STEP 3 Preprocessing the dataset 🔧.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 3: Preprocessing the dataset 🔧"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Checking the distribution of 'Sale Price'.\n",
    "\n",
    "* Log Transformation of the Dependant Variable\n",
    "\n",
    "* Detect outliers\n",
    "\n",
    "* Clean Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
      "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
      "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
      "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
      "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
      "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
      "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
      "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
      "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
      "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
      "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
      "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
      "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
      "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
      "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
      "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
      "       'SaleCondition', 'SalePrice'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Overview of the Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAIDrUWwAAAAAuB7FBgAAAIDrUWwAAAAAuB7FBgAAAIDrUWwAAAAAuB7FBgAAAIDrUWwAAAAAuB7FBgAAAIDrUWwAAAAAuB7FBgAAAIDrUWwAAAAAuF6M7QBAqKmslP78Z+nIEallS+n4cSkmRmrUSGrVSpo4UfJ4bKcEAABATazYAKf5+mtp/XqpokI6eVI6elTat0/65htp4ULzdQAAAIQWVmyA02zYYFZn7rxTGjpUWrXK/PnJk9I990grV0rdutnNCAAAgFOxYgPUUFUlbdwo9ewpxcae+rXYWGnQIFN8jh2zkw8AAAC1o9gANezebd6tueSS2r9+xRVmi9oXXwQ3FwAAAM6OYgPUsHGjFB0t9epV+9dTUqQuXcx2NK83uNkAAABwZhQb4L+8XrPNrHt3qUmTM3/f0KHS3r0MEQAAAAglFBvgv/LypP37pb59z/59l15qis/KlcHJBQAAgHOj2AD/tWGDOZ/mXMXGN0Rg/XozChoAAAD2UWyA/9qwwbw/k5h47u+94gpzkCdDBAAAAEIDxQaQOYAzL+/M09BOl5IiXXABQwQAAABCBcUGkFmtkfwvNpJZtdm3T/rqK2cyAQAAwH8UG0Cm2Jx/vtSypf+fYYgAAABA6KDYIOIVFZmDOeuyWiOZIQKXXWZKUXGxM9kAAADgH4oNIt6mTeZa12IjMUQAAAAgVFBsEPE2bJDatpXatav7Z9u1kzp2/O4dHQAAANhBsUFEO3ZM+vrr+q3W+Fx8sZSVJR0/HrBYAAAAqCOKDSJaZqZUVdWwYnPRRWbkM9PRAAAA7KHYIKJt3So1b24motVX585SXJy0fXvgcgEAAKBuKDaIaPn5ptR4PPW/R0yMdOGFFBsAAACbKDaIWJWV0t699RsacLqLLjKHdR440PB7AQAAoO4oNohY+/ebchOoYiOxagMAAGBLjFM3/vbbb3XHHXdU/z4nJ0czZ87UzTff7NQjgTopKDDXQBSbdu3MuzoUGwAAADscKzadO3fW/PnzJUmVlZUaOnSoRo4c6dTjgDrzFZu2bRt+L4/HrNpkZppVIAAAAARXULaiffHFF2rfvr1SU1OD8TjALwUFUsuWUuPGgbnfRRdJJSVm0hoAAACCKyjF5oMPPtBVV10VjEcBfisoCMw2NB/fezYrVwbungAAAPCP48Xm5MmTWrp0qcaMGeP0owC/VVVJhYWBLTaJiVJamvTpp4G7JwAAAPzjeLFZsWKFevTooVatWjn9KMBvBw9K5eWBLTaSWbVZt046fjyw9wUAAMDZOV5sPvjgA40fP97pxwB1kp9vrk4Um5MnpRUrAntfAAAAnJ2jxaa0tFSff/65Ro0a5eRjgDoL5Kjnmrp2lWJjpcWLA3tfAAAAnJ1j454lKT4+XqtXr3byEUC9FBSYc2fi4wN739hYqX9/ig0AAECwBWUqGhBqAj0RrabLL5c2bzbDCQAAABAcFBtEHCcmotV0xRXmumSJM/cHAADA91FsEHGKiqQTJ5wrNj16mIM/2Y4GAAAQPBQbRBzf4ICUFGfuHxUljRhhio3X68wzAAAAcCqKDSKOr9i0bevcM0aONM/Zts25ZwAAAOA7FBtEnIICKTFRatrUuWeMHGmuH3/s3DMAAADwHYoNIk5+vnPv1/h06CB16cIAAQAAgGCh2CCieL3OjnquKSND+uQTqaLC+WcBAABEOooNIsrhw1JZWfCKzdGj0po1zj8LAAAg0lFsEFF8gwOCUWyGDZM8HrajAQAABEOM7QBAMAWz2LRsKV1yiRkgcN99Z//eoiKpuLhu909MlJKS6p8PAAAgnFBsEFEKCqSEBKlZs+A8b8QI6ZlnpJIS89wzKS6WFi2q271Hj6bYAAAA+LAVDRHFNzjA4wnO8zIypPJyaeXK4DwPAAAgUlFsEDG8XjPqOSUleM+8/HIpNpbzbAAAAJxGsUHEOHBAOn48OO/X+DRpIg0ezAABAAAAp1FsEDF27jTXYBYbyWxH27jRFCsAAAA4g2KDiLFrl7kGu9iMGGGuS5cG97kAAACRhGKDiLFzpxQfL513XnCf27+/Gc3MdjQAAADnUGwQMXbuDO5ENJ+YGHNYJwMEAAAAnEOxQcTYtSv429B8RoyQvv1W2r3bzvMBAADCHcUGEeHAAfOPrWKTkWGurNoAAAA4g2KDiLBjh7naKjbdu5tn854NAACAMyg2iAhZWebaqpWd53s8ZtXm44+lqio7GQAAAMIZxQYRwVdsWrSwlyEjw2yH27zZXgYAAIBwRbFBRMjONqs1sbH2MvjOs2E7GgAAQOBRbBARsrOl1FS7GVJTzbs2FBsAAIDAo9ggImRlSWlptlNIo0ZJn3wiHT9uOwkAAEB4odgg7FVVSXv2hEaxGT9eKiuTli2znQQAACC8UGwQ9vbulU6csL8VTZKuvFJKSJDef992EgAAgPBCsUHYy84211BYsYmLk0aOlD74QPJ6bacBAAAIHxQbhD1fsQmFFRvJbEfLyZG2bLGdBAAAIHxQbBD2fGfYhEqxGTfOXNmOBgAAEDgUG4S97GxzMGfTpraTGCkp0qWXmu1oAAAACAyKDcJeVpbUoYPtFKe66irpiy+kgwdtJwEAAAgPFBuEvexsqWNH2ylONX68GUP9n//YTgIAABAeKDYIa16vKTahtmLTv7/Upg3b0QAAAAKFYoOwdvCgVFISeis2UVFmiMDChVJFhe00AAAA7kexQVjzjXoOtRUbyWxHO3zYvGsDAACAhqHYIKz5Rj2HYrEZNUqKiWHsMwAAQCBQbBDWfCs2obYVTZISE6WhQ3nPBgAAIBAoNghr2dlSs2ZS8+a2k9Ru/Hhp61YpJ8d2EgAAAHej2CCsZWWZ1RqPx3aS2l11lbkuW2Y3BwAAgNtRbBDWQnHUc00XXihdcIG0dKntJAAAAO5GsUFYy8oK7WIjmVWbzz+XTpywnQQAAMC9KDYIW0eOmH9CcXBATePHSydPSjt22E4CAADgXhQbhK1QPsOmpqFDpYQEafNm20kAAADci2KDsOU7wybUV2xiY6UrrpC2bJG8XttpAAAA3Ilig7DllhUbSRo+XCoqknJzbScBAABwJ4oNwlZWlhQfL7VubTvJuf3gB+bKdjQAAID6odggbPlGPYfqGTY1tWljslJsAAAA6odig7AV6mfYnK5XL2n3bunoUdtJAAAA3Idig7CVlRX6gwNq6tXLDA/YutV2EgAAAPeh2CAslZRIBw64a8Xm/POlxES2owEAANQHxQZhyU0T0XyiosyqzdatUmWl7TQAAADu4mixKS4u1syZMzVmzBiNHTtWGzZscPJxQDVfsXHTVjRJ6tlTKi2VvvnGdhIAAAB3iXHy5o888oiuuOIKPfvsszp58qTKysqcfBxQzY0rNpJ08cVSdLSUmSldeKHtNAAAAO7hWLE5duyY1qxZo8cee0ySFBsbq9jYWKceB5wiK0tq1Ehq1y74z66o+K5Y+au01FwbNzaFZssWafLkwGcDAAAIV44Vm5ycHLVo0UK/+c1vtGPHDvXo0UP33nuvmjRp4tQjgWrZ2eZl/CgLb5GVlEirVtXtM4MGfffrnj2lf/xD2r/fHYeLAgAAhALHfuyrqKjQtm3bdOONN+q9995TfHy8XnrpJaceB5wiK8t929B8evc2V6ajAQAA+M+xYtO2bVu1bdtWffr0kSSNGTNG27Ztc+pxwCmys903OMCnTRspOZliAwAAUBeOFZvWrVurbdu2+vbbbyVJX3zxhbp06eLU44BqJ05IBQXuXbGRzNjnr7+WmLcBAADgH0ffQLjvvvt05513asKECdq+fbumTZvm5OMASdKePebq1hUbyRSbigppxw7bSQAAANzB0XHPF110kebNm+fkI4Dvceuo55ouuMBMSNu8Werb13YaAACA0GdhZhTgrKwsc3VzsYmJMWfabN4seb220wAAAIQ+ig3CTna2OeQyLc12kobp0UM6csS8LwQAAICzo9gg7GRnS6mpZtXDzbp1M9evvrKbAwAAwA0oNgg7WVnuHhzg06qVlJRkpqMBAADg7Cg2CDt79kjnn287RcN5PGbV5quvpKoq22kAAABCm8s36yCSFRVJxcWn/llVlZSXJyUmfjcdzae0NHjZAqVbN2nVKik/3/3vDAEAADiJYgPXKi6WFi069c+OHDHnvxQVff9rgwYFL1ug1HzPhmIDAABwZmxFQ1g5dMhck5Ls5giUli3NuzYMEAAAADg7ig3CSlGRubZoYTdHIHXrJu3cyXs2AAAAZ0OxQVjxFZtwWbGRpAsvlI4fl3JzbScBAAAIXRQbhJWiIqlRIykhwXaSwOE8GwAAgHOj2CCsFBWZ1RqPx3aSwElKktq0odgAAACcDcUGYcVXbMKN7z2bykrbSQAAAEITxQZhJZyLTVmZlJNjOwkAAEBootggbFRVSYcPh2exufBCc2U7GgAAQO0oNggbxcWm3IRjsTnvPKldO4oNAADAmVBsEDbCcdRzTRdeKO3axXs2AAAAtaHYIGwcOmSu4VpsunWTTpyQsrJsJwEAAAg9FBuEDd+KTYsWdnM4hfNsAAAAzoxig7DhO5yzSRPbSZzRtKmUmip9/bXtJAAAAKGHYoOwUVRkVmvC6XDO03XrZt6zKS+3nQQAACC0UGwQNoqKpObNbadwVrduptTwng0AAMCpKDYIG74Vm3DWtatZkeI9GwAAgFNRbBAWKiulI0fCdyKaT0KClJIiffut7SQAAAChhWKDsBDOh3OermNHKTtb8nptJwEAAAgdFBuEhXA/nLOmDh2kY8ek3FzbSQAAAEIHxQZhIZKKTceO5pqZaTUGAABASKHYICwcOmSukVBsUlOlmBiKDQAAQE0UG4SFoiIpLi58D+esKSZGSkuTNm2ynQQAACB0UGwQFnxn2ITz4Zw1deggbdliBiYAAACAYoMwEQln2NTUsaMZIMB5NgAAAAbFBmHBt2ITKXwDBNautRoDAAAgZFBs4Hq+wzkjacWmbVvzPtGaNbaTAAAAhAaKDVzvyBFzWGUkTETziYqSevak2AAAAPhQbOB6kXSGTU29e0sbN0rl5baTAAAA2EexgetFcrEpKzPT0QAAACIdxQauF6nFpk8fc2U7GgAAAMUGYeDQIXM4Z3y87STBdf75ZmACxQYAAIBigzBw+LD5AT9SDuf08Xik/v0Z+QwAACBRbBAGDh2KrDNsaurfX9q8WSottZ0EAADALooNXM+3YhOJ0tPNOT4bN9pOAgAAYBfFBq7mO5wzUlds0tPNlfdsAABApKPYwNUOHzaHc0bqik1qqtSuHcUGAACAYgNXi9RRzzWlp1NsAAAAKDZwNYqNKTZffWW25AEAAEQqig1cjWLz3Xs269fbzQEAAGATxQauVlQkNW4ceYdz1tSvn7myHQ0AAEQyig1c7dChyF6tkaRWraROnSg2AAAgslFs4GqHD1NsJAYIAAAAUGzgaqzYGOnpUna2tG+f7SQAAAB2UGzgWidPSkePUmyk796z2bjRbg4AAABbKDZwrb17zeGcFBupd29z3bTJbg4AAABbKDZwrYICc23Rwm6OUNCypZSaKmVm2k4CAABgB8UGruUrNqzYGH36sGIDAAAiV4yTNx8+fLgSEhIUFRWl6OhozZs3z8nHIcJQbE7Vp4/00UfSiRNSXJztNAAAAMHlaLGRpFdeeUUt2CsEBxQWmoM5Gze2nSQ09O4tVVRIO3aYkgMAABBJ2IoG18rPZ7WmJl+ZYTsaAACIRI4Xm1tvvVWTJk3S22+/7fSjEGEKCig2NXXtalavKDYAACASOboV7a233lJycrIOHjyoqVOnqnPnzkpPT3fykYggBQVS9+62U4SOmBipRw8mowEAgMjk6IpNcnKyJKlly5YaOXKkMvmJCwFy4oR04AArNqfzTUbzem0nAQAACC7His3x48d17Nix6l9/9tln6tq1q1OPQ4TJzzdXis2p+vSR9u83gxUAAAAiiWNb0Q4ePKjp06dLkiorK3XVVVdp6NChTj0OESYnx1wpNqfq3dtcN22S2rWzmwUAACCYHCs27du314IFC5y6PSKcr9gwSfxUvslomZnSmDF2swAAAAQT457hSrm55tq8ud0coSYpSWrfnsloAAAg8knoqvkAACAASURBVFBs4Eo5OVJiIodz1sY3QAAAACCSUGzgSrm5UkqK7RShqXdvaccOMzkOAAAgUlBs4Eo5OVLbtrZThKY+faTKSmnbNttJAAAAgodiA1fKyWHq15n4BgiwHQ0AAEQSig1cp6zMnNXCVrTaXXCBFB9vJqMBAABECooNXCcvz1zZila76GipZ09WbAAAQGRx7BwbwCm+Uc8pKdKxY3az2FRRIWVn1/61Ll2kRYukrCzJ4/nuzxMTOdQUAACEJ4oNXMd3OGfbttKuXXaz2FRSIq1aVfvXvF6pqEh6551Tz/oZPZpiAwAAwhNb0eA6vmLDOzZnlpZmrr7VLQAAgHBHsYHr5OZKLVqYF+RRO4oNAACINBQbuE5Oznc/uKN2TZqY8kexAQAAkYJiA9fJzZXat7edIvSlpVFsAABA5PCr2Hz99ddO5wD8xoqNf9LSpL17pfJy20kAAACc51ex+f3vf6/JkyfrjTfeUHFxsdOZgDMqLZUOHGDFxh9paVJVlVRQYDsJAACA8/wqNm+99ZaeeOIJFRYW6rrrrtOcOXP02WefOZ0N+B7f4ZwUm3PzrWr5psgBAACEM7/PsenYsaNuv/129ezZUw8//LC2bdsmr9er2bNna9SoUU5mBKr5fkhnK9q5tW4txcbyng0AAIgMfhWbHTt2aN68eVq+fLkGDx6sF154QT169NDevXt1ww03UGwQNL4f0lmxObeoKHPWj2+VCwAAIJz5VWzmzp2rH/7wh5o9e7YaN25c/efJycmaNWuWY+GA0/lWbFJTpf377WZxg7Q0acMGyeuVPB7baQAAAJzjV7F56aWX1LhxY0VHR0uSqqqqdOLECcXHx+uaa65xNCBQU06O1LKlOacF55aWJn36qXT4sJSUZDsNAACAc/waHjB16lSVlZVV/760tFRTp051LBRwJpxhUze+d5F4zwYAAIQ7v4rNiRMnlJCQUP37hIQElZaWOhYKOBPOsKkbig0AAIgUfhWb+Ph4bd26tfr3W7ZsOeVdGyBYWLGpm/h4s3WPYgMAAMKdX+/Y/Pa3v9WsWbPUpk0bSdL+/fv19NNPOxoMON3x49LBg6zY1FVaGpPRAABA+POr2PTu3VsLFy7U7t275fV61blzZzVq1MjpbMApGPVcP2lpUmamdPKk7SQAAADO8fuAzs2bNysvL0+VlZXavn27JDERDUFFsamftDQz7jk/33YSAAAA5/hVbO666y7l5OSoe/fu1SOfPR4PxQZB5TvDhq1odZOaaq5sRwMAAOHMr2KzZcsWffjhh/Jwwh8s8q3YUGzqpnVrKS6OAQIAACC8+TUVrWvXrtrPMe+wLCdHatVKYiBf3URFSSkpFBsAABDe/FqxKSoq0vjx49W7d+9Thga88MILjgUDTpeTw/s19ZWWJq1bZ961AQAACEd+FZvbbrvN6RzAOeXmSh072k7hTmlp0sqVUkEB/w4BAEB48msr2oABA5SamqqKigoNGDBAvXr10sUXX+x0NuAUOTm8X1Nfvn9v/x1oCAAAEHb8KjbvvPOOZs6cqfvvv1+StHfvXk2fPt3RYEBNJSVSURFb0erLNxltxw67OQAAAJziV7F544039NZbb6lp06aSpI4dO+rQoUOOBgNqYiJaw8THm8EL27bZTgIAAOAMv4pNbGysYmNjq39fUVHhWCCgNr4zbFixqb+0NLaiAQCA8OXX8ID09HS98MILKisr02effaY333xTw4cPdzobUM23YkOxqb/UVCkzUzp+XGrSxHYaAACAwPJrxebOO+9UixYtdOGFF+rtt9/WlVdeqdtvv93pbEA134qN710R1F1amlRVJW3dajsJAABA4Pm1YhMVFaXrr79e119/vdN5gFrl5kpt2khxcbaTuJdvtWvTJik93W4WAACAQPOr2AwfPlwej+d7f/7xxx8HPBBQG0Y9N1zLllJCgtmOBgAAEG78Kjbvvvtu9a9PnjyphQsX6siRI46FAk6XkyN16WI7hbtFRUndupkVGwAAgHDj1zs2SUlJ1f8kJyfr5ptv1qpVq5zOBlTLzWVwQCBcdJEpNl6v7SQAAACB5deKzdYabxtXVVVpy5YtKikpcSwUUNPRo9Lhw2xFC4Tu3aU33pD27JE6dLCdBgAAIHD8KjaPPfbYdx+IiVFqaqqeeeYZx0IBNWVlmWunTlZjhIWLLzbXzEyKDQAACC9+FZvXXnvN6RzAGWVnmys/iDdct27mummTNGGC3SwAAACB5Fexefnll8/69alTpwYkDFAb34pNx442U4SHpk2lzp0ZIAAAAMKPX8Vmy5Yt2rx5s4YPHy5JWrZsmfr376927do5Gg6QTLFp3NicY4OG69OHkc8AACD8+FVsioqKNG/ePDVt2lSSNGPGDM2aNUuPPPKIo+EAyWxF69BBquUoJdRDnz7Se+9JJSXmXBsAAIBw4Ne45/z8fMXGxlb/PjY2Vnl5eY6FAmrKymIbWiD17m3GPW/ZYjsJAABA4Pi1YjNx4kRNnjxZI0eOlMfj0eLFi3XNNdc4nQ2QZIpNv362U4SPvn3NdeNGaeBAu1kAAAACxa9i86tf/UpDhw7V2rVrJUl/+MMfdLFvbizgoJIS6cABVmwCqWNHqWVLac0a6Ze/tJ0GAAAgMPzaiiZJpaWlatq0qX72s5+pbdu2ysnJcTIXIIlRz07weKT0dOnLL20nAQAACBy/is3zzz+vP//5z3rppZckSeXl5brrrrscDQZIjHp2Snq6tHWrWREDAAAIB34Vm8WLF+tPf/qT4uPjJUnJyckq4SciBAHFxhkDBkhVVdL69baTAAAABIZfxaZRo0byeDzy/Hfe7vHjxx0NBfhkZ0uxsVJysu0k4SU93VzXrLGbAwAAIFD8KjZjx47V/fffr+LiYr3zzjuaOnWqrr/+er8eUFlZqWuuuUa/5C1l1ENWlnm/Jsrvt8Hgj+Rk6fzzec8GAACEj3NORfN6vRo3bpy+/fZbJSQkaPfu3Zo5c6aGDBni1wNeffVVdenSRceOHWtwWEQezrBxzoABFBsAABA+zllsPB6Ppk+frnnz5vldZnwKCwv1ySefaNq0afrb3/5W34yIYNnZ0oQJtlOEp/R06Z//NOO0W7WynQYAAKBh/Nrg06dPH2VmZtb55o8++qjuuusuRbGPCPVQWirt3cuKjVMGDDBX3rMBAADhwK8DOlevXq2///3vSk1NrZ6MJkn//ve/z/iZZcuWqUWLFurZs6dWr17d8KSIOL4zbCg2zujXz5xps2aNNHas7TQAAAANc9Zik5+fr5SUFP3f//1fnW+8fv16LV26VCtWrNCJEyd07Ngx3XnnnXriiSfqHRaRxTfqmcM5ndGsmXTRRbxnAwAAwsNZ94hNnz5dkpSamqrHHntMqampp/xzNnPmzNGKFSu0dOlSPfXUUxo0aBClBnXCio3z0tPNio3XazsJAABAw5y12Hhr/LSTk5PjeBigpqwsqVEjqV0720nC14AB0r590p49tpMAAAA0zFm3ovkO5Dz913U1cOBADRw4sN6fR2TKyjJnrURH204Svmoe1MmWPwAA4GZnLTY7duzQpZdeKq/XqxMnTujSSy+VZFZyPB6P1q9fH5SQiEzZ2fyw7bTevaXYWPOezeTJttMAAADU31mLzfbt24OVA/ierCymdTktLk7q25cBAgAAwP04YAYhqaxMKihgcEAwpKdL69ZJlZW2kwAAANQfxQYhyfcyO1vRnDdggHTsmLRjh+0kAAAA9UexQUhi1HPw1BwgAAAA4FYUG4Qk3+GcFBvndetmDuvkPRsAAOBmFBuEpKwsM+Y5JcV2kvAXFSX178+KDQAAcDeKDUJSdrbUvr0Uc9a5fQiUAQOkTZvM0AYAAAA3otggJGVlsQ0tmNLTpfJyU24AAADciGKDkESxCa4BA8yV7WgAAMCtKDYIOSdPSvn5jHoOprQ0qW1bBggAAAD3otgg5OTkSF4vKzbB5PGY7WgUGwAA4FYUG4QcRj3bcdll0ldfSfv22U4CAABQdxQbhBxfsWErWnBlZJjr0qV2cwAAANQHxQYhJzvbnK2SlmY7SWS59FKpeXNp8WLbSQAAAOqOYoOQk5VlSk2jRraTRJboaGn4cFNsvF7baQAAAOqGYoOQk5XFNjRbMjLM8IZdu2wnAQAAqBuKDUIOZ9jY43vPZskSuzkAAADqimKDkFJeLuXlUWxsueACs1pGsQEAAG5DsUFIyc2VqqooNrZ4PGbVZulSqbLSdhoAAAD/UWwQUhj1bF9GhnT4sLRune0kAAAA/qPYIKRkZ5srKzb2DB9urmxHAwAAbkKxQUjJyjLbodq3t50kcrVpI/XpQ7EBAADuQrFBSNm9W0pJkWJjbSeJbCNHSp99Jh0/bjsJAACAfyg2CCm7dpnJXLArI0M6eVL69FPbSQAAAPxDsUFI+eYbik0ouPxys2q2eLHtJAAAAP6h2CBkHD0q7d1LsQkFCQnS4MG8ZwMAANyDYoOQ8c035kqxCQ0ZGdLGjdL+/baTAAAAnBvFBiFj1y5zpdiEhowMc1261G4OAAAAf1BsEDJ8xaZLF7s5YPTvL513HtvRAACAO1BsEDJ27ZKSk6VmzWwngSRFR5vDOhcvlrxe22kAAADOjmKDkMGo59CTkSFlZ3/3/hMAAECootggZFBsQo/vPRvGPgMAgFBHsUFIOH5cysuj2ISarl2lzp2l996znQQAAODsKDYICd9+a64Um9Di8Ug33mgGCBQW2k4DAABwZhQbhARGPYeuH/9YqqqS3nnHdhIAAIAzo9ggJDDqOXRdfLHUt6/0xhu2kwAAAJwZxQYhYdcuqWVLKSnJdhLU5ic/kb788rsCCgAAEGooNggJTEQLbTfcYN63efNN20kAAABqR7FBSKDYhLa0NOnKK02x4bBOAAAQimJsBwBOnJD27KHYBENFhTlwsy4SE80WwR//WPrFL6T166V+/ZzJBwAAUF8UG1i3e7dZBaDYOK+kRFq1qm6fGT3aFJvJk6Xp082qDcUGAACEGraiwTpGPbtDUpI0bpz01ltSZaXtNAAAAKei2MA6io17/OQnUkGBtHy57SQAAACnotjAul27pPPOM+OeEdquukpq1owzbQAAQOih2MA630Q0j8d2EpxLfLw0aZL0z39KZWW20wAAAHyH4QGwbtcuqX9/2yngr5/8RHrlFenDD03JOV1RkVRcXLd7+iavAQAA1BfFBlaVl0tZWeYASLjDsGFScrLZjlZbsSkulhYtqts9fZPXAAAA6outaLAqO9tM2GJwgHvExJgi+v77ZnUGAAAgFLBig4Cry1akzz4z1+Rk5/Ig8G6+WfrjH6W//lWaM8d2GgAAAIoNHFCXrUjLlplrq1bO5UHg9e0rXXml9Nxz0qxZZhUHAADAJraiwap9+6S4OKl1a9tJUFe33262Es6fbzsJAAAAKzawbP9+U2oqK80PyXVRWupMJvhnwgSpUyfpmWek666znQYAAEQ6ig2s2rdPSk2VSkqkVavq9tlBg5zJBP9ER0szZ0p33CGtXcvIbgAAYJdjW9FOnDihyZMn6+qrr9b48eP17LPPOvUouFRVlXTgANvQ3OyWW6RmzcwgAQAAAJscW7GJjY3VK6+8ooSEBJWXl+vHP/6xhg4dqr59+zr1SLjMoUNmCxrFJrRVVJx9m+DkydLrr0u33Wam27FFEAAA2OBYsfF4PEpISJAkVVRUqKKiQh6Px6nHwYX27zfXNm3s5sDZnWubYJcupvw88IB0zTVsEQQAAHY4OhWtsrJSEydO1ODBgzV48GD16dPHycfBZXzFhhUbd2vdWurTR1qxQjp50nYaAAAQqRwtNtHR0Zo/f76WL1+uzMxMff31104+Di6zb585/6R5c9tJ0FAjRpiVnS+/tJ0EAABEqqCcY5OYmKiBAwdq5cqVwXgcXMI36jmK05Rcr2tXqX176eOPJa/XdhoAABCJHPuR8tChQyouLpYklZWV6fPPP1fnzp2dehxcaN8+3q8JFx6PWbXJz5dWr7adBgAARCLHis2+ffs0ZcoUTZgwQZMnT9bgwYM1bNgwpx4Hl6mq+m7FBuGhf38z+vm112wnAQAAkcixqWjdu3fXe++959Tt4XJHjkjl5azYhJNGjaRhw6QFC6Thw83BqwAAAMHC2w2wYt8+c2XFJrz84AdSfLy0aJHtJAAAINJQbGBFQYG5tmtnNwcCKyFBuu46ac0a6eBB22kAAEAkodjAioICKS6OUc/h6KabzHXJErs5AABAZKHYwIq9e6W2bc00LYSXtm2lAQOkTz+Vjh2znQYAAEQKig2sKChgG1o4Gz1aOnlS+uQT20kAAECkoNgg6MrKpMOHzf9nH+EpJUXq1UtatswUHAAAAKdRbBB0hYXmSrEJb2PGmK1on31mOwkAAIgEFBsEna/YsBUtvF1wgdSli7R4sVRZaTsNAAAIdxQbBF1BgRQVxRk2kWD0aDP2ed0620kAAEC4o9gg6AoLpTZtpOho20ngtF69zMrcokWS12s7DQAACGcUGwRdYSHv10SKqChp1CgpN1fats12GgAAEM4oNgiqykpp3z6KTSQZMMAcxMqBnQAAwEkUGwTV/v1SVRXFJpLExEjDhpkVm7w822kAAEC4otggqAoKzJWJaJHliiuk2FhWbQAAgHMoNggqzrCJTAkJ0mWXSV9+KR05YjsNAAAIRxQbBFVhoXnfonFj20kQbCNGmHesli+3nQQAAIQjig2CqrCQbWiRKjlZ6t1b+uQT6eRJ22kAAEC4odggaLxeU2ySk20ngS0ZGVJJibRqle0kAAAg3FBsEDSHD0tlZazYRLKuXaXzz5c+/thMxwMAAAgUig2ChsEB8HjMqk1hobR1q+00AAAgnFBsEDS+YsOKTWTr148DOwEAQOBRbBA0BQVmGlpiou0ksMl3YOeOHVJOju00AAAgXFBsEDS+iWgej+0ksM13YOfHH9tOAgAAwgXFBkFTWMj7NTASEqTBgzmwEwAABA7FBkFRWmp+gKXYwGf4cHNg54oVtpMAAIBwQLFBUDA4AKdLTpZ69jTF5sQJ22kAAIDbUWwQFAUF5sqKDWoaPlwqLpY+/NB2EgAA4HYUGwRFYaEUHS21amU7CULJxRebsvvyy5LXazsNAABwM4oNgqKwUGrTxpQbwMfjMaOfMzOlVatspwEAAG5GsUFQ+EY9A6cbNEhq1kz64x9tJwEAAG5GsYHjKiqk/fvNy+LA6Ro3ln70I+mf/5Ryc22nAQAAbkWxgeP27ZOqqlixwZn97GfmHZs//cl2EgAA4FYUGzjON+qZiWg4k/btpauvll580Zx5BAAAUFcUGziOUc/wx8yZ0sGD0ltv2U4CAADciGIDxxUWSi1aSHFxtpMglP3gB1KvXtKzzzL6GQAA1B3FBo4rLGS1Bufm8ZhVm02bpBUrbKcBAABuQ7GBoyorzVa0lBTbSeAGP/mJ1LKl9PTTtpMAAAC3odjAUfv3S+XlUmqq7SRwg/h4afp0acEC6auvbKcBAABuQrGBo/LyzJViA39Nny7FxrJqAwAA6oZiA0fl5Zl3JzjDBv5q00aaMkV65RWz4gcAAOAPig0clZdnflCNjbWdBG4ye7ZUVib97//aTgIAANyCYgNH5eWxDQ111727NGGC9PzzHNgJAAD8Q7GBY06ckA4coNigfubMMf/9vPqq7SQAAMANKDZwTH6+OWiRYoP6GDpU6t9feuopqarKdhoAABDqKDZwDBPR0BAej3TnndLXX0vvv287DQAACHUUGzgmL88MDWjVynYSuNV110kdOkhPPGE7CQAACHUUGzgmL09KSZGi+K8M9RQTI91+u7RypbR6te00AAAglPEjJxyTn882NDTcrbdK550nPfmk7SQAACCUUWzgiOJi6ehRs2IDNESzZtK0adK770o7d9pOAwAAQhXFBo5gcAAC6Y47pLg4ae5c20kAAECootjAERQbBFJysjR9uvTGG9JXX9lOAwAAQhHFBo7IyzNbiBITbSdBuLj7bik+XnroIdtJAABAKKLYwBF5eazWILBat5ZmzJDeekvats12GgAAEGocKzYFBQW66aabNHbsWI0fP16vvPKKU49CiKmsZCIanHHnnVJCgvTgg7aTAACAUONYsYmOjtavf/1rLVy4UG+//bbefPNN7dq1y6nHIYTs2SOVl1NsEHitWkmzZknvvCNt3mw7DQAACCWOFZs2bdqoR48ekqSmTZuqc+fO2rt3r1OPQwjxvdxNsYETZs8272498IDtJAAAIJQE5R2b3Nxcbd++XX369AnG42DZjh2Sx8MZNnBGixbS7bdL8+ZJGzfaTgMAAEKF48WmpKREM2fO1G9/+1s1bdrU6cchBHz1lXnROzbWdhKEqzvukM47j1UbAADwHUeLTXl5uWbOnKkJEyZo1KhRTj4KIeSrr1itgbOaN5fmzJHmz5fWrbOdBgAAhALHio3X69W9996rzp07a+rUqU49BiGmtFTKyuL9Gjhv1iwpKUm6917bSQAAQChwrNisW7dO8+fP16pVqzRx4kRNnDhRy5cvd+pxCBHbtklVVRQbOC8xUfrd76RFi6SFC22nAQAAtsU4deP+/fvrK994LEQM3wheig2CYcYM6U9/MtvSMjKkRo1sJwIAALYEZSoaIsfmzVJcnNSmje0kiASxsdKTT0rbt0svvmg7DQAAsIlig4DavFnq2lWK4r8sBMmECdKIEdLvfy8dOmQ7DQAAsIUfPxFQmzdL3brZToFI4vFITz0lHT4sPfSQ7TQAAMAWx96xQeQ5cEAqLJS6d7edBJGgqEgqLja/Pu886Uc/kv7f/5Ouvlrq0qX2zyQmmklqAAAg/FBsEDC+wQHdupmxz4CTiovNRDSfSy+V/vUv6fbbzVCB2oweTbEBACBcsRUNAbNpk7myYgMbEhOlceNMwd62zXYaAAAQbBQbBMy6dVK7dkxEgz3Dh0utWkn/+IdUWWk7DQAACCaKDQJm3TqpXz/bKRDJGjWSJk+W8vOlFStspwEAAMFEsUFAHDsm7dhBsYF9ffua7ZALFkhHj9pOAwAAgoXhAQiITZskr5dig/qpqJCys+v2mTMNqPB4zIS0uXOl996Tbrqp4fkAAEDoo9ggINatM9d+/aTycrtZ4D4lJdKqVXX7zKBBZ/5aSoo0bJi0dKl0xRVSx44NigcAAFyArWgIiHXrpORkMzwACAUTJkjNmkl//7tUVWU7DQAAcBrFBgHhGxzg8dhOAhjx8dKkSdLu3dLq1bbTAAAAp1Fs0GAlJdL27bxfg9AzcKDUqZP07rscGgsAQLij2KDBMjPNVh+KDUJNVJR0441mat+//207DQAAcBLFBg1Wc3AAEGo6dJCGDJGWLZO+/tp2GgAA4BSKDRps3TqpTRspNdV2EqB211wjNW4sPfigGUsOAADCD8UGDbZunXTppQwOQOhq1sxMSfvsM+n9922nAQAATqDYoEFKS6Vt29iGhtB35ZVS587SnXdKJ0/aTgMAAAKNYoMGycyUKispNgh90dHSvfea92xeeMF2GgAAEGgUGzQIgwPgJsOHSxkZ0gMPSIcO2U4DAAACiWKDBlm3TmrVSmrf3nYS4Nw8HunJJ6XDh6W5c22nAQAAgUSxQYOsX8/gALhL797SrbdKzz/P+GcAAMIJxQb1VlYmbdnCNjS4R0WFlJ0t/eIXUlycdNtt5vdn+6eoyHZqAADgjxjbAeBemzebHxQpNnCLkhJp1Srz64wMaf586dlnpW7dzvyZ0aOlpKTg5AMAAPXHig3qjcEBcLOMDKlFC+kf/5CqqmynAQAADUWxQb2tW2d+MOzQwXYSoO5iY6Vrr5Vycr5bxQEAAO5FsUG9MTgAbpeeLnXsKC1YwKGdAAC4HcUG9XLihHnHhm1ocDOPR5o0yQwIWLbMdhoAANAQFBvUy5YtUnk5xQbu162b1LOn9J//mOECAADAnSg2qBcGByCcTJoklZZKCxfaTgIAAOqLYoN6Wb9eat5c6tTJdhKg4VJTpUGDzHa0gwdtpwEAAPVBsUG9rFvH4ACEl6uvNtcFC+zmAAAA9UOxQZ2VlUmZmWxDQ3hp0UIaPlxavdqMgAYAAO5CsUGdrV1rRuNefrntJEBgjRkjxcdL//qX7SQAAKCuKDaos5UrzXXIELs5gEBLSJDGjpW2bpV27LCdBgAA1AXFBnW2cqV08cVSy5a2kwCBN2yY2Zb27rtSVZXtNAAAwF8UG9RJZaX0+edsQ0P4atRImjhR2rPHvG8DAADcgWKDOtmyRTpyRLriCttJAOcMGCB17CjNmycdO2Y7DQAA8AfFBnXy6afmyooNwllUlHTDDVJxsfTcc7bTAAAAf1BsUCcrV0ppaVKHDraTAM7q1Em67DLpr3+Vdu60nQYAAJxLjO0AcA+v1xSbK6/kYE5EhmuvNWc2zZ4t/fvfzj6rqMisEPkrMVFKSnIuDwAAbkOxgd+ysqT8fLahIXKcd550223SY49J//mPOefGKcXF0qJF/n//6NEUGwD4/+3deXiTVb4H8G+60oWlhS6I0AFtHWXYVBBpUWlpC6SlrHdwxAVhcGQUZdFhUR9lCirXEQHBQRTxqiDILr1XHAuVlkVgQDrIeocWCpcW7L6mpD33j9+ThNIWuiR9m+T7eZ7zJLzNm/eXk5Dkl3Pe3yG6EaeiUYOZ1q9h4QByJpMmAXffDbz8sixMS0RERK0TExtqsLQ0oEMHoGdPrSMhajmensCSJcCZM8CHH2odDREREdWHiQ01WGoqEB4uFaOInIleDwwfDrz1FpCTo3U0REREVBd+RaUGuXYNOH2a59eQc9LpZNSmrEwKCSildURERER0MyY21CD79sklz68hZ3XPPcDrrwPr1kkJaCIiImpdmNhQg6SmyrkGDz6odSRE2pk/Hxg6FHjhBSkDTURERK0HExtqkLQ0YMAASW6IdWU2OgAAHkBJREFUnJWrK/Dll1JEY/x4oLhY64iIiIjIhIkN3VZpKXD0KKehEQFAUBDw9dfA//4v8Kc/8XwbIiKi1oKJDd3WwYOA0cjCAUQmjz4KLFgg59usXq11NERERAQwsaEGSEuTqlCDBmkdCVHrMXcuEBMDTJ8O/Pyz1tEQERERExu6rdRUoHdvoH17rSMhaj1cXOR8m44d5Xyba9e0joiIiMi52SyxmTt3Lh5++GHExcXZ6hDUAq5fl6loPL+GqLaAADnfJisL6N+fIzdERERaslliM2bMGHzyySe2untqIT//LMUDmNgQ1W3wYJmuWVUl0zW//lrriIiIiJyTzRKb/v37oz3nLtm91FS5ZOEAovo9+CBw5AjwwAPA448Df/mLJDpERETUcniODd3Srl1AWBhwxx1aR0LUugUFAcnJwLRpwOLFwIgRQF6e1lERERE5DyY2VK+iImDPHmDkSK0jIbIPHh7AihVSAjolBbjnHuCVV4Bz57SOjIiIyPExsaF67dolxQOY2BA1zpQpwL59wCOPAEuWyKhnZKScf2MwaB0dERGRY2JiQ/XasUNK2T78sNaRENmfBx8ENm+WimkLFwIZGXL+zZ13AlOnAt9+C5SVaR0lERGR47BZYjNz5kxMmDABGRkZeOSRR/DNN9/Y6lBkA0YjkJQE6PWAm5vW0RDZr86dgXnzgH//W0ZBTSM3I0cCnTrJ5erVwNWrWkdKRERk32z2lfX999+31V1TC9i3D8jP5zQ0ImtxcQFiYqQZDMCPP8qojam5ucno6IgRgL+/1tESERHZH05Fozrt2CEnQsfEaB0JkePx9JT/W8uXyxS19HTgD38ADhwAXnsNWLdOflggIiKihuMkI6pFKWD7diAqCmjbVutoiBybTgf06gUsWADcey/wP/8jC37u2yfrR+n1QLt2WkdJRETU+nHEhmo5fVrOB+A0NKKW5e8PPPEE8Ne/yrS0vXuBxEQgM1PryIiIiFo/JjZUy44dchkXp20cRM6qY0dg4kSZlubmBrz3HnDkiNZRERERtW6cika17NgBPPCAlKUlcnZGI3DhQuP2adcO8PNr/rG7dAHmzAH+/nepnJadLVPTdLrm3zcREZGjYWJDNeTkyAnMb76pdSRErUNpKXDwYOP2iY21TmIDSJI0Ywbw1VdSPS07G3jqKevcNxERkSNhYkM1JCVJ8QCeX0PUeri7A08/LWvibN0KXLsmhQVCQrSOjIiIqPVgYkM17NgBdOsG9OmjdSRE9qsp09fKy2/9d51ORoKCgmRa2rPPAqmpgI9P0+MkIiJyJExsyKy8HPj+e2DyZM7hJ2qOpkxfGziwYbfr2xf44x+BVauAsWMta04RERE5O1ZFI7PkZEluOA2NqHXr2xd4+21g1y7gmWeA6mqtIyIiItIeR2zIbMcOWZDz0Ue1joSIbuf3v5eEZu5coFMnYOlSjrQSEZFzY2JDAOQL0rffAsOHc1oLkb34y1+Aq1eBJUvk3Jv587WOiIiISDtMbAgA8OOPUkY2IUHrSIiooXQ6Wbzz2jVZzDMgAJg6VeuoiIiItMHEhgAAK1YA/v7A6NFaR0JEjeHiAqxZA+TlAc8/LwvrjhihdVREREQtj8UDCJcuAdu2STU0Ly+toyGixnJ3BzZskDLtv/89cPy41hERERG1PCY2hFWr5Byb55/XOhIiaipfXzlPrn17QK8HLl/WOiIiIqKWxcTGyRkMwMcfA3FxQPfuWkdDRM3RpQuQlAQUFsr/6ZISrSMiIiJqOUxsnNymTVJV6c9/1joSIrKGPn2AjRuBf/0LmDABqKrSOiIiIqKWwcTGya1YAYSGAtHRWkdCRNYyfDiwfLmM3syYoXU0RERELYNV0ZzYP/8JHDgAfPCBVFaqS34+UFTUuPstL29+bER0a0YjcOFC/X8fMQL44x8lwfHyAqZNA9q1A/z8Wi5GIiKilsTExomtWAH4+ABPP13/bYqKgF27Gne/Awc2Ly4iur3SUuDgwVvf5v77gQEDgMWLgYwM4D//k4kNERE5Lk5Fc1K5ucD69cDEiUCHDlpHQ0S24OICPPOMJDjffAN88YXWEREREdkOExsntWYNUFHBogFEjs7VFZgyRYoKvP468MknWkdERERkG0xsnFBVFbByJfDoo0CvXlpHQ0S25uoq59s89hgwdSrw+edaR0RERGR9TGyc0H//N5CZCbzwgtaREFFLcXcH/v53ICoKePZZmYpKRETkSFg8wInk58vCfYmJQFAQ0LfvrasqAaxwRuRI2rQBtm+XimlPPAGcOAG89RbgZuVPgqZUU2TFNiIiai4mNk6kqAhYuBA4dAj4wx+A3btvvw8rnBE5Fm9vGbV96SVg0SLgxx9l9KZrV+sdoynVFGNjmdgQEVHzcCqaEykpATZtArp1AwYP1joaItKKtzewejWwbh1w/LiM3u7YoXVUREREzcPExoksWwYUFMhoTX0LchKR83j8ceDYMeA3vwESEoCXXwYMBq2jIiIiahp+vXUSv/wiJZ4jIoDu3bWOhohai7vvBvbvB6ZPB5YulRHdV14BTp/WOjIiIqLGYWLjBJSSCmg+PsDo0VpHQ0StjaenJDXJyUB4OPDBB8C998r1NWtkGmtjVVfLWlmFhcDVq8C1a0BlpfVjJyIiMmHxACfw9ddASopUQ/P11ToaImqtIiOl5eQAX3whi3lOngz86U9AcDAQECAtMFAuXVyAvDwgN1daXp60oiKgrKzuY/j6SpEAf3+57N6d62kREZF1MLFxcMXFwKxZwAMPyHz6H37QOiIiau2CgoDZs+W9Y/9+4NtvJdkxjbycPi3XlQI6dpTm7y+jPKbKZleuyEhQmzaAh4fcNj9fzvMzJUNnz8qPLi4u8gPMf/wHEB8PhIVp+vCJiMhOMbFxcG+9BWRnA9u2yerjREQNpdMB990H3Hln4/YrLwf27r397aqrgYsXpTJbRoYkU7NnS4I0bRrwzDMcZSYiooZjYuPAfv5Z5spPmQIMGHD7xTiJiG7WlDVpGrr+lYuLVGT7zW9kHRtARoe+/BJ48UXgtdfk/evFF4GQkMbFQEREzofFAxzUlSvAyJEypWTRIq2jISK6vZAQKXRy8CBw4AAwfLj8ONOjBzB+vGwjIiKqDxMbB1RWJklNbi6wcyfQqZPWERER3ZrRKKPKpta5M/DOO0BqKjB1KvCPfwCDBslo0H/9F5CZKefsEBERmXAqmoOprgaefBL45z/lvJp+/bSOiIjo9kpLZaSmLv36ybk+qamS4Dz9tKy3M2eOJD08f5CIiACO2DicefOALVuAv/1NRm2IiByBpycwdKiUrX/ySVkjZ9o0oGdPYOVKqQBJRETOjSM2DmTNGuDdd2XNiZdf1joaImptTNO9GqO83DaxNJW7OxARIdPSqqqAzz4D/vxnGb155hlJdn77W62jJCIiLTCxcRB79gDPPQfExADLlkmZViKiG91quld9GlrhrKW5uEhxgWnTgJ9+AlasAFatApYvl5Gd554DRowAvL21jpSIiFoKp6I5gO+/BxISZFG7jRvlF00iImeg00ny9cUXsiZOYqIsIDp+PBAQAIwbB6xbJ2WriYjIsXHExk7l58sH9fr1stZDWBjw6aeyqndBQd37tLYpJUREzVHX1LqJE4EJE2QU57vvZA2ezZsBDw/gscdkFCciAujTB3DjJyARkUPh27qdKigAnn9ePrR79pTKQOnp0urTWqeUEBE1xe2m1pnOxcnIAI4dk5Gc77+Xv/n4yHtieLjcpl8/IDCwZeImIiLbYGJjh8rLZSXuXbuARx6RXydZ7pSIqDYXF+Cuu6TFxsp75b59QFqatMREKZMPAHfcISM5ffta2t13y30QEVHrx8TGzuTkAKNHy6+U48bJSbIsFEBEdHtGo1wOHCht9mwpE/2vfwEnT1raP/5hua23t1RZu/deWUvnvvvk315e9R+nXTvAz8/2j4eIiGpiYmMnlJLzaaZPl+kXK1fyV0Qiosa41dS1rl2lxcYC168DV64AWVmS4Bw5IuuDffWV3FanA4KC5PZ33imtWzdJaAC5DyY2REQtj4mNHbh0Sc6n2blTfmX89FOZH75rl9aRERE5Hnd3SVS6dZP33IMH5cel3Fx5P87Kknb+PHD4sGW/Dh1kn1OngKgo4P77ZXobERG1DCY2rZhSwOrVwCuvyC+IS5bIuTWuro1fZI+IiJpOpwM6dZLWt69le2mpJdm5cEFKTi9dCnzwgfw9IADo3dvSevWS6Ww3T2UzVbpsDE55IyKqiYlNK3XgADBvHpCSAkRGSoLTo4fWURER0Y18fIB77pFmEh4O5OUBR49aqlV+9BFQUSF/1+lkZCc0VFpYGNC+vSRF/v6Ap2fDjs0pb0RENTGxaUWUkkQmMRHYvRvo2BH4+GNgyhQWCCAishe+vlKGf/Bgy7aqKuDf/5Yk55dfgHPngLNn5dzJm9ce8/WVBMffXz4Hbr708eFnAhFRXZjYtAJKyfkyiYlShjQ4GPjb34DnnpMPMCIism+urjIyExYmFS1NlAJ+/RVITQWSkuQ8nvx8uczJkSptlZU178vDQxKcdetkpCgkpGa74w7nWAKA0/eoteJrUztMbDT0yy/Axo3STp+WCjsrVgDPPgu0aaN1dEREZGs6nZyH88ADkuDcTCk5jycvT5Kd3Nya17dsqb2fq6tUars54enWzXJ5q3LV9qKoqPFFdDh9j1oCX5vaYWLTgpSSZGbTJklmTp2Sks2PPAK8+irwxBPySxwREREgiY+vr7Ru3Wr+LTZWEpXSUjk/58IFSwED0/WUFODyZcsipCaBgUCXLkDnzvW34OCGn+/THJWVkqT9+qulmRK3G5O5vDx5rOXl0kpLgZISmebn4iJNp7Nc9/CQHwm9vOSyTRtgzx4Z0WrfXn4hN7W2bS39fGPz9tZm2p9S8rgqK+W5c3OTan2mx0j2yfS8Go3yA4SbG59Pa7NpYrN3714sXLgQ1dXVGD9+PKZOnWrLw7UqSkmlnCNHpBzokSPS8vPlRfzoo8ALLwBjxsiHBxEROQajsfGVK8vLm3ccb29ZRPTee2vf7vp1mdZ2+bJlfZ7Ll2XbhQvyGZWbWzv5AaSEdXCwtA4dJCG4sXl6yhdu0xdvd3f5jDMYpFiCwWC5XlBQM3kxtVtN2fH2ll+xO3SQFhBgSVLc3WV/V1eJvbpaPntN1ysrpV8rKiQJ+vVXeeylpdIaQqeTKeFeXnLp7S3XTY/35setlOzn6iqJVXW19H9lZc1W1zaDQbab/lYfNze5fx+fmklY27bSTH1l6jc/P8v1bt1kdgi/TDdddTVQWAhcu2Z5Dd98PSsLyMiQxLuiQp5To9Gy8O+N3NykeXlJkh0QYKnA2KlT/f/293eOKaeNZbPEpqqqCgsWLMBnn32GoKAgjBs3DpGRkbj77rttdUirqq6WF6DpxWh6sykrk5WqS0qkFRdLsnL5siQypstLl+SFD8gLtlcvmVfdvz8QH89khojIUd1qIdD6DBzYMscZMkT2CQurub26Wj7PCgtrtzZt5LPv/HlJTkzbG8vHp+YXtNBQy3WdTkaaTF/SfXykubvXf3+mNYYawzTKVVUlj7eoSB6L6TP95lZcDPzf/wFnzliStNJS2f/mdiNfX0lsdDq5NDVvb0kIb9zm7i73e+WK5Uuuq6vlF33AkqzdeDw/PyAz05I0ZmfL82Qa1aqPu7uco1Vfa9++9kiX6frN2zw8ao6U6XS1r2uVRClVdwJZV4JpMFheDze2/PzayUtubu3n28TLSxKPdu0s19u0sTyvpmTY1dXyPJq+Y5rWvDId78wZOWZJSd3H0unkNXBz4uPnV3NEsn17SXg9PWu+7upq7u6W59Re2SyxSU9PR0hICLp27QoA0Ov1SE5OtovE5v33gVmzGrePTifJSpcu8mb92GPAb38riUzv3o4xn5mIiByTi4tlFOZmpmTgRqZEyPTl0NSMRvmb6cuvp6fl0u0W3zguXGjZRaddXS0jG7fTlNjq6jNrH+NWiV1VlWVkyjRlr7RUvqNUV9ec4nfunNxPbu6tR4qaoyGJj2m0y1qXzeXubhkl7NRJvtPdmETUdd3bW/a15mumokKemxtHhW4eJfr1VxkhOnRIktymjADX5cbnaMAAYP9+69yvLemUstZLoKbvvvsOqampWLhwIQBg27ZtSE9PxxtvvGGLw2lm8uTJyM/P1zoMIiIiIiKr8/Pzw6effqp1GA1isxGbuvIlnQNO6rSXJ5qIiIiIyJHZbBZdcHAwsrOzzf/OyclBYGCgrQ5HREREREROzGaJTa9evZCZmYmsrCxUVlYiKSkJkZGRtjocERERERE5MZtNRXNzc8Mbb7yBKVOmoKqqCmPHjkVoaKitDkdERERERE7MZsUDiIiIiIiIWoodV6omIiIiIiISTGyIiIiIiMjuMbFpor179yI2NhbR0dH4+OOPtQ6nVZk7dy4efvhhxMXFmbcVFBRg0qRJiImJwaRJk1B4w7LVq1atQnR0NGJjY5GammrefuLECcTHxyM6OhqJiYnmEuKVlZV4+eWXER0djfHjx+PSpUvmfbZu3YqYmBjExMRg69atLfBoW9aVK1fw5JNPYvjw4dDr9fj8888BsH+txWAwYNy4cRg5ciT0ej2WLVsGgP1rTVVVVRg1ahSee+45AOxba4qMjER8fDwSEhIwZswYAOxfaykqKsL06dMxbNgwDB8+HMeOHWPfWsn58+eRkJBgbvfffz/Wrl3L/rWStWvXQq/XIy4uDjNnzoTBYHDsvlXUaEajUUVFRamLFy8qg8Gg4uPj1blz57QOq9U4dOiQOnHihNLr9eZt7777rlq1apVSSqlVq1apxYsXK6WUOnfunIqPj1cGg0FdvHhRRUVFKaPRqJRSauzYsero0aOqurpaTZ48WaWkpCillPryyy/V66+/rpRSaufOneqll15SSimVn5+vIiMjVX5+viooKFCRkZGqoKCgxR53S8jJyVEnTpxQSilVXFysYmJi1Llz59i/VlJdXa1KSkqUUkpVVlaqcePGqWPHjrF/rWjNmjVq5syZaurUqUopvjdY05AhQ1Rubm6Nbexf63j11VfVxo0blVJKGQwGVVhYyL61AaPRqAYNGqQuXbrE/rWC7OxsNWTIEFVeXq6UUmr69Olq8+bNDt23HLFpgvT0dISEhKBr167w8PCAXq9HcnKy1mG1Gv3790f79u1rbEtOTsaoUaMAAKNGjcIPP/xg3q7X6+Hh4YGuXbsiJCQE6enpuHr1KkpKStCvXz/odDqMGjXK3Me7d+/G6NGjAQCxsbE4cOAAlFJIS0tDeHg4OnTogPbt2yM8PLzGrw2OIDAwED179gQA+Pr6okePHsjJyWH/WolOp4OPjw8AwGg0wmg0QqfTsX+tJDs7GykpKRg3bpx5G/vWtti/zVdSUoLDhw+bX7ceHh5o164d+9YGDhw4gK5du6JLly7sXyupqqpCRUUFjEYjKioqEBgY6NB9y8SmCXJychAcHGz+d1BQEHJycjSMqPXLzc01L9AaGBiIvLw8APX35c3bg4ODzX2ck5ODzp07A5Cy4m3btkV+fr7TPS+XLl3CqVOn0KdPH/avFVVVVSEhIQGDBg3CoEGD2L9WtGjRIrzyyitwcbF89LBvrWvy5MkYM2YMNmzYAID9aw1ZWVnw9/fH3LlzMWrUKMyfPx9lZWXsWxtISkoyT2Nn/zZfUFAQnn32WQwZMgQRERHw9fVFRESEQ/ctE5smUHVUyNbpdBpEYv/q68tb9XFT9nE0paWlmD59OubNmwdfX996b8f+bTxXV1ds374dP/74I9LT03H27Nl6b8v+bbg9e/bA398fv/vd7xp0e/Zt461fvx5bt27F6tWr8dVXX+Hw4cP13pb923BGoxEnT57E448/jm3btsHLy+uW59ayb5umsrISu3fvxrBhw255O/ZvwxUWFiI5ORnJyclITU1FeXk5tm/fXu/tHaFvmdg0QXBwMLKzs83/zsnJMWe+VLeOHTvi6tWrAICrV6/C398fQP19efP27Oxscx8HBwfjypUrAOQDp7i4GB06dHCa5+X69euYPn064uPjERMTA4D9awvt2rXDQw89hNTUVPavFRw9ehS7d+9GZGQkZs6ciYMHD2L27NnsWysKCgoCIO8H0dHRSE9PZ/9aQXBwMIKDg9GnTx8AwLBhw3Dy5En2rZXt3bsXPXv2RKdOnQDwc80a9u/fjzvvvBP+/v5wd3dHTEwMjh075tB9y8SmCXr16oXMzExkZWWhsrISSUlJiIyM1DqsVi0yMhLbtm0DAGzbtg1RUVHm7UlJSaisrERWVhYyMzPRu3dvBAYGwsfHBz///DOUUrX2MVXX2LVrFwYOHAidToeIiAikpaWhsLAQhYWFSEtLQ0REhDYP2EaUUpg/fz569OiBSZMmmbezf60jLy8PRUVFAICKigrs378fPXr0YP9awaxZs7B3717s3r0b77//PgYOHIj33nuPfWslZWVlKCkpMV/ft28fQkND2b9WEBAQgODgYJw/fx6AnAdy1113sW+tLCkpCXq93vxv9m/z3XHHHTh+/DjKy8uhlHKO166VixE4jZSUFBUTE6OioqLUypUrtQ6nVZkxY4YKDw9X9913nxo8eLDauHGjysvLU0899ZSKjo5WTz31lMrPzzfffuXKlSoqKkrFxMSYq2wopVR6errS6/UqKipKvfXWW6q6uloppVRFRYV68cUX1dChQ9XYsWPVxYsXzft88803aujQoWro0KFq06ZNLfegW8jhw4dVWFiYiouLUyNHjlQjR45UKSkp7F8rOXXqlEpISFBxcXFKr9er5cuXK6UU+9fKDh48aK6Kxr61josXL6r4+HgVHx+vRowYYf5cYv9ax8mTJ9Xo0aNVXFycev7551VBQQH71orKysrUgAEDVFFRkXkb+9c6li5dqmJjY5Ver1ezZ89WBoPBoftWp1Qdk+CIiIiIiIjsCKeiERERERGR3WNiQ0REREREdo+JDRERERER2T0mNkREREREZPeY2BARERERkd1jYkNERFbx0UcfQa/XIz4+HgkJCTh+/Hi9t50zZw6+++67W97fnDlzEBkZiYSEBIwePRrHjh2r83ZLly7F/v37mxU7ERHZPzetAyAiIvt37NgxpKSkYOvWrfDw8EBeXh6uX7/e7Pt99dVXMWzYMKSlpeGNN97At99+W+PvVVVVeOmll5p9HCIisn9MbIiIqNmuXbsGPz8/eHh4AAD8/f0BAB9++CH27NkDg8GAfv36YcGCBdDpdDX2PXHiBN555x2UlZXBz88Pb7/9NgIDA2vcpn///rh48SIAWel6zJgx2LdvHyZOnIjU1FQ89thjGDZsGNLT07Fo0SKUlZXBw8MDa9euhZeXF9577z0cOnQIlZWVeOKJJzBhwoQW6BUiImpJnIpGRETNFh4ejitXriA2NhZvvvkmDh06BACYOHEiNm/ejJ07d6KiogJ79uypsd/169eRmJiIZcuWYcuWLRg7diyWLFlS6/53796NsLAw8789PT2xfv166PV687bKykrMmDED8+bNw44dO7B27Vq0adMGmzZtQtu2bbF582Zs3rwZGzduRFZWlo16goiItMIRGyIiajYfHx9s2bIFR44cwU8//YQZM2Zg1qxZ8PHxwSeffIKKigoUFBQgNDQUkZGR5v0yMjJw9uxZTJo0CQBQXV2NgIAA898XL16Mjz76CP7+/li4cKF5+4gRI2rFkJGRgYCAAPTu3RsA4OvrCwDYt28fzpw5g127dgEAiouLceHCBXTt2tX6HUFERJphYkNERFbh6uqKhx56CA899BDCwsKwYcMGnDlzBps3b0bnzp2xfPlyGAyGGvsopRAaGooNGzbUeZ+mc2xu5uXlVWubUqrWNDfT9tdeew2DBw9u4iMjIiJ7wKloRETUbOfPn0dmZqb536dOnUL37t0BAH5+figtLTWPmNyoe/fuyMvLM1c8u379Os6dO9ekGHr06IGrV68iPT0dAFBSUgKj0YiIiAisX7/eXMwgIyMDZWVlTToGERG1XhyxISKiZisrK0NiYiKKiorg6uqKkJAQLFiwAG3btkV8fDy6dOmCXr161drPw8MDy5YtQ2JiIoqLi1FVVYWnn34aoaGhjY7Bw8MDS5YsQWJiIioqKtCmTRt89tlnGD9+PC5fvowxY8ZAKQU/Pz+sXLnSGg+biIhaEZ1SSmkdBBERERERUXNwKhoREREREdk9JjZERERERGT3mNgQEREREZHdY2JDRERERER2j4kNERERERHZPSY2RERERERk95jYEBERERGR3ft/Q14/yQfF3w8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette=\"deep\")\n",
    "f,ax = plt.subplots(figsize=(14,10))\n",
    "\n",
    "sns.distplot(train['SalePrice'], color=\"Blue\" )\n",
    "ax.set(ylabel = \"Frequency\")\n",
    "ax.set(xlabel = \"SalePrice\")\n",
    "ax.set(title = \"SalePrice Distribution\" )\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(constrained_layout=True, figsize=(12,8))\n",
    "grid = gridspec.GridSpec(ncols=3, nrows=4, figure=fig)\n",
    "sns.set_color_codes(palette=\"deep\")\n",
    "\n",
    "# QQplot\n",
    "\n",
    "ax2 = fig.add_subplot(grid[2:,:2])\n",
    "stats.probplot(train['SalePrice'],plot=ax2)\n",
    "ax2.set_title(\"QQplot of SalePrice\")\n",
    "\n",
    " # Boxplot\n",
    "    \n",
    "ax3 = fig.add_subplot(grid[2:,2])\n",
    "sns.boxplot(train['SalePrice'],ax=ax3,orient=\"v\",color=\"Blue\")\n",
    "ax3.set_title(\"Boxplot of SalePrice\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  **Skewness and the kurtosis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Skewness**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.pinimg.com/originals/c1/01/b0/c101b0da6ea1a0dab31f80d9963b0368.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ". Skewness is the degree of distortion from the symmetrical bell curve or the normal distribution.\n",
    "\n",
    ". symmetrical distribution must have a skewness of 0.\n",
    "\n",
    ". Skewness can be positive or negative.\n",
    "\n",
    ". Positive Skewness when The mean and median will be greater than the mode.\n",
    "\n",
    ". Negative Skewness The mean and median will be less than the mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Kurtosis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.pinimg.com/564x/e1/f0/b2/e1f0b20eb0773915fc6e9b91909adfa3.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ". Kurtosis is the measure of outliers present in the distribution.\n",
    "\n",
    ". There are three types of Kurtosis: Mesokurtic, Leptokurtic, and Platykurtic.\n",
    "\n",
    ". Mesokurtic distribution indicates an excess kurtosis of zero. This means that the data has a normal distribution.\n",
    "\n",
    ". Leptokurtic shows a positive excess kurtosis, so Leptokurtic distribution indicates heavy tails that's means the existance of large outliers .\n",
    "\n",
    ". A Platykurtic distribution shows a negative excess kurtosis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Calcul the skewness and the kurtosis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness : 1.882876\n",
      "Kurtosis : 6.536282\n"
     ]
    }
   ],
   "source": [
    "print(\"Skewness : %f\" % train['SalePrice'].skew() )\n",
    "print(\"Kurtosis : %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The skewness value is 1, that means that the data are moderately skewed, and the high Kurtosis indicates that the data\n",
    "\n",
    "  has heavy tails or outliers, so we need to investigate!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We notice in the graph above that the variable target is right skewed, the best way to fix it is to perform a log transformation of the same data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Log Transformation of the Dependant Variable(SalePrice)** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In this case, We use the numpy fuction log1p which  applies log(1+x) to all elements of the column.\n",
    "\n",
    "train['SalePrice'] = np.log1p(train['SalePrice'])\n",
    "sns.set_color_codes(palette=\"deep\")\n",
    "\n",
    "## Visualize of SalePrice after the normalization\n",
    "fig,(ax1,ax2) = plt.subplots(2,1,constrained_layout=True,figsize=(12,9))\n",
    "\n",
    " # Histrogram\n",
    "sns.distplot(train['SalePrice'],ax=ax1, color= \"red\")\n",
    "ax1.set_title(\"Histrogram of SalePrice\")\n",
    " # QQplot\n",
    "stats.probplot(train['SalePrice'],plot=ax2)\n",
    "ax2.set_title(\"QQplot of SalePrice\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- So now the skewed seems corrected, and the data is normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Discover outliers with visualization tools**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Before performing statistical analyses,we should identify potential outliers. According to this article [Tha author Dean De Cock ](www.amstat.org/publications/jse/v19n3/decock.pdf), there are outliers for the GR LIVE AREA, so we will visualize this feature graphically with the BoxPlot and ScatterPlot.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A.**Box Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "name": "GrLivArea",
         "type": "box",
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       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace0 = go.Box(\n",
    "    name = \"GrLivArea\",\n",
    "    y = train[\"GrLivArea\"]\n",
    ")\n",
    "\n",
    "trace1 = go.Box(\n",
    "    name = \"MasVnrArea\",\n",
    "    y = train[\"MasVnrArea\"]\n",
    ")\n",
    "\n",
    "trace2 = go.Box(\n",
    "    name = \"KitchenAbvGr\",\n",
    "    y = train[\"KitchenAbvGr\"]\n",
    ")\n",
    "\n",
    "trace3 = go.Box(\n",
    "    name = \"BedroomAbvGr\",\n",
    "    y = train[\"BedroomAbvGr\"] \n",
    ")\n",
    "\n",
    "trace4 = go.Box(\n",
    "    name = \"LotFrontage\",\n",
    "    y = train[\"LotFrontage\"]\n",
    ")\n",
    "\n",
    "trace5 = go.Box(\n",
    "    name = \"GarageCars\",\n",
    "    y = train[\"GarageCars\"]\n",
    ")\n",
    "data = [trace0, trace1, trace2, trace3, trace4,trace5  ]\n",
    "plotly.offline.iplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **B. Scatter Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "hovertemplate": "GrLivArea=%{x}<br>SalePrice=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "showlegend": false,
         "type": "scattergl",
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   ],
   "source": [
    "fig = px.scatter(train, x='GrLivArea' , y='SalePrice')\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **As we see, there are some points which are far from the population in the top right corner.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
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       "      <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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>524</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
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       "      <td>12.126764</td>\n",
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       "      <th>691</th>\n",
       "      <td>692</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>104.0</td>\n",
       "      <td>21535</td>\n",
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       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>13.534474</td>\n",
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       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>1183</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>15623</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>Ex</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>13.521141</td>\n",
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       "      <th>1298</th>\n",
       "      <td>1299</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
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       "      <td>480</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
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       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "523    524          60       RL        130.0    40094   Pave   NaN      IR1   \n",
       "691    692          60       RL        104.0    21535   Pave   NaN      IR1   \n",
       "1182  1183          60       RL        160.0    15623   Pave   NaN      IR1   \n",
       "1298  1299          60       RL        313.0    63887   Pave   NaN      IR3   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "523          Bnk    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "691          Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1182         Lvl    AllPub  ...      555     Ex  MnPrv         NaN       0   \n",
       "1298         Bnk    AllPub  ...      480     Gd    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "523      10   2007       New        Partial  12.126764  \n",
       "691       1   2007        WD         Normal  13.534474  \n",
       "1182      7   2007        WD        Abnorml  13.521141  \n",
       "1298      1   2008       New        Partial  11.982935  \n",
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     "execution_count": 96,
     "metadata": {},
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   "source": [
    "train.iloc[np.where(train.GrLivArea > 4000)]"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Clean Outliers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **The presence of outliers in the dataset may affect our analysis and they can cause problems in statistical procedures. so we need to remove them.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<200000)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### **After Removing Outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig = px.scatter(train, x='GrLivArea' , y='SalePrice')\n",
    "fig.show()"
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