLWw+HhhqP2Ob/t7mjy8v8Ah+L/AMCPqzwT\n+254Z+HXj74e6bZ2uqXXgrwPoV7o0ly8fl3F5Je/6+fZ/vv9yub8WfGT4f8Ah39j/WPh94cv9Y1X\nV9Y8TQa49xc2P2eDYiOmyvnkpJcR7sfKuaJGLQRn+LJ5qFkeH5+f+vi5v/SjaPF2OjCy5ftR/wAP\nNHl/9Jieq/tw/GjSv2hv2o/FnjHQo7hdL1h7QwG4j8uT93awQf8AoaV1ngn42+BNZ/YvX4a+I73W\nNL1RNdfVoLy2tftEcfyfc2V89SfMN1DHjbXZ/ZVB4aGF/k5f/JTy6efYmOLq4xfHV5ub/t74j6ys\nv27tAsvHcWiRx6xD8P4fBKeBI72H93qESoxb7d/103fwVxdt8U/hnpeqeGbO/vvHniaO28+TVNVf\nUJ4JI3f7nkQ7/wDln/5ErwPLkY/vU0lifrXNTyDCQ/hndU4qxtVWly/1y/8AyJ9Oat+1z4f8NeEP\nhxodrqWueNJPBvjGDxO+pajB5clvAj/8eSfx7P4/9+vJP2lde8N+MPjD4h17wzqV5fWXiC/udRdL\nm1+zyW/nzvJs/wDH68+B2UmeMVth8nw9Gp7Wn/XU5sdxBicXR9hU+H3f/JT6p+C/7UXgH4L6paa9\npd94vWGLQnsb3wfcu9xZ387p5f33/wCWf8eysDxF8aPhv8drT4fap44k12y1HwbocWgahY2sPmR6\nxDbEmF0f+B38yTfXzuszJ+HtSHdJ+FZf2DQ9p7T7f/7X/wAkdH+tGK9h7CUY8v8A+z/8ij6I1D45\nfD/40fCDwr4Z8UPq/h3/AIQfUJ/sLWcH2v7Rpzyb/I/66R/362fHf7dOl/FDw/8AHr7ZZ3ljdfEi\nHR7TRbZP3kdvBYv/ABv/ALlfMEbBH3dcdqRX2oVxy3Q+lP8AsLC/1/ijL/0qJVPi3Hw+D+vdlH8I\nyPpHQf2kvDPj/wCFHgHRfFWteLPDk/gXfaP/AGW8nl6xau+/Z+7f5JP4N9ea/EXUvAfizw5rWr6b\nN4ktfE11q+bGwuZPtFull/tzP+8d684adpOO3pQpYjP92taOUwpz9pT0OTEZ5Wr0vZ1oxf8A+zyx\n/wDAT2H9n348ab8KPgx8UNDumu49S8XaSllYvDH/AMtN5f5/zrq/A/7W+j3Hwn8Lr4mS8fx18M9T\nhvvDOoom83lqj7/sU5/uV85f8sfxpx+eLd/EKWIyfD1Kk6j7/wDA/IMFnmKw0FCHwe9H/wACPpD4\nq/H3wF4r1Txr4lt9Y8cX+oeKv9K0/RHuXt7fSLqb55977/3yRyb9iVq+K/2qfBPxV1nR/FPiLVPG\nlne6foyWN94e025e3t9QnhTYkiTRv8iV8tLMyEei9qHkbJ/2uorGOQYdQR2f61YyVScuWPv/APyX\nN/XQ+tfiJL4H1/8AYW+AaeKNW1PSri3ufEM8Ettb/azs+3J5iSJ/f/1fz/WvEv2rfjnb/tC/GGbW\nbG1ks9JtLaHTtOhf/WR2sKbE3155PqMk8EcTSyPDB9xGk/dx1Xxg1pgcqhQlzv8Av/8Ak8uYxzTi\nCeLXLy8vuwj/AOAR5T6Qu/2i/B2s+IPgT4kkutUt9S+G0emafqViLTfG0Frdb3kR/wDc/grR/ZU8\nYzeMf+CgeqeKNNs5J/C/iDWtRk1Pzv3ccenXTyb9/wDwB6+X1qxp+r3mmJJHa3Nxbxyff8mTy/Mq\nZ5FRUZxh9tSXylqFPiPEe1hKf2Jxl/4CfU3hr9s/QvD37c3inxtqa3EXh/8Asm90LS4baPzPLg8n\nyIf/AECvPfGH7Qui69+zr8G/Ctut5/aPgTUdRu9Q3x/u9k9156bP+2deJE/pSg7D/u1NHIcNTlCc\nfscv/kvN/wDJG1bizGyUo/zc3/k3L/8AIxPtzU/jZpv7V1x8aLGzsdU/4RXxPc2upWt5CiSXlnPA\nkcf+o3/vI6ofHPXfCvwi8H/s36Gda1/RR4Pj1i+vbm0RP7Us/Pnjkgn2fweZs37P+edfHlhqlxol\nz5ltcXFrIf44ZPLqPULy41W4864mnuJv78z75K5o8O041Ick/ch/8jyHZW4wqV8PKNaHvy+1/wBx\nPaf+lFrxffw6n4x1S6t7m4uoLi+nkSa5/wCPi4Tf99/9us96QrgUhOa+hp0+SHIfF1ajnPnYUUUV\noZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU\nAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAftz/wAEjv8AlH14B/3dR/8AThdV9H184f8ABI7/AJR9\neAf93Uf/AE4XVfR9fyzn3/IyxH+Of/pR/ffCP/Ikwn/XqH/pJ5B/wUG/5MX+MX/Ynat/6STUUf8A\nBQb/AJMX+MX/AGJ2rf8ApJNRX5XxZ/Hh/hP7B8Df+RZiP+vi/wDSUJ/wT5GP2GPg/wD9idpP/pJD\nX4x/tknb+178Ttv/AENOp/8ApU9fs5/wT5XH7DHwf9/B2kH/AMlIa/K79qj9j74peKf2ofiLqGl+\nB9fvrK68TahNDNDD+7lR5ndJP++Hr978K8RQoVJupPTkj+h/A/0jcFXxNX9xDn/e1D5q8tv7v60e\nU3939a9Z/wCGFPjB/wBE+8S/+AlH/DCnxf8A+ifeJf8AwEr9x/tbB/8AP6B/Kv8AYWZ/8+J/+AyP\nJvKb+7+tHlN/d/WvWf8AhhT4v/8ARPvEv/gJR/wwp8X/APon3iX/AMBKP7Wwf/P6Af2Fmf8Az4n/\nAOAyPJvKb+7+tHlN/d/WvWf+GFPi/wD9E+8S/wDgJR/wwp8X/wDon3iX/wABKX9rYL/n9AP7CzP/\nAJ8T/wDAZHk3lN/d/Wjym/u/rXrP/DCnxf8A+ifeJf8AwEo/4YU+L/8A0T7xL/4CU/7Wwf8Az+gH\n9g5n/wA+J/8AgMjybym/u/rR5Tf3f1r1n/hhT4v/APRPvEv/AICUf8MKfF//AKJ94l/8BKP7Wwf/\nAD+gH9g5n/z4n/4DI8m8pv7v60eU3939a9Z/4YU+L/8A0T7xL/4CUf8ADCnxf/6J94l/8BKP7Wwf\n/P6Af2Dmf/Pif/gMjybym/u/rR5Tf3f1r1n/AIYU+L//AET7xL/4CUf8MKfF/wD6J94l/wDASj+1\nsH/z+gH9hZn/AM+J/wDgMjybym/u/rR5Tf3f1r1n/hhT4v8A/RPvEv8A4CUf8MKfF/8A6J94l/8A\nASl/a2C/5/QF/YOZ/wDPif8A4DI8m8pv7v60eU3939a9Z/4YU+L/AP0T7xL/AOAlH/DCnxf/AOif\neJf/AAEo/tbBf8/oD/sHM/8AnxP/AMBkeTeU3939aPKb+7+tes/8MKfF/wD6J94l/wDASj/hhT4v\n/wDRPvEv/gJT/tbB/wDP6Af2Dmf/AD4n/wCAyPJvKb+7+tHlN/d/WvWf+GFPi/8A9E+8S/8AgJR/\nwwp8X/8Aon3iX/wEo/tbB/8AP6Af2Fmf/Pif/gMjybym/u/rR5Tf3f1r1n/hhT4v/wDRPvEv/gJR\n/wAMKfF//on3iX/wEpf2tgv+f0A/sHM/+fE//AZHk3lN/d/Wjym/u/rXrP8Awwp8X/8Aon3iX/wE\no/4YU+L/AP0T7xL/AOAlH9rYL/n9AP7BzP8A58T/APAZHk3lN/d/Wjym/u/rXrP/AAwp8X/+ifeJ\nf/ASj/hhT4v/APRPvEv/AICU/wC1sH/z+gH9g5n/AM+J/wDgMjybym/u/rR5Tf3f1r1n/hhT4v8A\n/RPvEv8A4CUf8MKfF/8A6J94l/8AASj+1sH/AM/oB/YOZ/8APif/AIDI8m8pv7v60eU3939a9Z/4\nYU+L/wD0T7xL/wCAlH/DCnxf/wCifeJf/ASj+1sH/wA/oB/YOZ/8+J/+AyPJvKb+7+tHlN/d/WvW\nf+GFPi//ANE+8S/+AlH/AAwp8X/+ifeJf/ASl/auC/5/QD+wcz/58T/8BkeTeU3939aPKb+7+tes\n/wDDCnxf/wCifeJf/ASj/hhT4v8A/RPvEv8A4CU/7Wwf/P6Af2Dmf/Pif/gMjybym/u/rR5Tf3f1\nr1n/AIYU+L//AET7xL/4CUf8MKfF/wD6J94l/wDASl/a2C/5/QD+wsz/AOfE/wDwGR5N5Tf3f1o8\npv7v616z/wAMKfF//on3iX/wEo/4YU+L/wD0T7xL/wCAlH9rYL/n9AP7BzP/AJ8T/wDAZHk3lN/d\n/Wjym/u/rXrP/DCnxf8A+ifeJf8AwEo/4YU+L/8A0T7xL/4CUf2tgv8An9AP7BzP/nxP/wABkeTe\nU3939aPKb+7+tes/8MKfF/8A6J94l/8AASj/AIYU+L//AET7xL/4CU/7Wwf/AD+gH9hZn/z4n/4D\nI8m8pv7v60eU3939a9Z/4YU+L/8A0T7xL/4CUf8ADCnxf/6J94l/8BKP7Wwf/P6Af2Dmf/Pif/gM\njybym/u/rR5Tf3f1r1n/AIYU+L//AET7xL/4CUf8MKfF/wD6J94l/wDASl/a2C/5/QD+wcz/AOfE\n/wDwGR5N5Tf3f1o8pv7v616z/wAMKfF//on3iX/wEo/4YU+L/wD0T7xL/wCAlP8AtbB/8/oB/YOZ\n/wDPif8A4DI8m8pv7v60eU3939a9Z/4YU+L/AP0T7xL/AOAlH/DCnxf/AOifeJf/AAEo/tbB/wDP\n6Af2Dmf/AD4n/wCAyPJvKb+7+tHlN/d/WvWf+GFPi/8A9E+8S/8AgJR/wwp8X/8Aon3iX/wEpf2t\ngv8An9AP7CzP/nxP/wABkeTeU3939aPKb+7+tes/8MKfF/8A6J94l/8AASj/AIYU+L//AET7xL/4\nCU/7Wwf/AD+gH9g5n/z4n/4DI8m8pv7v60eU3939a9Z/4YU+L/8A0T7xL/4CUf8ADCnxf/6J94l/\n8BKX9rYL/n9AP7BzP/nxP/wGR5N5Tf3f1o8pv7v616z/AMMKfF//AKJ94l/8BKP+GFPi/wD9E+8S\n/wDgJR/a2C/5/QD+wsz/AOfE/wDwGR5N5Tf3f1o8pv7v616z/wAMKfF//on3iX/wEo/4YU+L/wD0\nT7xL/wCAlH9rYL/n9AP7BzP/AJ8T/wDAZHk3lN/d/Wjym/u/rXrP/DCnxf8A+ifeJf8AwEo/4YU+\nL/8A0T7xL/4CU/7Wwf8Az+gH9hZn/wA+J/8AgMjybym/u/rR5Tf3f1r1n/hhT4v/APRPvEv/AICU\nf8MKfF//AKJ94l/8BKP7Wwf/AD+gH9g5n/z4n/4DI8m8pv7v60eU3939a9Z/4YU+L/8A0T7xL/4C\nUf8ADCnxf/6J94l/8BKX9rYL/n9AP7CzP/nxP/wGR5N5Tf3f1o8pv7v616z/AMMKfF//AKJ94l/8\nBKP+GFPi/wD9E+8S/wDgJT/tbB/8/oB/YOZ/8+J/+AyPJvLb0/Wjy2/u/rXrP/DCnxf/AOifeJf/\nAAEo/wCGFPi//wBE+8S/+AlL+1sF/wA/oB/YOZ/8+J/+AyPJvKb+7+tHlN/d/WvWf+GFPi//ANE+\n8S/+AlH/AAwp8X/+ifeJf/ASn/a2D/5/QD+wcz/58T/8BkeTeU3939aPKb+7+tes/wDDCnxf/wCi\nfeJf/ASj/hhT4v8A/RPvEv8A4CUf2tg/+f0A/sHM/wDnxP8A8BkeTeU3939aPKb+7+tes/8ADCnx\nf/6J94l/8BKP+GFPi/8A9E+8S/8AgJR/a2D/AOf0A/sLM/8AnxP/AMBkeTeU3939aPKb+7+tes/8\nMKfF/wD6J94l/wDASj/hhT4v/wDRPvEv/gJR/a2D/wCf0A/sHM/+fE//AAGR5N5Tf3f1o8pv7v61\n6z/wwp8X/wDon3iX/wABKP8AhhT4v/8ARPvEv/gJS/tbBf8AP6Af2Fmf/Pif/gMjybym/u/rR5Tf\n3f1r1n/hhT4v/wDRPvEv/gJR/wAMKfF//on3iX/wEp/2tg/+f0A/sHM/+fE//AZHk3lN/d/Wjym/\nu/rXrP8Awwp8X/8Aon3iX/wEo/4YU+L/AP0T7xL/AOAlH9rYP/n9AP7BzP8A58T/APAZHk3lN/d/\nWjym/u/rXrP/AAwp8X/+ifeJf/ASj/hhT4v/APRPvEv/AICUf2tg/wDn9AP7BzP/AJ8T/wDAZHk3\nlN/d/Wjym/u/rXrP/DCnxf8A+ifeJf8AwEo/4YU+L/8A0T7xL/4CUf2tg/8An9AP7CzP/nxP/wAB\nkeTeU3939aPKb+7+tes/8MKfF/8A6J94l/8AASj/AIYU+L//AET7xL/4CUf2tg/+f0A/sHM/+fE/\n/AZHk3lN/d/Wjym/u/rXrP8Awwp8X/8Aon3iX/wEo/4YU+L/AP0T7xL/AOAlL+1sF/z+gH9g5n/z\n4n/4DI8m8pv7v60eU3939a9Z/wCGFPi//wBE+8S/+AlH/DCnxf8A+ifeJf8AwEo/tXBf8/oB/YOZ\n/wDPif8A4DI8m8pv7v60eU3939a9Z/4YU+L/AP0T7xL/AOAlH/DCnxf/AOifeJf/AAEp/wBrYP8A\n5/QD+wcz/wCfE/8AwGR5N5Tf3f1o8pv7v616z/wwp8X/APon3iX/AMBKP+GFPi//ANE+8S/+AlH9\nrYP/AJ/QD+wsz/58T/8AAZHk3lN/d/Wjym/u/rXrP/DCnxf/AOifeJf/AAEo/wCGFPi//wBE+8S/\n+AlH9rYP/n9AP7CzP/nxP/wGR5N5Tf3f1o8pv7v616z/AMMKfF//AKJ94l/8BKP+GFPi/wD9E+8S\n/wDgJR/a2D/5/QD+wsz/AOfE/wDwGR5N5Tf3f1o8pv7v616z/wAMKfF//on3iX/wEo/4YU+L/wD0\nT7xL/wCAlH9rYP8A5/QD+wsz/wCfE/8AwGR5N5Tf3f1o8pv7v616z/wwp8X/APon3iX/AMBKP+GF\nPi//ANE+8S/+AlH9rYP/AJ/QD+wsz/58T/8AAZHk3lN/d/Wjym/u/rXrP/DCnxf/AOifeJf/AAEo\n/wCGFPi//wBE+8S/+AlH9rYP/n9AP7CzP/nxP/wGR5N5Tf3f1o8pv7v616z/AMMKfF//AKJ94l/8\nBKP+GFPi/wD9E+8S/wDgJR/a2D/5/QD+wsz/AOfE/wDwGR5N5Tf3f1o8pv7v616z/wAMKfF//on3\niX/wEo/4YU+L/wD0T7xL/wCAlH9rYP8A5/QD+wsz/wCfE/8AwGR5N5Tf3f1o8pv7v616z/wwp8X/\nAPon3iX/AMBKP+GFPi//ANE+8S/+AlH9rYP/AJ/QD+wsz/58T/8AAZHk3lN/d/Wjym/u/rXrP/DC\nnxf/AOifeJf/AAEo/wCGFPi//wBE+8S/+AlH9rYP/n9AP7CzP/nxP/wGR5N5Tf3f1o8pv7v616z/\nAMMKfF//AKJ94l/8BKP+GFPi/wD9E+8S/wDgJR/a2D/5/QD+wsz/AOfE/wDwGR5N5Tf3f1o8pv7v\n616z/wAMKfF//on3iX/wEo/4YU+L/wD0T7xL/wCAlH9rYP8A5/QD+wsz/wCfE/8AwGR5N5Tf3f1o\n8pv7v616z/wwp8X/APon3iX/AMBKP+GFPi//ANE+8S/+AlH9rYP/AJ/QD+wsz/58T/8AAZHk3lN/\nd/Wjym/u/rXrP/DCnxf/AOifeJf/AAEo/wCGFPi//wBE+8S/+AlH9rYP/n9AP7CzP/nxP/wGR5N5\nTf3f1o8pv7v616z/AMMKfF//AKJ94l/8BKP+GFPi/wD9E+8S/wDgJR/a2D/5/QD+wsz/AOfE/wDw\nGR5N5Tf3f1o8pv7v616z/wAMKfF//on3iX/wEo/4YU+L/wD0T7xL/wCAlH9rYP8A5/QD+wsz/wCf\nE/8AwGR5N5Tf3f1o8pv7v616z/wwp8X/APon3iX/AMBKP+GFPi//ANE+8S/+AlH9rYP/AJ/QD+ws\nz/58T/8AAZHk3lN/d/Wjym/u/rXrP/DCnxf/AOifeJf/AAEo/wCGFPi//wBE+8S/+AlH9rYP/n9A\nP7CzP/nxP/wGR5N5Tf3f1o8pv7v616z/AMMKfF//AKJ94l/8BKP+GFPi/wD9E+8S/wDgJR/a2D/5\n/QD+wsz/AOfE/wDwGR5N5Tf3f1o8pv7v616z/wAMKfF//on3iX/wEo/4YU+L/wD0T7xL/wCAlH9r\nYP8A5/QD+wsz/wCfE/8AwGR5N5Tf3f1o8pv7v616z/wwp8X/APon3iX/AMBKP+GFPi//ANE+8S/+\nAlL+1sH/AM/oB/YWZ/8APif/AIDI/V7/AIJHH/jX14B/3dR/9OF1X0fXgX/BMLwZqvw+/Ya8C6Vr\nVhcadqVul6ZraZPLkj3307p/5Devfa/mTO6nPmVacP55/wDpR/cnClOcMnwlOp/z6h/6SeQf8FBv\n+TF/jF/2J2rf+kk1FH/BQb/kxf4xf9idq3/pJNRX5fxZ/Hh/hP6+8DP+RZiLf8/F/wCkoT/gnx/y\nYz8H/wDsTtI/9JIa9i8MIHutSyM/6UOv/XGOvHf+CfH/ACYx8H/+xO0j/wBJIa9i8Lf8fWpf9fQ/\n9Ex19phf4Mf8J/POeJPNcRf+aX/pRseWv91fyo8tf7q/lTqK2uzzOVDfLX+6v5UeWv8AdX8qdmuF\ni+PXh66+N8fgG3vIrjXv7Lk1WSOJ9/kRo6R/P9S4ouHKjuPLX+6v5UeWv91fyrzP4g/HvUfAmo6h\nH/whPiK+sdPUyPfw7PIkTbnfWTp/7Y2ia38P/CesWelatdah41R5NL0hE/0udE+++P7g4596Lhyo\n9i8tf7q/lR5a/wB1fyrzHTf2oPDVz8Ntc8R30k2lR+F7l7TV7O5Q/abOfjEOzP333ps9d4qT4d/t\nCx+M/F1touoeH9a8O3t/a/a7NL+HHnoPv/lRcOVHpXlr/dX8qPLX+6v5U6ii4cqG+Wv91fyo8tf7\nq/lTqKLhyob5a/3V/Kgxrj7q/lTs0Z3UXDlRGI9w5UGjyFQfdX8q5f4lfFHT/hZo9vcXwkmmvJ0t\nLW3hTdJdTP0RBWb4K+OGn+L9d1TSbi3utH1XSYUup7e9+Q+S3/LT/cyKn2i5uQ1jhKkoe15Tuii5\nGVX8qNgI5UflXB6H+0H4b13QPEurR3n/ABLfC11JaXtzj93vjjR32+v3xVPwt+0Pp2u+IdN0+807\nUdHk1xfM05ruPYt3gZwnvswaPaxL+oVve9w9K8tf7q/lR5a/3V/KnA5oqrnPyob5a/3V/Kjy1/ur\n+VOoouHKhvlqf4V/Kgxr6AfhTZX2rXyj+wt/wV2+Gf7fXxx+Ivw+8IxavZeIPhvczW98l/D5YuRH\ncPA8kP8AfTelTfmlyhypR5j6w8tf7q/lR5a/3V/KvlB/+CvXwxb/AIKQW/7MdvDrFx48njd2uUg/\n0CN0g8949/8AfEdeo/tkftm+Cf2F/g1N418cXs8Gn/aUsbO2tk8y41C6k/1cESfxO9Vd8vMLlXNy\n2PXPK/2RR5X+yK+Tf2R/+CvHgX9qj44P8N5/DnjT4feNZbI6lZaX4n077HLfwJ9+SP6V9bZ4ouFk\nN8tf7q/lR5a/3V/KnZozRcfKhvlr/dX8qPLX+6v5U7NGaLi5UN8tf7q/lR5a/wB1fypM/JXjVx+2\nl4Xtf217T4Dvb6gfFt14efxKk3l/6P5CP5eN/wDfpc3vcocq5eY9mEa/3V/Kjy1/ur+VBO0e9Z/i\nPXofDOgX2ozD9xp9u9w+3+4iFz+gNRKpyx5pD5UX2Rf7o/KhVX+6v5V5P+xl+1z4d/bi+AGl/ETw\npHfQ6JrE08MCXkflyAwzvE3H1Q1qeHPj2uvftB+IPAK+HfEVq2g2EN8dXmtdmn3fmHHlxyfxOP6V\npd83KRzQ5eY9F8tf7q/lR5a/3V/KnA5oouVyob5a/wB1fyo8tf7q/lTqKLj5UN8tf7q/lR5a/wB1\nfyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1\nFFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5\nUN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8t\nf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/\nlR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a\n/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1\nfyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1\nFFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5\nUN8tf7q/lR5a/wB1fyp1FFw5UN8tf7q/lR5a/wB1fyp1FFw5UfC//BYz/go54w/YXl8F6b4N0+1l\nvPFEd1O1zNb/AGjy/I8v5Nnvvr4h0v8A4L+fHKWS3SWTw95lxceTGj6OkfmH+5Xo3/BzH4Y8SeLf\njL8FLPw5pOoaxdf2ZrDtDD/q/v2/36/P6/8Agj4n+H/gfStb1yw0v95deRPbef8A6RZv/q08z+55\nlF2LlXY+0/C//Bb39oTxv440Pw/p8nhX7XrF1HaR79H/AHkj+Zsf5K+9PjJ/wUM0r4D+PPDfgDWf\nFGhjx9qWnpdzWrQY8z/4ivxJ/Zf+NNz8Fv2iLXxD5NvPdafPPsR/3kdu+/56+2vFvg3wZ+118SND\n+Lt/bRyeI/C8HmXrzT+XHGkH3N//AF0ouw5V2P14+HviqHx58P8ARdbhVfL1iyiuk2j++getzy1/\nur+VeY/sY6uniL9kr4a6gmNt94asZvl/24ENeoUXHyob5a/3V/Kjy1/ur+VOoouHKhvlr/dX8qPL\nX+6v5U6ii4cqG+Wv91fyo8tf7q/lTqKLhyob5a/3V/Kjy1/ur+VeH6r+274dsb7UZo9N1i58NaPq\nA0q98QRJ/oFvP5nlyc/7D8V7ZbXcd3bpLGweORd6NRcOVEnlr/dX8qPLX+6v5VwGiftGeG/Evx1v\nvh/p95He65peljVLryX8yOBPM8vYf9usz9qb9qfw1+yT8PY/EXiQXE8NzcpawW9snmT3D57f7nWi\n4cqPUvLX+6v5UeWv91fyry/4r/tJ2vwz8b+GfDdroeseINY8UWd1f21tYoh8uGDy97v/AN/krqvB\nfjuXxF4ZbUtW0m88M+W2Hh1FkQoP7+f8aLhyo6by1/ur+VHlr/dX8q85+E37SvhX4x/DjWPFuk3i\n/wBgaLfXthNdv/q3+yuUkkH+x8hrmfC37aeh69eeHJrnRdf0vRPF0yW+i6veQeXb3jv/AKv/AHN9\nGouVHtnlr/dX8qPLX+6v5U6ii4+VDfLX+6v5UeWv91fyp1FFw5UN8tf7q/lR5a/3V/KnUUXDlQ3y\n1/ur+VHlr/dX8qdRRcOVDfLX+6v5UeWv91fyrz/40/tEeGfgU2iR65dx/avEGpwaTZWisDPLJNJs\nB2f3BTfi7+0Hpvwn1/R9F+x6hrXiLXt8llpdim+4kjT78n+4KLhyo9C8tf7q/lR5a/3V/KuD+Cfx\n30r44WOrNZx3Wnal4b1FtN1bTrtPLnsJ9gkCP9Y3R/xrvqLhyob5a/3V/Kjy1/ur+VOoouHKhvlr\n/dX8qPLX+6v5U6uY8V/EODw5q0GmxW0+oalcRvMltbf6zyx/HRcOVHS+Wv8AdX8qPLX+6v5Vh+Cf\nGlp410uS4tRIjW8z288L/wCsgkTqhreouHKhvlr/AHV/Kjy1/ur+VOoouHKhvlr/AHV/Kjy1/ur+\nVOoouLlRzHhz/kX7H/r1j/8AQKt1U8Of8i/Y/wDXrH/6BVutDE8g/wCCg3/Ji/xi/wCxO1b/ANJJ\nqKP+Cg3/ACYv8Yv+xO1b/wBJJqK+B4s/jw/wn9QeBv8AyLMR/wBfF/6ShP8Agnx/yYx8H/8AsTtI\n/wDSSGvYvCw/0vUv+vof+iY68d/4J8f8mMfB/wD7E7SP/SSGvYvC3/H1qX/X0P8A0THX2mF/gx/w\nn8855/yNcR/il/6UbVB6UUVt0PNIyMivnaXwXpfhj/gpFpl1p9jDZzap4JupLqSFP+Ph/tsf36+i\ngcg15Ne/sm6Lqnxjh8cN4i8bf23bL5SBNVPkJD5nmeRs2f6veo+SoXxRk/60CWseUyf21deutY8K\naL8PdMeSPWPiRfrpQZPv21p9+6nH+5GM/jXPyeHbbwb/AMFA/AemxQrb6VaeA7q00tP4BIk8fmbP\n+2eyvYrz4YaTc/Fu18ZXEc02tafp0mmWu6TMdvC7iSTYn999iZP+wKg+KnwW0X4tHT31AX1rqGkz\nGew1Cxn8i7s3b73lv2zVQfL/AF8iZe8uT+u58X/tHjUpPjp8YG0mSKPS5PHngK3uXdP9Hjuf3fne\nZ+D2lfQNxf8AiH4N/tQeC9Dn8QXnifRvGkN7uhv9j3GnvBH5m9H/AOedegaZ+zT4Q0/4Yal4Rl09\n77S9alkuNQe7lMtxfTSdZnfvJwPn/wBgU34a/s16D8OfFCa19s1zXNYt7X7HDd6xem7ktIf+ecf9\nwU6fuv8Ar+XlKqe8ekDgUUUUAFB6UUGgDjfi94d1nxd8PtR0/wAPaw+h61NGfst2iZET18ofst6X\n+0Z43+Mt1a+LdfvtJ8P+HLny7x2jj/4mA/uR/wDxdfbUsZZxhtq+gpUJDtyF9K56mH5qvtUergc3\nlhsPPDckJc3Vx1j6HP8AxB1LQvDuif2xrwtY7bRz9pSaYD9w/qv+1Xl/wQ8I6h8TviFr3xB8Q2f2\nG21iwTR9J06ZP3kdkjmTfJ/tu7k49BXefGL4K6V8atMsrPVpb9LexukvEW1uTBmRPu7sdRVzwN8N\no/Bl9JNHrPiHUPMTZ5V9eGaOP6Cq9nJy94yp1qVKh7svfkfLPiDSP7C+A3xWttPijtbK18eJ56J9\nxId9t5ler/tVOtzqnwh+wbPtEni+ye32/wDPHypN/wD5DzXpOkfBrw/pdj4js1tBNa+KrqS71CKV\nvMSeR0CP19QorP8AB37OmgeE/EFnqKtqWoXGmQ+RY/b7n7QlgnpCCPkqI0ZR5f8At3/yU9CeaU5S\nUn05v/JopHog6UUDgUV0nz4UUUUAV5mBib2X+lfz+/so/Emw/YC8Q6H+0peiS18Ox/GTxt4M8XXC\nf6v7FdT+ZBI//XO4T/yJX9AjKuPpXzHr3/BJD4L+Kf2YfFHwf1PRL+98D+MPEdx4q1G2lvXMn26e\nf7Q8iP1T5x2rN88Zc0f696P/ALbcPdlDll/Xuy/U/NH9k34e3lt/wU3/AGR/iVr0ITxN8bJvFnjO\n+d08uTyJ4JPsqf8AbODy6+1v+Cw/w9+Gf7a3wJ8A6D/wujw74D8UR+MYLvwPrAuku7S41uBJPLtn\n25TnzO5zxX0drH7Cvw71n4w/DHxxJp94mufCLT5tK8NmK5KQW8EsHkOjp/H+745ry/Q/+CLX7Puj\nfspah8Fz4XvL3wXqGtTeIY/tV7JJeWN/LgGeCf78bjaMVpq4cv8AL8P/AIF/8j+JNPmUpTn8Uv8A\n5H3v/Jj5g8F/tE/G39lT/gpb8H/h/wDtG2/wr8f3vjXRru30bxbomneRqekeRBI8jv8Ax+XJsr4w\n+Mv7Zvi7wv4p0P45eANc+P8A4isr/wCIlrZ2vjXVLqCz8HavA915E9jHZb9/9+P/ALZ76/Xv9lL/\nAII7fBH9kP4jTeMdB03xJr/iqSyksINS8Ta1PrFxZwv9+OF5j8m/vXnV5/wbq/sx3NprVnJ4d8TS\n6brNy15Bp7eIbo2ehzGfz/MsYc7IHyX5x/GaqGlSMp/Z/wDkuYJa05Rf2v8A5HlPKfhd4I8WftSf\n8Fs/jz4c1T4keMdJ8F/D/wDsTWbXSNNvngjuJ/L+5J/0z/2K+Yb/AMKfED4zfsLftbfFy4+MXxI0\nrWPgj4y1+48L2thqjx20XkeXP5cn/PROdn4V+v3wT/Yj8B/s/fGnxR498P22pL4l8YafZabqt1c3\njz/aIbWPy4OD/EB371z2h/8ABM74W6D8BPij8OLex1YeF/jFeX194kiOoSeZPJdqEn2P1TIA6frX\nHGnJLljtyy/8C5o//IhH3pc0/wCaP/gPKfndqXiz4hfBH4l/sQ/HK4+KHjDXNX/aDvdOt/FmlXl1\n/wASf7Pdad9q2QQf8s/Lr55/a8/bQ8WeG/FWqfG74d67+0B4ij0/x/BYWni+5uo7PwfJ5d15D6b9\ni/1j/wAab6/Z7xZ/wTk+GPjLw38GdHv9P1KSx+BM9tc+FU+3P+4MEAgj87/np+745rxTxJ/wbzfs\nyeJb7xUt74b8RSaf4omkvH03+37j+z9MuXfzHntYM7IJP9sA/eNdvtKbrKa+GMpf+A3j7oOMvZ8i\n/l/zPkz/AIK16h8XNT/bO+IWsT/8Ld1j4deG/DenzeHpvhrrUEf/AAik/keZdT6ra7/+en7xPM/5\nZ1G3g7/h4B/wVd+FC6f8QfFFjo/iz4HR3d7rumv9g1TUI/Mj/wC+PMf79fbHx9/4IdfAf9oLxha+\nINasvGFnqsekWmi3suk+IJ7H+3ILVPLhN7s/17iP5N784r1zwL+wH8Mvh18edB+Img6JJpeveG/D\nY8KafFBMUs7fTwf9WIenUda56MUuVv7MpfjGpH/26JNepKWkP7v/ALb/APIyPyN/4a4+K3w+/wCC\neviv4U2/xG1y38z9pyb4Kp4zv5/M1DR9EdI5Hd5/+en3/n/6aV7JB8LNb/Y1/wCCvfhH4X6b8WPG\n/jTwfqnwt8Raq+la1qv2ySzn8n/WP/v/AMFfTX7W/wDwSj8O6n+xx8WvAfw68J6Hrt18VPFc/jLV\nrHxPqM8cVxqM+wyTQTp88Mm6OPZ6c14h/wAEyv8AgjJ44+Dv7TNt8Sfidp3h/wAO/wDCPeHr7w9Z\nWema9da1d6wl7/rJ7i6n+f8Adx/u0SujSTlD+7/7Zy/+le8dE+SMPc/r3ub/ANJPibwt4B8Zfs9f\n8EJND/aW8N/E7xpo3irwJ4pVdI0ezvdmj+Q+v+Q8ckH8efMd6/TT9m/4p+INe/4LyfHDw3dazqE3\nh/T/AIfaJd2umyTf6PbzO/zuiV61rX/BKb4Oa9+w1N+zvNpusN8M5rpLyS1Gov8AaPMS7F3/AK7r\n/rkzXoXgv9j3wX4G/aj8TfGDT7W+Txp4r0i10TUJnuS0D21vzGFj7YpOpC0Et+aX/pHL/wClHDKn\nPk/r+bm/9JPXqKKKk6gooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK\nACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA\nKKKKACiiigAooooAKKKKAPzZ/wCDhHVfivo3gHwrc/DDTdL1WWOG6+3Quj/bdm+3/wBS/wD33X5s\n/wDDOfjn48afdXni3xtcQaPqFlBBPYQwfZ7yRH/9qV9ff8HWNr8V7C1+FmtfDnxF4o0G1srbUoNR\n/srf5c7yeR5e/wD74evkH/gmTpnjz4g/COd9eudU1j5Pv3MD/aI3/uUAeQXXgjTfhL+0h4xtori4\nj03VJ4JNISZ/9W+/y5/+/nyPX1L8PvHmieCPhP4gh8UW2qalY6hsgSzs/wDlo7/u68j+PH7I2seI\n/wBrjwXf3FhqH2GS9e72eQ/8EHmf+jKt/th/GTVf2ffB8mlaDomoX2seR5k6JavJ5f8Ac/8AZKAP\n3w/4J7WdrZfsI/B6GzhuLW0j8H6WkMNz/rY0+yx/I/vXs9eN/sATa/c/sM/CGTxVb/ZfEz+ENKfV\nINnl+XdfZY/MX/vvNeyUAFFFFABRRRQAVV1nd/ZN1s/1nkvt/KrVB5oA+E/hM9va/wDBGjxMupeX\n9st7HW4L1f8Alp9r+3XH3/8Appv2V9a/AyC8i+CfhFdQz9sXSLYT5/v+UlcjrH7EngfxB4putQlj\n1aOy1C/TVbzR4r100y7ulx+/eD+/079q7O0+D+l2Pxcn8axzamurXGmJpLRNdv8AY1hR/MGIc7N/\n+3Wlwnr/AOTf+THiXgXwVpXgr/gqBr50uwt9P/tLwBHPceUmPPf7f9+vFf22fFeq+KfAfxe8QeLP\nBPjiFtPsZNJ8Nn+y/Ms7O13x77rfv/1klfUen/seaHpvxnl8dR+I/GzeIJI/s7s+rZieDzPM8nZs\n/wBXv5xXe/FX4Y6X8Y/h7qvhnW45pNK1iA29ysUnlyFD/tVn9mJV/e/8BPC/Fng1/jt+0d8N9Wsf\nFl34PvrDwlqMk+ltB9n1iWCee0/ef7CB4P8Ax+tf9ljxhqPxI1X4n/DvxjNF4mi8EawmnJfzJzfw\nyR+eiSf7cdehfFD9nzQ/ipq2j6ndSappetaDG8FjqmlXX2W9gjfHmIHH8D7F4pfBf7POgfDn4c6l\n4d0GTU9LTVJZLi81GG6xqFxO/wB+Z5upk9/am2uS39bi6nin7CEXh3wJ+zL8SE1RLPTfC+k+MvEq\nXSSLi3gtUvpd/wD2z2VN+1skeij4b+JmvLXVPh3Za3piW2j28OzEkhKQTo/8ez5MJXpnwf8A2SfD\nPwRs9YtNOvPEepadrz3Ml9Y6rqBvLeaS4ffPJswPnf1qj4R/Ym8F+D7/AEaRZNe1DT/Ddx9r0jS7\n3UXuNP0txjZ5MZ6eX/B6UqcV7v8Ad5f/ACUj+b/t7/yY9qHSiiigoKKKKACiiigAooooA+YP+CjX\ngrSLvTfh/rs1hbyaxa+NtGggvNn7yNDdZ2V6f+0B8T/DHwG0RfGGsWcd1rSR/wBnaTDDEHvtQmkP\n7u1h75d6h+Ov7LGjftC6jYya5q/iu3h0+aC5gtbDUzBbxzwyb45tmPv+9ZvxG/Yw8O/E7xJ4X1jV\nNb8ZDVPB9vJBp13DqvlyqX+/I52cyf7fWpj8PL/eD7fN/dKf7GvwS174d2vizxd4ye3/AOE2+JWq\n/wBs6vbW3+osESCOCC1Q/wAflxxqN/f+fuhGa5H4afDNfhvb3Ua654j1v7U6yb9XvjdvHj+6ccV1\n1aVCYbBRRRUlBXK+P/Flj4AthqTWwuNUuV+zWUKJ/pF2/ZBXVVyPi34P6f4v8V2utTXerWt9ZwmG\nF7a68vywaAPPdX8Nax4K+Htk11d/ZNY17xJBPeyWz/c8x/uVqR+EI7X44NoMd1fLo95on26aATv8\n8qT7Ov8AwMflXZN8M7O706GzvLrUtRW3ukvY5Lm43yJIn3Oa0T4StP8AhMv7c2N9v+x/Yd2f+We7\nf/Opv/X/AG6T/X/kx5Bo/iG+1Sx0Pw7NfXCWt54hurB5t/7x4IPnRN9dp4Cnm8OfFnXvDsMlxc6X\nBZWt/AZZN/2d3eRHT/xzf+NbFx8JNGvdDmsdlxHHJeNfpKsn7y3nP8aN2q/4T8D2fg8XDW7XE1zd\nvvmuLl/Mmn+rVRRu0UUUAcx4c/5F+x/69Y//AECrdVPDn/Iv2P8A16x/+gVbrQ5zyD/goN/yYv8A\nGL/sTtW/9JJqKP8AgoN/yYv8Yv8AsTtW/wDSSaivgeLP48P8J/UHgb/yLMR/18X/AKShP+CfH/Jj\nHwf/AOxO0j/0khr2LwsP9L1L/r6H/omOvHf+CfH/ACYz8H/+xO0j/wBJIa9d8PGT7ZqXl7Nn2kff\n/wCuMdfa4X+DH/Cfzznn/I1xP+KX/pRv0VTzd/37ejN3/ft61PNLlFU83f8Aft6M3f8Aft6ALlFU\n83f9+3ozd/37egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN3/ft6ALlFU83f8Aft6M3f8Aft6ALmKM\nVTzd/wB+3ozd/wB+3oAuUYqnm7/v29Gbv+/b0AXKKp5u/wC/b0Zu/wC/b0AXKKp5u/79vRm7/v29\nAFyiqebv+/b0Zu/79vQBcoqnm7/v29Gbv+/b0AXMUYqnm7/v29Gbv+/b0AXKKp5u/wC/b0Zu/wC/\nb0AXMUVTzd/37ejN3/ft6ALmKMVTzd/37ejN3/ft6ALmKMVTzd/37ejN3/ft6ALlGKp5u/79vRm7\n/v29AFzFFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5RVPN3/ft6M3f9+3oAuUVTzd/37ejN\n3/ft6ALlFU83f9+3ozd/37egC5RVPN3/AH7ejN3/AH7egC5QelU83f8Aft6Cbr+/b0AeT/tNfELV\nvCuseGdPs/DMPiK11S68q586289LceteiaZ4E0bSLf8A0XRdJtww+ZYbNI8/kK0Ps9wB/wAu/wCK\n07y7pVIEkI/OqOHD4erCrKcp/H/5KV5PBukTPG7aXprPH9xvsyfJUF58NfD+ozeZNoeizSf3pLGN\nv5itHN1/ft6M3f8Aft6k7iykQiTao2qKfVPN3/ft6M3f9+3oAuUVTzd/37ejN3/ft6ALlFU83f8A\nft6M3f8Aft6ALlFU83f9+3ozd/37egC5RVPN3/ft6M3f9+3oAuUYqnm7/v29Gbv+/b0AXKKp5u/7\n9vRm7/v29AFyiqebv+/b0Zu/79vQBcoqnm7/AL9vRm7/AL9vQBcoqnm7/v29Gbv+/b0AXKKp5u/7\n9vRm7/v29AFyiqebv+/b0Zu/79vQBcoqnm7/AL9vRm7/AL9vQBcoqnm7/v29Gbv+/b0AXKKp5u/7\n9vRm7/v29AFyiqebv+/b0Zu/79vQBcoqnm7/AL9vRm7/AL9vQBcoqnm7/v29Gbv+/b0AXKKp5u/7\n9vRm7/v29AGJ4c/5F+x/69Y//QKt1U8Of8i/Y/8AXrH/AOgVbrQ5zyD/AIKDf8mL/GL/ALE7Vv8A\n0kmoo/4KDH/jBf4xf9idq3/pJNRXwPFn8eH+E/qDwN/5FmI/6+L/ANJR5R+w7+2/8HfCH7HHwu0n\nWPil8PdM1LTvCul2l1aXPiCzgntJktIUdHRn+R0f+CvU9B/4KB/AuG61Dd8YPhmqyXAZM+J7P5v3\nUf8A01oor2MLmFd0I6nz2e+H+Wf2nileXxy6x/m/wmr/AMPEfgT/ANFi+GP/AIU9l/8AHKP+HiPw\nJ/6LF8Mf/Cnsv/jlFFbfXq3c8b/UHL/55/fH/wCRD/h4j8Cf+ixfDH/wp7L/AOOUf8PEfgT/ANFi\n+GP/AIU9l/8AHKKKPr1buH+oOX/zz++P/wAiH/DxH4E/9Fi+GP8A4U9l/wDHKP8Ah4j8Cf8AosXw\nx/8ACnsv/jlFFH16t3D/AFBy/wDnn98f/kQ/4eI/An/osXwx/wDCnsv/AI5R/wAPEfgT/wBFi+GP\n/hT2X/xyiij69W7h/qDl/wDPP74//Ih/w8R+BP8A0WL4Y/8AhT2X/wAco/4eI/An/osXwx/8Key/\n+OUUUfXq3cP9Qcv/AJ5/fH/5EP8Ah4j8Cf8AosXwx/8ACnsv/jlH/DxH4E/9Fi+GP/hT2X/xyiij\n69W7h/qDl/8APP74/wDyIf8ADxH4E/8ARYvhj/4U9l/8co/4eI/An/osXwx/8Key/wDjlFFH16t3\nD/UHL/55/fH/AORD/h4j8Cf+ixfDH/wp7L/45R/w8R+BP/RYvhj/AOFPZf8Axyiij69W7h/qDl/8\n8/vj/wDIh/w8R+BP/RYvhj/4U9l/8co/4eI/An/osXwx/wDCnsv/AI5RRR9erdw/1By/+ef3x/8A\nkQ/4eI/An/osXwx/8Key/wDjlH/DxH4E/wDRYvhj/wCFPZf/AByiij69W7h/qDl/88/vj/8AIh/w\n8R+BP/RYvhj/AOFPZf8Axyj/AIeI/An/AKLF8Mf/AAp7L/45RRR9erdw/wBQcv8A55/fH/5EP+Hi\nPwJ/6LF8Mf8Awp7L/wCOUf8ADxH4E/8ARYvhj/4U9l/8cooo+vVu4f6g5f8Azz++P/yIf8PEfgT/\nANFi+GP/AIU9l/8AHKP+HiPwJ/6LF8Mf/Cnsv/jlFFH16t3D/UHL/wCef3x/+RD/AIeI/An/AKLF\n8Mf/AAp7L/45R/w8R+BP/RYvhj/4U9l/8cooo+vVu4f6g5f/ADz++P8A8iH/AA8R+BP/AEWL4Y/+\nFPZf/HKP+HiPwJ/6LF8Mf/Cnsv8A45RRR9erdw/1By/+ef3x/wDkQ/4eI/An/osXwx/8Key/+OUf\n8PEfgT/0WL4Y/wDhT2X/AMcooo+vVu4f6g5f/PP74/8AyIf8PEfgT/0WL4Y/+FPZf/HKP+HiPwJ/\n6LF8Mf8Awp7L/wCOUUUfXq3cP9Qcv/nn98f/AJEP+HiPwJ/6LF8Mf/Cnsv8A45R/w8R+BP8A0WL4\nY/8AhT2X/wAcooo+vVu4f6g5f/PP74//ACIf8PEfgT/0WL4Y/wDhT2X/AMco/wCHiPwJ/wCixfDH\n/wAKey/+OUUUfXq3cP8AUHL/AOef3x/+RD/h4j8Cf+ixfDH/AMKey/8AjlH/AA8R+BP/AEWL4Y/+\nFPZf/HKKKPr1buH+oOX/AM8/vj/8iH/DxH4E/wDRYvhj/wCFPZf/AByj/h4j8Cf+ixfDH/wp7L/4\n5RRR9erdw/1By/8Ann98f/kQ/wCHiPwJ/wCixfDH/wAKey/+OUf8PEfgT/0WL4Y/+FPZf/HKKKPr\n1buH+oOX/wA8/vj/APIh/wAPEfgT/wBFi+GP/hT2X/xyj/h4j8Cf+ixfDH/wp7L/AOOUUUfXq3cP\n9Qcv/nn98f8A5EP+HiPwJ/6LF8Mf/Cnsv/jlH/DxH4E/9Fi+GP8A4U9l/wDHKKKPr1buH+oOX/zz\n++P/AMiH/DxH4E/9Fi+GP/hT2X/xyj/h4j8Cf+ixfDH/AMKey/8AjlFFH16t3D/UHL/55/fH/wCR\nD/h4j8Cf+ixfDH/wp7L/AOOUf8PEfgT/ANFi+GP/AIU9l/8AHKKKPr1buH+oOX/zz++P/wAiH/Dx\nH4E/9Fi+GP8A4U9l/wDHKP8Ah4j8Cf8AosXwx/8ACnsv/jlFFH16t3D/AFBy/wDnn98f/kQ/4eI/\nAn/osXwx/wDCnsv/AI5R/wAPEfgT/wBFi+GP/hT2X/xyiij69W7h/qDl/wDPP74//Ih/w8R+BP8A\n0WL4Y/8AhT2X/wAco/4eI/An/osXwx/8Key/+OUUUfXq3cP9Qcv/AJ5/fH/5EP8Ah4j8Cf8AosXw\nx/8ACnsv/jlH/DxH4E/9Fi+GP/hT2X/xyiij69W7h/qDl/8APP74/wDyIf8ADxH4E/8ARYvhj/4U\n9l/8co/4eI/An/osXwx/8Key/wDjlFFH16t3D/UHL/55/fH/AORD/h4j8Cf+ixfDH/wp7L/45R/w\n8R+BP/RYvhj/AOFPZf8Axyiij69W7h/qDl/88/vj/wDIh/w8R+BP/RYvhj/4U9l/8co/4eI/An/o\nsXwx/wDCnsv/AI5RRR9erdw/1By/+ef3x/8AkQ/4eI/An/osXwx/8Key/wDjlH/DxH4E/wDRYvhj\n/wCFPZf/AByiij69W7h/qDl/88/vj/8AIh/w8R+BP/RYvhj/AOFPZf8Axyj/AIeI/An/AKLF8Mf/\nAAp7L/45RRR9erdw/wBQcv8A55/fH/5EP+HiPwJ/6LF8Mf8Awp7L/wCOUf8ADxH4E/8ARYvhj/4U\n9l/8cooo+vVu4f6g5f8Azz++P/yIf8PEfgT/ANFi+GP/AIU9l/8AHKP+HiPwJ/6LF8Mf/Cnsv/jl\nFFH16t3D/UHL/wCef3x/+RD/AIeI/An/AKLF8Mf/AAp7L/45R/w8R+BP/RYvhj/4U9l/8cooo+vV\nu4f6g5f/ADz++P8A8iH/AA8R+BP/AEWL4Y/+FPZf/HKP+HiPwJ/6LF8Mf/Cnsv8A45RRR9erdw/1\nBy/+ef3x/wDkQ/4eI/An/osXwx/8Key/+OUf8PEfgT/0WL4Y/wDhT2X/AMcooo+vVu4f6g5f/PP7\n4/8AyIf8PEfgT/0WL4Y/+FPZf/HKP+HiPwJ/6LF8Mf8Awp7L/wCOUUUfXq3cP9Qcv/nn98f/AJEP\n+HiPwJ/6LF8Mf/Cnsv8A45R/w8R+BP8A0WL4Y/8AhT2X/wAcooo+vVu4f6g5f/PP74//ACIf8PEf\ngT/0WL4Y/wDhT2X/AMco/wCHiPwJ/wCixfDH/wAKey/+OUUUfXq3cP8AUHL/AOef3x/+RD/h4j8C\nf+ixfDH/AMKey/8AjlH/AA8R+BP/AEWL4Y/+FPZf/HKKKPr1buH+oOX/AM8/vj/8iH/DxH4E/wDR\nYvhj/wCFPZf/AByj/h4j8Cf+ixfDH/wp7L/45RRR9erdw/1By/8Ann98f/kQ/wCHiPwJ/wCixfDH\n/wAKey/+OUf8PEfgT/0WL4Y/+FPZf/HKKKPr1buH+oOX/wA8/vj/APIh/wAPEfgT/wBFi+GP/hT2\nX/xyj/h4j8Cf+ixfDH/wp7L/AOOUUUfXq3cP9Qcv/nn98f8A5EP+HiPwJ/6LF8Mf/Cnsv/jlH/Dx\nH4E/9Fi+GP8A4U9l/wDHKKKPr1buH+oOX/zz++P/AMiH/DxH4E/9Fi+GP/hT2X/xyj/h4j8Cf+ix\nfDH/AMKiy/8AjlFFH16t3D/UHL/55/fH/wCROR+JH/BSb4S2It10f4tfDeQybxI6eIrNtn/j9fOv\n7af/AAW/0X9nL4Wpe+DtW0T4keJtQk8i1sNIvobqO3P/AD0ndH+RKKK5amZYiOzPp8t8Ncoq00p8\n33x/+RPF/wBjj/g4j8XfE/4tWPhn4oeHNL8H2GpeZs1pLxPsdv8A3I53/g/36+y/+HmPgDf/AMlc\n+Hn/AIPbP/4qiisVm2JlDVnZW8McnptW5vvj/wDIno+jf8FFPgfeaPbzT/GD4YwzSRI7ofE1l8jH\n/trVwf8ABQ/4E/8ARYvhj/4U1j/8door0Fj61tz4ypwHlyk0nL74/wDyI7/h4j8Cf+ixfDH/AMKe\ny/8AjlH/AA8R+BP/AEWL4Y/+FPZf/HKKKPr1buH+oOX/AM8/vj/8iH/DxH4E/wDRYvhj/wCFPZf/\nAByj/h4j8Cf+ixfDH/wp7L/45RRR9erdw/1By/8Ann98f/kQ/wCHiPwJ/wCixfDH/wAKey/+OUf8\nPEfgT/0WL4Y/+FPZf/HKKKPr1buH+oOX/wA8/vj/APIh/wAPEfgT/wBFi+GP/hT2X/xyj/h4j8Cf\n+ixfDH/wp7L/AOOUUUfXq3cP9Qcv/nn98f8A5EP+HiPwJ/6LF8Mf/Cnsv/jlH/DxH4E/9Fi+GP8A\n4U9l/wDHKKKPr1buH+oOX/zz++P/AMiH/DxH4E/9Fi+GP/hT2X/xyj/h4j8Cf+ixfDH/AMKey/8A\njlFFH16t3D/UHL/55/fH/wCRD/h4j8Cf+ixfDH/wp7L/AOOUf8PEfgT/ANFi+GP/AIU9l/8AHKKK\nPr1buH+oOX/zz++P/wAiH/DxH4E/9Fi+GP8A4U9l/wDHKP8Ah4j8Cf8AosXwx/8ACnsv/jlFFH16\nt3D/AFBy/wDnn98f/kQ/4eI/An/osXwx/wDCnsv/AI5R/wAPEfgT/wBFi+GP/hT2X/xyiij69W7h\n/qDl/wDPP74//Ih/w8R+BP8A0WL4Y/8AhT2X/wAco/4eI/An/osXwx/8Key/+OUUUfXq3cP9Qcv/\nAJ5/fH/5EP8Ah4j8Cf8AosXwx/8ACnsv/jlH/DxH4E/9Fi+GP/hT2X/xyiij69W7h/qDl/8APP74\n/wDyIf8ADxH4E/8ARYvhj/4U9l/8co/4eI/An/osXwx/8Key/wDjlFFH16t3D/UHL/55/fH/AORD\n/h4j8Cf+ixfDH/wp7L/45R/w8R+BP/RYvhj/AOFPZf8Axyiij69W7h/qDl/88/vj/wDIh/w8R+BP\n/RYvhj/4U9l/8co/4eI/An/osXwx/wDCnsv/AI5RRR9erdw/1By/+ef3x/8AkQ/4eI/An/osXwx/\n8Key/wDjlH/DxH4E/wDRYvhj/wCFPZf/AByiij69W7h/qDl/88/vj/8AIh/w8R+BP/RYvhj/AOFP\nZf8Axyj/AIeI/An/AKLF8Mf/AAp7L/45RRR9erdw/wBQcv8A55/fH/5EP+HiPwJ/6LF8Mf8Awp7L\n/wCOUf8ADxH4E/8ARYvhj/4U9l/8cooo+vVu4f6g5f8Azz++P/yIf8PEfgT/ANFi+GP/AIU9l/8A\nHKP+HiPwJ/6LF8Mf/Cnsv/jlFFH16t3D/UHL/wCef3x/+RMLQv2/vgZDotjG/wAYPhmkkcCb0/4S\nS0/uf9dau/8ADwT4G/8ARYvhh/4U9p/8dooqVmlXsvuNJeHuXL7c/vj/APInk/7b37cPwd8Yfsc/\nFPSdJ+KHw91TU9Q8K6paWtrbeIbOaa6me0nVEREf53d/4KKKK+T4jxVR4lX7H7x4Q8G4Gnl9ZRlP\n419r+6vI/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('Kaggle-Score.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### kaggle score is 0.14503"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Discussion\n",
    "\n",
    "1. In this work, I didn't work with deep learning models. That might help increaseing the accuracy more.\n",
    "\n",
    "2. Feature Engineering, Outlier Removal, Skewness Reduction and Hyper Parameter Tuning had great impact\n",
    "   on the improvement of the Final Score.\n",
    "   \n",
    "3. Neural Network has an Inherent Tendency to overfit. Further improvement of the Final Score is deteriorated\n",
    "   due to this contingency.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# 5. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. From the project we did, we can say that ANN without any parameter selection provides the best result with a kaggle \n",
    "   score of 0.14503 However, with proper tuning of neural network size and nodes, the result may vary. \n",
    "   \n",
    "3. If I droup some column which is less correlation to the SalePrice, the accuracy may improve.\n",
    "   \n",
    "4. Also different algorithms such as XGBoost might provide higher accuracy in a regressor problem.\n"
   ]
  }
 ],
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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