Feature_Importance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "72a0cffd-9f50-4ec3-9343-704b9fb690ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa85e33e-1509-40fe-92c2-571d4cdcd088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand             0\n",
       "Price           172\n",
       "Body              0\n",
       "Mileage           0\n",
       "EngineV         150\n",
       "Engine_Type       0\n",
       "Registration      0\n",
       "Year              0\n",
       "Model             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Train.csv\")\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d37998-3abf-432e-acf2-21221905d779",
   "metadata": {},
   "source": [
    "# Price and Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "807e1fd0-7ffe-42d8-bb5c-30034d2c7924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df.groupby('Brand').Price.mean().sort_values().reset_index()\n",
    "plt.figure(figsize = (8,5))\n",
    "sns.barplot(x= tmp.Brand,y = tmp.Price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a69848-61df-4c8c-b882-1034349181f5",
   "metadata": {},
   "source": [
    "observation - \n",
    "\n",
    "    1- There is clear relation between price and car model. \n",
    "    \n",
    "    2- M-Benz is higest in price while Renault is lower in price.\n",
    "    \n",
    "    3- Toyota and BMW is very near in term of mean value of car. \n",
    "    \n",
    "Next step -\n",
    "\n",
    "    1- We may think of combining Toyota and BMW together. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932776b2-c35c-425a-b3fc-914aef25bd7b",
   "metadata": {},
   "source": [
    "# Price and Body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "783c1b5a-d9a5-478a-bcc3-5df6962e7fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df.groupby(df.Body).Price.mean().sort_values().reset_index()\n",
    "sns.barplot(x = tmp.Body,y=tmp.Price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b4946e-adbc-4bea-a2d0-3ad483b7b238",
   "metadata": {},
   "source": [
    " Observation - \n",
    "\n",
    "1- Vagon and Hatch is very same in mean term we can club these two catagoery together. \n",
    "\n",
    "2- crossover is very costly as compare to other Body type.\n",
    "\n",
    "3- Body feature is important for price prediction.\n",
    "\n",
    "\n",
    "#Next step:\n",
    "\n",
    "1- we can make two model one without combining and another combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39f3db1e-1318-4faf-953d-a3b5faab5bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
       "       'Registration', 'Year', 'Model'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d4da1e-a7b2-4da2-96eb-9f0014fa85e8",
   "metadata": {},
   "source": [
    "# Price and Mileage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1987765-f970-4257-a346-0a4082a3cac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,7))\n",
    "sns.regplot(x = df.Mileage,y = df.Price).set(xlabel='Mileage', ylabel='Car Price', title='CMileage vs. Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31467c23-b07c-41ca-8b04-bb28fce9b1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZnKCQDaeMFbIhRwcnWuJ6IiIiIjK7pahJr3P3ITPbT1JycsrMNrv7ibSUpVY3cRTY1nDaVuB4Or61yXjjOUfNLAP0AGfT8d3TztnfZF53A3cD7Ny5c0YQfzn44Jce50NfeYrxckRHLqSnkGWyEtGeO/cUT1Yitva2X9D1t/W20z9avGTXExEREZHZLUV3lw1mtib9ugC8EjgE7ANq3VbeBHwu/XofcEvaseVq4Brga2lJzKiZ3ZDWm79x2jm1a70BuD+tW/8i8Coz6027x7wqHVtRPvilx/nA/U8yWYnIBEnwfHRwktNjJSbKVdydiXKVSuTs2bX9gu5jz67tVCK/ZNcTERERkdktRSZ9M/AxMwtJXhR8xt3/xsweAD5jZm8GngV+CsDdHzWzzwDfBarAW9LOLgC/BHwUKJB0dbk3Hf8w8Akze5Ikg35Leq2zZvYe4Ovp7e5097OL+miXwYe+8hSBQSZIXnMFBhBTrsb0dbVxdHCCrRfZjWX3jj7uJKlNvxTXExEREZHZWZJwlpqdO3f6wYMHl3saC/Lc3/w8mQACO/fGSOwx1Ri+/zs/vowzExEREZHZmNlD7r6z2THtOLoCdORC4mmvtWJPxkVERETk8qMgfQX4hZuuJnaoxnGaQY+JPRkXERERkcvPknZ3kcVx+yuvBZjS3eUXbrq6Pi4iIiIilxfVpE9zOdaki4iIiMjlRzXpIiIiIiKXEQXpIiIiIiItRkG6iIiIiEiLUZAuIiIiItJiFKSLiIiIiLQYBekiIiIiIi1GQbqIiIiISItRkC4iIiIi0mIUpIuIiIiItBgF6SIiIiIiLUZBuoiIiIhIi1GQLiIiIiLSYhSki4iIiIi0GAXpIiIiIiItRkG6iIiIiEiLUZAuIiIiItJiFKSLiIiIiLQYBekiIiIiIi1GQbqIiIiISItRkC4iIiIi0mIUpIuIiIiItBgF6SIiIiIiLUZBuoiIiIhIi1GQLiIiIiLSYhSki4iIiIi0GAXpIiIiIiItRkG6iIiIiEiLUZAuIiIiItJiFKSLiIiIiLQYBekiIiIiIi1GQbqIiIiISItRkC4iIiIi0mIUpIuIiIiItBgF6SIiIiIiLUZBuoiIiIhIi8ks9h2Y2Tbg48AmIAbudvcPmNm7gV8EBtKb/qa7fz495x3Am4EIuN3dv5iOvwz4KFAAPg/8iru7meXT+3gZcAb4aXd/Oj3nTcA70/v4bXf/2KI+4Au0/1A/ew8c5vFTI1QiJ5cJuKaviz27trN7R99yT09EREREltBSZNKrwK+5+/OBG4C3mNkL0mPvd/eXpB+1AP0FwC3AdcBrgD8yszC9/R8DtwHXpB+vScffDAy6+/OA9wN3pddaC7wL+GHg5cC7zKx3UR/tBdh/qJ879j3KU6fHGClWmaxEDE9UePrMGHfse5T9h/qXe4oiIiIisoQWPUh39xPu/nD69SjwGLBljlNeB3zK3Uvu/hTwJPByM9sMdLv7A+7uJJnz1zecU8uQ3wO8wswMeDVwn7ufdfdB4D7OBfYtY++Bw2RDY7RYJcDIBAFBYIxMVsmGxt4Dh5d7iiIiIiKyhJa0Jt3MrgJeCnw1HfplM/uWmX2kIcO9BTjScNrRdGxL+vX08SnnuHsVGAbWzXGt6fO6zcwOmtnBgYGB6YcX3ZHBCQrZkHIUY1abE5SjmEI25OjgxJLPSURERESWz5IF6WbWCfwl8KvuPkJSuvJc4CXACeD3ajdtcrrPMX6h55wbcL/b3Xe6+84NGzbM9TAWxbbediYrEbkwwL02J8iFAZOViK297Us+JxERERFZPksSpJtZliRA/zN3/ysAdz/l7pG7x8CfkNSMQ5Lt3tZw+lbgeDq+tcn4lHPMLAP0AGfnuFZL2bNrO5XI6WrLEONU45g4droLGSqRs2fX9uWeooiIiIgsoUUP0tPa8A8Dj7n77zeMb2642U8C30m/3gfcYmZ5M7uaZIHo19z9BDBqZjek13wj8LmGc96Ufv0G4P60bv2LwKvMrDctp3lVOtZSdu/o486br+Pq9Z30tGUoZEN62rNcta6TO2++Tt1dRERERFaZRW/BCPwI8PPAt83skXTsN4FbzewlJOUnTwN7ANz9UTP7DPBdks4wb3H3KD3vlzjXgvHe9AOSFwGfMLMnSTLot6TXOmtm7wG+nt7uTnc/uyiP8iLt3tGnYFxEREREADD3GSXaq9rOnTv94MGDyz0NEREREVnhzOwhd9/Z7Jh2HBURERERaTEK0kVEREREWoyCdBERERGRFqMgXURERESkxShIFxERERFpMQrSRURERERajIJ0EREREZEWoyBdRERERKTFKEgXEREREWkxCtJFRERERFqMgnQRERERkRajIF1EREREpMUoSBcRERERaTEK0kVEREREWoyCdBERERGRFqMgXURERESkxShIFxERERFpMQrSRURERERajIJ0EREREZEWoyBdRERERKTFZJZ7AnLp7D/Uz94Dh3mif5RyNSYbGtdu7GbPru3s3tG33NMTERERkXlSJn2F2H+onzv2PcrTZ8YYnqgwWYkYKVZ56vQYd+x7lP2H+pd7iiIiIiIyTwrSV4i9Bw6TDY2RySpBYGSCgABjtFglGxp7Dxxe7imKiIiIyDwpSF8hjgxOUMiGlKMYs2TMDMpRTCEbcnRwYnknKCIiIiLzpiB9hdjW285kJSIXBrgnY+6QCwMmKxFbe9uXd4IiIiIiMm8K0leIPbu2U4mc7kKGOHaqcUyM09WWoRI5e3ZtX+4pioiIiMg8qbvLCrF7Rx93ktSmV6Kku0suNK5e36nuLiIiIiKXGQXpK8juHX0KxkVERERWAJW7iIiIiIi0GAXpIiIiIiItRkG6iIiIiEiLUZAuIiIiItJiFKSLiIiIiLQYBekiIiIiIi1GQbqIiIiISItRkC4iIiIi0mIUpIuIiIiItBgF6SIiIiIiLWbRg3Qz22Zmf2dmj5nZo2b2K+n4WjO7z8yeSD/3NpzzDjN70sy+Z2avbhh/mZl9Oz32QTOzdDxvZp9Ox79qZlc1nPOm9D6eMLM3LfbjXWz7D/Vz690PctNd93Pr3Q+y/1D/ck9JRERERC6xpcikV4Ffc/fnAzcAbzGzFwBvB77s7tcAX06/Jz12C3Ad8Brgj8wsTK/1x8BtwDXpx2vS8TcDg+7+POD9wF3ptdYC7wJ+GHg58K7GFwOXm/2H+rlj36P0jxZZU8jSP1rkjn2PKlAXERERWWEWPUh39xPu/nD69SjwGLAFeB3wsfRmHwNen379OuBT7l5y96eAJ4GXm9lmoNvdH3B3Bz4+7Zzate4BXpFm2V8N3OfuZ919ELiPc4H9ZWfvgcNkQ6M9l8Es+ZwNjb0HDi/31ERERETkElrSmvS0DOWlwFeBje5+ApJAHuhLb7YFONJw2tF0bEv69fTxKee4exUYBtbNca3p87rNzA6a2cGBgYGLeISL68jgBIVsOGWskA05OjixTDMSERERkcWwZEG6mXUCfwn8qruPzHXTJmM+x/iFnnNuwP1ud9/p7js3bNgwx9SW17bediYr0ZSxyUrE1t72ZZqRiIiIiCyGJQnSzSxLEqD/mbv/VTp8Ki1hIf1cK6w+CmxrOH0rcDwd39pkfMo5ZpYBeoCzc1yr5cxnQeieXdupRM5EuYp78rkSOXt2bV+GGYuIiIjIYlmK7i4GfBh4zN1/v+HQPqDWbeVNwOcaxm9JO7ZcTbJA9GtpScyomd2QXvON086pXesNwP1p3foXgVeZWW+6YPRV6VhLme+C0N07+rjz5uvo62pjeLJCX1cbd958Hbt39M1yZRERERG5HGWW4D5+BPh54Ntm9kg69pvA+4DPmNmbgWeBnwJw90fN7DPAd0k6w7zF3Ws1Hr8EfBQoAPemH5C8CPiEmT1JkkG/Jb3WWTN7D/D19HZ3uvvZRXqcF6xxQShAey7DRLnK3gOHZwTgu3f0KSgXERERWeEWPUh396/QvDYc4BWznPNe4L1Nxg8CL2wyXiQN8psc+wjwkfnOdzkcGZxgTSE7ZUwLQkVERERWL+042gK0IFREREREGilIbwFaECoiIiIijRSktwAtCBURERGRRkuxcFTmQQtCRURERKRGmXQRERERkRajIF1EREREpMUoSBcRERERaTEK0kVEREREWoyCdBERERGRFqMgXURERESkxShIFxERERFpMQrSRURERERajIJ0EREREZEWoyBdRERERKTFKEgXEREREWkxCtJFRERERFqMgnQRERERkRajIF1EREREpMUoSBcRERERaTEK0kVEREREWoyCdBERERGRFpNZ7gkIfPBLj/OhrzzFeDmiIxfyCzddze2vvHa5pyUiIiIiy0RB+jL74Jce5wP3P0lgkAlgshLxgfufBFCgLiIiIrJKqdxlmX3oK0+lAXpAYEH6ORkXERERkdVJQfoyGy9HBDZ1LLBkXERERERWJwXpy6wjFxL71LHYk3ERERERWZ0UpC+zX7jpamKHahwTe5x+TsZFREREZHXSwtFlVlscqu4uIiIiIlJj7n7+W60iO3fu9IMHDy73NERERERkhTOzh9x9Z7NjyqS3oP2H+tl74DBHBifY1tvOnl3b2b2jb7mnJSIiIiJLRDXpLWb/oX7u2Pco/aNF1hSy9I8WuWPfo+w/1L/cUxMRERGRJaIgvcXsPXCYbGi05zKYJZ+zobH3wOHlnpqIiIiILBEF6S3myOAEhezU9ouFbMjRwYllmpGIiIiILDUF6S1mW287k5WpGxlNViK29rYv04xEREREZKkpSG8xe3ZtpxI5E+Uq7snnSuTs2bV9uacmIiIiIktEQXqL2b2jjztvvo6+rjaGJyv0dbVx583XqbuLiIiIyCqiFowtaPeOPgXlIiIiIquYMukiIiIiIi1GQbqIiIiISItZ9CDdzD5iZv1m9p2GsXeb2TEzeyT9+PGGY+8wsyfN7Htm9uqG8ZeZ2bfTYx80M0vH82b26XT8q2Z2VcM5bzKzJ9KPNy32YxURERERuRSWIpP+UeA1Tcbf7+4vST8+D2BmLwBuAa5Lz/kjM6s1Df9j4DbgmvSjds03A4Pu/jzg/cBd6bXWAu8Cfhh4OfAuM+u99A9PREREROTSWvQg3d0PAGfnefPXAZ9y95K7PwU8CbzczDYD3e7+gLs78HHg9Q3nfCz9+h7gFWmW/dXAfe5+1t0Hgfto/mJBRERERKSlLGdN+i+b2bfScphahnsLcKThNkfTsS3p19PHp5zj7lVgGFg3x7VmMLPbzOygmR0cGBi4uEclIiIiInKRlitI/2PgucBLgBPA76Xj1uS2Psf4hZ4zddD9bnff6e47N2zYMMe0RUREREQW37L0SXf3U7WvzexPgL9Jvz0KbGu46VbgeDq+tcl44zlHzSwD9JCU1xwFdk87Z/+legxLbf+hfvYeOMyRwQm29bazZ9d29VIXERERWaGWJZOe1pjX/CRQ6/yyD7gl7dhyNckC0a+5+wlg1MxuSOvN3wh8ruGcWueWNwD3p3XrXwReZWa9aTnNq9Kxy87+Q/3cse9R+keLrClk6R8tcse+R9l/qH+5pyYiIiIii2DRM+lm9kmSjPZ6MztK0nFlt5m9hKT85GlgD4C7P2pmnwG+C1SBt7h7lF7ql0g6xRSAe9MPgA8DnzCzJ0ky6Lek1zprZu8Bvp7e7k53n+8C1pay98BhsqHRnkuervZcholylb0HDiubLiIiIrICWZJ0lpqdO3f6wYMHl3saU9x01/2sKWRJW8MD4O4MT1b4h9/4sWWcmYiIiIhcKDN7yN13Nju2LDXpsjDbetvpHy3WM+kAk5WIrb3tyzir81MdvYiIiMiFWc4WjDJPe3ZtpxI5E+Uq7snnSuTs2bV9uac2K9XRi4iIiFw4BemXgd07+rjz5uvo62pjeLJCX1cbd958XUtnpRvr6M2Sz9nQ2Hvg8HJPTURERKTlqdylRZyvNGT3jr6WDsph6mMYGC2xqTs/5XghG3J0cGKZZiciIiJy+ZhXJt3MCmb2A4s9mdVqJZSGTH8MZnBsqMhosVK/zeVQRy8iIiLSCs4bpJvZvwAeAb6Qfv8SM9u3yPNaVe76wiH6R4o8e3aCp06PE8W+5KUh+w/1c+vdD3LTXfdz690PLvgFwvTylo1dbQCcHC5eNnX0IiIiIq1iPpn0dwMvB4YA3P0R4KrFmtBqs/9QP4/3jxG7EwZGNXaODxWpRvGSlYZcikz+kcEJCtmw/n13IcuWNW04XDZ19CIiIiKtYj416VV3H27s0S2XTi0D7TEYhhlUPebZs5PkMgG33v3gorcuvBSbJTVrE5kJA66/spdP3nbDosxbREREZKWaTyb9O2b2M0BoZteY2X8H/nGR57VqHBmcYGNXnhgnjp1qFFOJHAc2deeXpD59ehYcFr7I83JsEykiIiLSquYTpP9H4DqgBPw5MAz86iLOaVXZ1ttOJgy4oqdAJkzKXQzIh0Z3IbckrQu39bYzWYmmjC10kefl2CZSREREpFWdt9zF3SeA/5R+yCW2Z9d23nbPNxkrVYniJIMeGGzqKdRvc6GtC+e74+eeXdu5Y9+jTJSrFLIhk5XogrLgl0ObSBEREZHLwXy6u9xnZmsavu81sy8u6qxWGQOiKKYaOQCxw0S5Wj9+Ia0LF7IYVFlwERERkdYyn4Wj6919qPaNuw+amaK3S2TvgcOEgYEZudCI3anEzsBYmfZcSCYMLiirvdDFoMqCi4iIiLSO+dSkx2Z2Ze0bM3sO4Is3pdXlyOAEo8UqAUYQGJkwIBcmnXROjpQuOKt9KRaDioiIiMjymE8m/T8BXzGzv0+/3wXctnhTWl229bZzcrhIJjjX4tLMaM8a6zrzfPK2G+obDZ2vtnz6dae3RNSOnyIiIiKXh/ksHP2CmV0P3EBSPv1Wdz+96DNbJfbs2s7Dzw4SuRMauCcf+VzA8GSFnb99H6PFKr3tWSbLEQ8MnuGBw2cw4PUv2cz7b7l+1uteisWgIiIiIrL0Zi13MbMd6efrgSuB48Ax4Mp0TC6B3Tv6eMvu5xKYUYliQoOOfMBIMaIjHzJRqhK70z9SYrh4bjGpA5995ARv/dTDs15Xi0FFRERELk/m3ry83MzudvfbzOzvmhx2d/+xxZ3a8ti5c6cfPHhwye+31i7x6OAEw5MVOvIh6zvbOHRyhDAwipW46XlhYHz/d358iWcrIiIiIhfLzB5y951Nj80WpKcnBsCN7v5/F2tyrWa5gvRGN911P2sKWcyMwwNjVGOnVG0epAPcuH3dvOrURURERKR1zBWkz9ndxd1j4HcXZVYyq8YdQDd05ZnjdRTAnD3QRUREROTyM58WjH9rZv/KzOz8N5VLYc+u7VQiZ6JcpTOfYV1nlmCWn/6aQpYodvpHiuz504e49e4HFayLiIiIXObmE6T/v8BfACUzGzGzUTMbWeR5rWrTF31eta6Tj7zph/jJl2xONj5KrSlkWdOe5fhQkdg9WWCqrLqIiIjIZW/OmvTVaDlq0muLRufbB/3Wux+s90Cv1azjkAmN7Rs6mShX6etq45O33bCEj0JEREREFuKCatLN7Boz+5yZfcfM/tzMtizeFFev/Yf6uWPfo/SPFgkNvnFkkDd//CCv/YMDs2bDG8thJisR5WpMKYqpRjEjkxXtLCoiIiJymZurBeM/AB8HDgA3k3R5+ZdLOLdlsdSZ9FpWvBo5x4cnCTAcJzCjr3v23ub7D/Vz1xcO8djJUQzIBBBYQIyzriNHb3uONe05jgxO0JkLMTNGS1W29bZz4/a1PHD4LI+fGqESOblMwDV9XeoQIyIiIrKELrS7S5e7/4m7f8/d/xtw1aLMbpU7MjhBIRtyeqxEgBEEyUfkTjY09h443PS83Tv6WNOeY3NPnmwYEAQBlj6bZ8bLDIyV6tn5JwfGeaJ/jNDg6TNjfOD+J3nsxDAjxSQTPzxR4ekzY6plFxEREWkRcwXpbWb2UjO7Pt1htDDte7kEau0Wy1FMrX+OO+TC4LxlK0cGJ1jXkeeKNW1kAiOKnWxg4NBTyNKey3B6rExoRhgYp8fKjExWCQxGilUCjEwQEATGyGR1zhcFIiIiIrJ0MnMcOwH8fsP3Jxu+d2BF7ji61Pbs2s4d+x4lDIw4dowkSN/QlWeyErG1tx1ovrh0W287/aNFutqydLVlAZgoVzk6OEkhGwJQjmJCM7Dka4DAoOLUXxRYeky17CIiIiKtYdYg3d1/dCknslrt3tHHncD77n2MJwbGyJqxqSdPGBiVyNmza3t9cWk2NNYUsvU2i2+4fgv3PHyMiXKVQjZkshJRiZyr1yXZ+fZchlwYUI2SdQe5MHnjpBzFBJa8GDA7l7lvfFEgIiIiIstnPn3SZQn0duRZU8iSywSMlyP6us4tGt174DDZ0GjPZTBLPmdD44HDZ6f0U6+d8/bXPr/e/WV9Z45qHFOqxhQrEZUoJoqd7rYMMU41joljp7uQqb8oEBEREZHlNVe5iyyBxiz55p5CPRve2GnlyOAEawrZKefVSlN27+hr2pHlTmDvgcM8cWoEM8gFhgNBYHRmAjZ1t3F6rEQ57e5y1bpOdXcRERERaRFzBulmZsBWdz+yRPNZdRqz5ADtuQwT5Sp7DxyuB8y12vPabYDzlqbUgvfGjY9qJspVejvyfOGt/3SRHpWIiIiIXIw5y108aaL+10szldWp1oKx0fQFnI2bF7knn+dbmjKf64uIiIhIa5lPTfqDZvZDiz6TVarWgrHR9Cz57h19TWvP51OaMp/ri4iIiEhrmU9N+o8Ce8zsGWAc0i6B7i9a1JmtEnt2bef2Tz7MSGlqIP2iLd1Tvp+t9nw+179j36MzOsBogaiIiIhI65pPkP7aRZ/FKvato0MzAnSAzz5ygqvXP87tr7wWaN4nfT5Be63F494Dhzk6OMHWBZwrIiIiIsvDkrLzedzQrA9oq33v7s8u1qSW086dO/3gwYNLdn8vevcXGSlWmx7rbsvwrXe/ekoHmMZs+HxLXkRERESk9ZjZQ+6+s9mx89akm9nNZvYE8BTw98DTwL2XdIar2Hh5ZhZ9+rHZ+qTvPXB4qaYpIiIiIktoPgtH3wPcADzu7lcDrwD+73zvwMw+Ymb9ZvadhrG1ZnafmT2Rfu5tOPYOM3vSzL5nZq9uGH+ZmX07PfbBtD0kZpY3s0+n4181s6saznlTeh9PmNmb5jvnpdSRC897bL4dWvYf6ufWux/kprvu59a7H2T/of5LP2ERERERWXTzCdIr7n4GCMwscPe/A16ygPv4KPCaaWNvB77s7tcAX06/x8xeANwCXJee80dmVotO/xi4Dbgm/ahd883AoLs/D3g/cFd6rbXAu4AfBl4OvKvxxUCr+IWbrsZmOfaKHRuA+XVoqZXE9I8WWVPI0j9a5I59jypQFxEREbkMzSdIHzKzTuAA8Gdm9gGgeRF1E+5+ADg7bfh1wMfSrz8GvL5h/FPuXnL3p4AngZeb2Wag290fSHu3f3zaObVr3QO8Is2yvxq4z93PuvsgcB8zXywsu9tfeS1vfeU1ZIKpoXpve4aHnh3mg196nMHxEk+fGeeJU6OMTJab9klXSYyIiIjIyjGfIP11wATwVuALwPeBf3GR97vR3U8ApJ9rqx+3AI27mx5Nx7akX08fn3KOu1eBYWDdHNeawcxuM7ODZnZwYGDgIh7Whbn9lddyTV8nbZmAbGh05EJ6CjkqUcQf7v8+ldjZuqYABkeHiuTCYMaiUW1aJCIiIrJyzBqkm9nzzOxH3H3c3WN3r7r7x4BHgDWLNJ9mlR8+x/iFnjN10P1ud9/p7js3bNgwr4leSvsP9fN4/xixO2FgVGPn+FCRs2Nlothpz2XoLuS4pq+Lq9a1s6Y9N6OrizYtEhEREVk55sqk/wEw2mR8Ij12MU6lJSykn2uF00eBbQ232wocT8e3Nhmfco6ZZYAekvKa2a7VcmqlKrX/BWaYQSly8pmpT9Fs2fE9u7YzPFnhif5RDp0c4Yn+UYYnK9q0SEREROQyNFeQfpW7f2v6oLsfBK66yPvdB9S6rbwJ+FzD+C1px5arSRaIfi0tiRk1sxvSevM3Tjundq03APendetfBF5lZr3pgtFXpWMt58jgBBu78sQ4cey4Jx8AXW1T95uaKztuAE5yrjd/K0FEREREWt9cO462zXGsMN87MLNPAruB9WZ2lKTjyvuAz5jZm4FngZ8CcPdHzewzwHdJFqe+xd1rNRy/RNIppkDSp73Wq/3DwCfM7EmSDPot6bXOmtl7gK+nt7vT3acvYG0J23rb6R8tckVPgdNjJcpRTBgYWztzBGHIRLk6ZROjZtnxvQcO013Isqnn3FMzUa6y98BhbXgkIiIicpmZK0j/upn9orv/SeNgGlg/NN87cPdbZzn0illu/17gvU3GDwIvbDJeJA3ymxz7CPCR+c51uezZtb2+o+jV6zum7CgKSQB+dHCCrb3t7Nm1vWnQfWRwgjWF7JQxLRwVERERuTzNFaT/KvBZM/tZzgXlO4Ec8JOLPK9VZfeOPu4E3nfvYzzRPwbA9vUd9WPzyYTXsvHtuXNPqRaOioiIiFyeZg3S3f0U8E/M7Ec5l8H+P+5+/5LMbBWaqMRs7S3US1vu2Pcod0I9SN9/qJ+9Bw5zZHCCbdOy6rVs/HxKY0RERESktc2VSQcg3WH075ZgLqta42ZEAO25zJSa8tqOotnQpuwoWgvia9n4+ZTGzMdcLwhEREREZHGdN0iXpfFE/ygTpSqV2MmFARu68nTmM/Wa8vMF8TD/0pjzOd8LAhERERFZXPPZcVQW2f5D/YwWkwC9cTOjM+Olek35Uu4o2viCwCz5nA2NvQcOX/L7EhEREZGZlElvAXsPHKaQDRierFKJnMAgdjgxXOLUSIkXvfuLdLdlmKxEVCOf0qbxqrXnXxi60NIVdYoRERERWV4K0lvAd44NMVqK6t/Hfu5YNjQmKxFjpSr50KjEEFiyUVE1cs6Ml9l/qL9p0L3/UD93feEQj/ePkQ2NjV35eZWuqFOMiIiIyPJSucsy23+on7GGAH26wAIyQUAYGKXIyQSGA9kwYGtvge5CtmkZSq2u/KnT44QGHsPx4SLVyM9burJn13YqkTNRruKefK51itl/qJ9b736Qm+66n1vvfpD9h/ovxY9BRERERBook77M9h44jJ//ZgQGFYfn9XViZvVxd29ahlKrK488qXM3DGI4PVbi6vUdc5auzNYp5ltHh/jD/d8nip18JqAaxVpQKiIiIrIIFKQvsyPzrPOOHcIgKX2plaGMFiucHC7iwK13Pzil1rxWV54LA6qxYwZmUI7ieZWuTO8Us/9QP3+4//vEnmTza6U26zpyUzrMiIiIiMjFU7nLMuvKz/06KfaYahwTO9z8ok31MpSRyTJHzk5QrMZUophvHBnkbfd8s15+sq23nclKxIauPO4QuxOnWfUL2eRo74HDVONksaqZEQRGgDFarGpBqYiIiMglpiB9mbn7rE9CezakGiedVX7lx57H+2+5njtvvo6+rjaODxWJHTIGuTDAYxiaqPC+ex8DztWVh4GxuSePAZE7V61t586br1tw5vvI4AT5MMAbanPMoFSNtaBURERE5BJTucsyGytHrOvMMjBWqY/VKs5LUUxHLuQXbrqa2195LXCuDOUH3nkvWYMwSEJ8M/DYeerMRP12jXXlL72y96J2Dd3W204Ux5wZqxCTlM/U6t0XmpUXERERkbkpSF9mXfkMRwcnp4zVktWZIGl9+IH7nwSoB+rzdSl2IK31WH/81AhjpYjOfEixElGKYjJBwFt2P1f16CIiIiKXmMpdlln/yOSsx2rtFwODD33lqSnHtq/vIE5rzR2nEsWUo6Tu/FK1Rqy1cewfLbK5p0Bve5bxckR7PsPLr1rH3p972YJfOIiIiIjI+SmTvszOTlbPfyN3RopVfuCd9wKwoTOHmSWLQSPH/Vz2vRI5Dxw+wwOHz9DdluGDt7x01kz3+XYirbVxrHWT2dDVRkc+Q19XG5+87YaLetwiIiIiMjsF6cvMz9MkPYqdSly7rRPFztGhIqHB+o4cI8UqxWqMwYx+6yPFKv/hzx/mj37m+hmBei1Lng2N0OAbRwZ588cPckVPGx25kLFyxMBoiU3d+SnnFbKhurmIiIiILDIF6S0s9qSEpaYaO7HXjsF4OeKajV1898QwUdz8GhPlqL67aGPWfGiiTDZM+p0fHy4SYBjOkcFJMoGxZU0bBhwbKgJGdyELMK8e6yIiIiJycRSkt7BqnHZtaQjMa5wkSB8tVsiHARPxLFE68OjxYfb86UNTdgodGCuzdU0bp8fKBBie3h8k2ftTIyU29bRxdHCSU6NFutoyTFaiC+qxLiIiIiILo4WjLez7v/Pj3HD1OoK0J6M1uc3xoSJt2XDO64yWqjN2Cg0NTo2UKEcxsScLT2scKKYR+5Y1bbjD8GSFvq62C+qx3sz+Q/3ceveD3HTX/ZdsoauIiIjISqFM+jLLZwJK1ZlZ8Hwmef20Z9d2vvrUGWBmzTlAOYoZLsbs2NjBoVPjM44HaSY+DAzDMAPi5GqVOCYbBpQqM+/fDE4MTWLpK4Stve3cuH0td33hEHv+9CEArl7XzvM3d/HlQwOMlyM6ciGv2LGBkyPlWRejwtR6+DWFLP2jRe7Y9yh3gto5ioiIiKAgfdldva7QNLi+el0BSILWLWsKHBmcvVVjFMOP/+AVPH/zGH/9yIl6ML++M4e7M1mOiByiOKbSUDOTD4317VmODpcwIDSolcCbQylyMp5k0586PcYDh8/UzzXg0KkxDp0aIwwgExhjpSqffeQEa9uzXLGmMGvwPb1rTHsuw0S5yt4DhxWki4iIyJI5X6e75aQgfZk93j8zQJ8+3pELyQRGFHvTbDokfdS/9e5X8/5bpo7feveDPH1mjP6REtG0k6uxU4qcrT15Tk9UiGInkxbBlyPHgK29Bbrasjx2fHjKuY2Xck96ukMEwNBkhS297bMG30cGJ1iTLkStUdcYERERWUqt/s6+atKXWTxL1N04PlaO2LKmjVxm9qdrrNS83/qeXdvJhiFhMLWiPRMYmTBgtFilq5Cjr6uNK9e2c01fJ1vXthMExpVrkwB9ZLJCdY5WkbWFrc0WuDYLvrf1tjNZiaaMqWuMiIiILKXGd/bNks/Z0Opd8ZabgvTLwLbedjJhwLUbu2Ycq4XeZs2WlSavBO+8+br6cQNyYUA2DDCDahwzVqpy583X0dfVVl8gem1fJ5kw+fU4PVaa1zxrU2h8PdAs+N6zazuVyJkoV3FPPqtrjIiIiCylI4MTFKY132ild/ZV7nIZ2LNrO2+755scG5pZl15LWnvs7D/U3/Ttmd07+ti+voPvnRol9iQwhyRIzwQBW3vb2b2jj907+uq1WUcHJxgrR8kmSefZcMmBanQuM76mkE1q4SsRw5MVcmHATXfdP6XW606o38/Wi6gBa+VaMhEREWld23rb6R8t1tfIQWu9s68g/TJh0Ly9C8nbIblsUK+j+tbRIT70laemdFwZGCvVz4896QoTGqztyNUz2LXarHI1oliNCYxZN0marhJDezbk1df1cXKkzBP9o4yXqpSqMWPFKhu78zNqvS42mF6MWjIF/SIiIqvDnl3buWPfo0yUqxSyYcvtB6NylxZW6x2+98BhugtZrtnYxVXr2smF5562WkdFj51yNeKdn/0WH7j/SSYrEZkgeUX42UdOUI1itq1tJx9avUQmnwn5b294cT0IrdVmjRarBBi5MCSfCWjLBGSC5uU0RlLe8twNHbx42xref8v17Nm1nfZcBiepfXfgxHCJKPZLWut1MbVkzfq014L+/tHilKBfPdxFRERWnlpJcGO576XaD+ZSUCa9hdW6ohwZnCA0ODwwlmbApwbM2QDAODNepho5mdDIBEkgHxhUooiRYpVtazvoTruquDvDk5WmXVca78Ms2YG0tz3DwFhlxhwd6OvMT6nhqgXPUeyEZpgZMc7AaImr13dwdHDigjPWjecNjJbY1J2fcnw+tWSzZeA7cqFaQ4qIiKwil+Kd/cWiIL2FfefYELfe/SAnh4tU0yx0GNiUGvHG3UYr1RgHKpETxRGZIEg3MZrZRaZZzVWtNisXBlQjJ+3GSC4MGC1FtGUCegpZTo2eW0iaDY2+7jYmytX69WrBfi5MNmqKPcYdysCZ8RJrCrk5y1RmC+CnB9enx0ocGypiZnS1ZWc8rtmuM1uf9sOnx7mmr3PKz6SVFpCIiIjI6qFylxY2VoroHy0SponzSuREkU+pTY/dcZxKFE0JxN2hEsVEsRMESUb8fN1Ual1XutoyxDjVOCaOne5ChkrkbOzO09fdVi+5yaUTm369WovF9lxINXbidMoO9I+WGS1WZi1TmavkZHp5y8auNgBODhdnPK65rjPbam5ArSFFRESkJSiTvswCm71XemBJljcmKWmperIBUXsuwD3ZHTQTGOUoJnYIg+RVVyU+F8eXo2QB6NpClqPprqVXr2vnP//EC2a8vdPYdaUajVCOHHdnvBQRGJwaKdWz1lesSYNjoK+rjU3dOW7/1DcYL0fkMwGhQSV2Qktr5h3aMgFr2rMMjJVnzVjPtRvp9E2QugtZJsoVTo9XeOzkKB25kF+46Wp27+jj1rsfnPU6s63mvnpdOxOVuGUXkIiIiMjqoUz6MmvPzf4U5NPNi3JhQGAB+UxAJjQ2dOXBjMiTevEretoIzAgwtvS209eZq/cqN5IdS9d05NjUnccMvndqjP/w5w+z87fvqy+arNm9o49P3nYDv/tTL2HLmgITlZhyFLOmkKEaO0cHJxmZLFOqRjjQmQ85cmaMz33zRH2xaiWKGS9HlCsxZknXl6vWtXPNxi7WdyY15LNlrOfqWTp9E6SRyQqDE1XyYcDzN3WxoSvPPQ8fmzNbfnRwYtY+7W9/7fNbegGJiIiIrB7KpC+zH9zSy9efPkO1SavDWr35+s48x4cnIU5eVdUy4hs6s4yWIo4OFclnAnoKGbrasnS1ZdnYU2CiXGVgtMSGrjzVyDk+XKQaOQ5MlCMmyhGnx87wwOEzQBLQd7VleMWODTz07DDHhiaI4uSFQLES054NiIHjQ0WCwOhtz7K+M893T4wQO2Sw5MWCASTZ/eesa19QxnrvgcOz9iyd3irp1GgRgE09bfWymfNly2s94efq066gXERERJabgvRlduP2tfUguVE+hPFyxES5SldbhnXVHIMTFeI46d6ysastrTOPqRDj7lTT7HA1ijk1WqJcTRZsjhSr85qLA2OlKp995ARtmWBGj/SJSkw2NBxna++54LtWrlONY8IgeWERpF1hhicrHBuaTDq9BEZnPsPvvuHFM3q518pUgDl7lnbkQg6fHgeShbJhaDxzJlnYmQuNTT1tHB2c4D2ve+Gc12nl1dwiIiIiCtKX2QOHz2JQ76RilmTLM2FIIRvQ15UEnVev7+R9u7bzzs99hzWFLGOlKseHiumuoUapElONK5wdLxM5hHauh/pC1ALuYrPUPsniVYCRyXI9SK/V1ccOxUqEWfI48pmgvgmTu4MnnWa+dXSIex4+xoauPFemAfQ9Dx/jRVvXzJrlBuqdXa7p6+TMeIkTwyXi6FxBfylyjpyd5NqNnXNe59a7H9RmRSIiItLSFKQvs6R2OiByCNLe5O5OqRrzoq1r+ORtN0y5/bYDSRnHwGgpCejN6q0XIQmUs6FRjZwwAPNZNyqdVe0Fw1xOj1Voz1XoLmTpzIWMlJJacSc916GjPUN3IcumnkL9vIlylQ995Sk2dOVn7UfeuLnSkXQx6dBEecpC0JHJc+8OWPp/nj5WS3+O07Pli7FDqYiIiMhiUJC+zLb1thPFMf0jpXoXFEgCzyf6R7n17genZHtv3L6WP9z/fUrVGGNqAF7Lctc+V2Pqu4suxPkCdNL7ffbsBAEQTTsWGHS3ZRgtVtnU3TblWCEbMl6OuHLaos5qFPPws4PcdNf9dOUzDIyV6Clk68H002cm2LomudZoscJEedq9err7aWCMlZqX98zVOUZBuoiIiLQSBenLbM+u7bztnm8m3zQEx7WuLI3ZXoB7Hj7G2o4sJ4dL88qQLzSLvhDOzAA9ky4onShHlKoxT/aPsamnbcpmQ/kw4MmBMaLYyYUB7bmQwYkKmSDJcD/ZP0Y1djpyGSyXBNWhwbNnJwmCIvG0npW174ykzGe2vubTWziCNisSERGR1rSsQbqZPQ2MksR6VXffaWZrgU8DVwFPA//a3QfT278DeHN6+9vd/Yvp+MuAjwIF4PPAr7i7m1ke+DjwMuAM8NPu/vQSPbx52b2jjw2decaKVSL3ZPMhS3YWPT1WZvuGznq2F5JSlp5CG4PjlVnrxpdTNXYGxsoAhOn3Rwcn2bLGyYQBI5MVMgFMVJzAknaNA2MRAbCpt4CZEXly7PRYie5CltFipd6VJo591hcetd1Wb9y+tunxuTq+rCSz7bQqIiIil49W6JP+o+7+EnffmX7/duDL7n4N8OX0e8zsBcAtwHXAa4A/MrNazcQfA7cB16Qfr0nH3wwMuvvzgPcDdy3B41mw0VKV5/V1smNTN2FgZALDLNmICM5lext7f0fu9R0/z6crHxAGF1L4MlOyUHV+t42B3vYsmdA4OVKir6uNdR05NnS3sbW3QDYM6gF3NhPUs+25MLmD2uMfGC0Rhka+4fFaQx/4mnxobOzO88Dhs03ns2fXdkYmKzxxapTHTgzzxKlRRiYrK2qzorl2WhUREZHLRyuWu7wO2J1+/TFgP/Ab6fin3L0EPGVmTwIvT7Px3e7+AICZfRx4PXBves6702vdA/wPMzP3+VRdL53GDG8uDKhGTpxm1Q+dHCE04+r1Haxpz029XewkDRHn9u3fei37D/Xzbz769YuaZz4TUI1j8pmQTJwsbp3rvh0YmqwQmFGqJjXnURyzZU2B7kKuHpR/v3+UckOXlg1deY4OTpIJDXenWI0IzNjUU+DUSDG532l3HBhs6inQ1ZaZUb5Syyw/0T/K8GQF3JPFpXZx5UCtmLFW3b2ISGtoxb8RcnlZ7ky6A39rZg+Z2W3p2EZ3PwGQfq79Rm8BjjScezQd25J+PX18yjnuXgWGgXXTJ2Fmt5nZQTM7ODAwcEke2ELUdsB86vQY4+WIUhRTiZzYk/KNYjXm2OAEN25fW98pc31nLi2NmfvamcDYf6if3Tv6LmgRaaPAYG17jk09bXS3ZeYV4FaiJJiHpGtNNYYjZycZLVbqt+lpzxIGVt8BNEzr2q9a287J4UnAiGI/F6A3XL/2tXtSc356rDSlfKUxszxRW1BqxhU9Ba7p66KnkK2XEi1Eq2as59ppVURElkar/o2Qy8tyZ9J/xN2Pm1kfcJ+ZHZrjts1iTJ9jfK5zpg643w3cDbBz584lz7Lv3tHHlr9/kmfPNg+kkhrumHu/c5I7b76u3vv7mr5ORifLnBor1zu6NDJgfWeOO/Y9ysseOTpnUF3rdT6XyUrMZKXM2dqmSoFRPd9JqVyYlNxkPCaKnZPDRTrzGSYrEdkw5C27r+SBw2frPc3/80+8AEh7o2cCTo+W6zX4ATP7v9daP54eK/G+f/mi+nhjZrmSbqjk8bl69/kGsNMzIoPjpZbMWK+WunsRkVamdzXlUljWIN3dj6ef+83ss8DLgVNmttndT5jZZqD2svMosK3h9K3A8XR8a5PxxnOOmlkG6AGaFywvo7d+6mEeeGpw1uPZMCSKYw6fHp/S+7v2Sv3Kte1Uo5jjQ5NU0ug1nwnY1N1GdyHL9/tH+ewjcweiuUxAqTJ3+UpNFDuhJZnx+QT3kNSXW3TuFVKpGvO9k6MEgXH1unZetHUNt7/y2vrj2nvgMA8/O4gBm3ra2NJb4Ol0Z9HGAH16G8pqTD0zvntH35SOLvUSIUs2azo8MEapGtOeC+vvNjTTrL/602fG2bqmMOV2rZCx3rNr+5w7rYqIyOJTNzG5FJat3MXMOsysq/Y18CrgO8A+4E3pzd4EfC79eh9wi5nlzexqkgWiX0tLYkbN7AZLdrF547Rzatd6A3B/q9Wjv/VTD/PZR07MeZtiGmjF06Zee6Uexc6J4RJBkDydBvXbjkxWmKicvwtMcZ4Beu2tCQeiBW6UNL1MxYFN3XkqsdffBmx8izD25DEfHyoyOF6e9ZrWMC+DKW8rbuttZ7KSNIrc0JXHPekoE8eevHAAOvLhnG9DNmZEzJLP2SDg1Ghpyu1aIWO9e0cfd958HX1dbQxPVujrauPOm69T5kZEZAk1/u2paYW/EXJ5Wc5M+kbgs+nukBngz939C2b2deAzZvZm4FngpwDc/VEz+wzwXaAKvMXda/8CfolzLRjvTT8APgx8Il1kepakO0xL+dw35w7QobHu2qdkfGuv1J86PZ4EtGlKu9aKcLbymYvRuLNpMqcLv1Y1dp45O0k+NNZ25uoZ8HI14sxYlWpawhMGMFxsvkFR45wAMmkwPVGu8s7PfoszExUmK0kwvqEzx7rOpMc8JJn1DV15utqyc74N2SwjsrE7z9GhYktmrKfvtCoiIktL72rKpbBsQbq7HwZe3GT8DPCKWc55L/DeJuMHgRc2GS+SBvmtaj6lIkbScnB9Z35KILmtt72+2LSZlnrLYA6lyDk1XKISOe7OSLFKgJEJoBIzo+59eolLoyj25N2DcoWBsQrZMLlOFEP/WJltvQXWdmTZ3JP0ZK+Z623IZnXe5SgmHwY8e3aCapTUuj9vQ8fF/ihERGQF2L2jjzuhvoZsq7q7yAVY7oWjMg+1RZGD42Wq0Uh9/Mbta/na00tfYt9Y8nKpxFDfpTTpWuNkgoBs4FSnbWBkBtkgqPdRrwlIxk+PlepvM2bSEqBsCNU4ZniywnVX9NA/WiSKnYHREuUorre5bGbPru38+j3f5NjgJNU4JrBkwWxPIUNpMplbNXa+PzDG2+75Jr/7hhfrP8QiIquc3tWUi6UgfZl15gLGyvPbObQUOaXxCi97z99y7cZuhibK9HXlODFcOv/Jl5BZ8qIhF1pS4z3PDi/nM1GO6i0la7Xj1qSXeezMCNAhCfSrcUzkRuyQnbbiIjAYL0f1oHtwokKQtEtPd0otzbqA1AEMLA3QcdJdYs/dphInL6Ted+9jM66hfrkiIiKyEArSl9maQpax8sKC7LFilW8cGaRYicmHNu8OK5dKJkg2NSo3aft4qTT2P1+IyCHAMZKguRKfKwUyoKstw+4dfazryDFaqhLFTi4MWN+ZJxNa07r0vQcO01NISmQADp0cwaDp448cvj8wPiUo78pnGBgr0VPITumXeycsKFBXoC8iIrJ6KEhfZgPjlfPfaJpS5FgaIJYWMVBuxkgy3It1r816oM+Yg80dvMcOuQCmv0HhwHWbuwAYK0c8b0PnlLp0d29al35kcILQ4PDAGOUoKceZa2OoSuy8+eNfJxsEbOzO89Tpcaqx05HLYDm7oH65zdpAXkigLyIiIpcHBemyYIv5sqA6j4vPJ7s+WwXRA08N8to/OEBnLllt37gY9PRYiYlyxM7fvo9yNSYbGtdu7MaAY0NFwsAI03qcZptHNQrNcODEcInIk77ytQ2UYOH9ci/1xhjKyouISKtb7X+rFKQvs+3rOzh0cnTBgW/j7bOhUYm83i98fhXuF+Zy6Rgzl0MnR+tfO0mtuqc930ODkeK52z5w+Ez96yh2MgEEFhCaM1ecHphhZsQ4HjuYTamjX2i/3PNtjLGQ/5ApKy8iIq1Of6uWcTMjSfzGa3Zc9DXcYX1njlwmuKgA3eaq4WhBF/rL6w0fkJTH1L6eK/B2SHd0dbb2FljfmWN9Z462bEA2TH54oUFbJqhn+82SgD12CAPD3ZkoVxfcL3eujTEaN4Bq/A/ZgjZnSuvxRUREWoH+VilIX3bfOjp0UdnpWly9viPH3p972UUF2pdZjL6o7xjMyYxMGHBNXxe/+4YX89JtvWzqbqO7LUNfd56N3W3EJJtLxbGTCYze9ixXrW1neLJCLgxozwa883Pf4da7H5w1mG60Z9d2KlES4E8P9Bf6H7IjgxNUo5jDA2McOjnC4YExqlGs7apFRKRlHBmcoJANp4wttFT0cqcgfZl96CtPzXk8H9qcgbcDkTvHhibYvaOP8CIi7aXsEHM5K1VjBkaKDI6XeOfnvgPAe173Qj54y0vJhiGZ0Liipw0Lksz81es7+G9veDFfeOs/5T2veyHj5YhK7PPKetfs3tHHnTdfR19XG8OTFfq62rjz5uvYvaNvwf8h68pnODZUrC+CHS9HPHN28tL8cERERC6Bud5BXi0UpC+zsdLs291D0r0l8Lmz3O4wWoq55jf/D9VlSy+vHgZMVuMZgTZQD6Rjh5du6+XDb9zJvb+6C4Bb736QPX/6EP2jRaqRz/vtu/2H+rn17genvCD45G03TNl5diH/IXNPdnatRD7lhdmJ4cl5ZfVFZGnU/u3fdNf9837XTWSlmOsd5NVCC0eXmZ2vnyAQzXn0nMoqDNCNpV3MGgaGpQHu4YHxKff9bz/6dXZs6qqvM9h74DDv/Nx36Lw35Mx4me5ClijdsfT4cJK57i5k58x6z7VwpnYfT/SPMjxZAU92P80EAV1tGf7zT7yg6TXHytGMH1ryar15n3gRWXpaNCfzsZK7n+ze0cedJH/njg5OsHWFPb75UJC+zDKB02TzTJmnpQzQk9dT57q6TL9vB57oH+NXPv0N4tipxE4UO8fTvuod+Qz5TEg1dszPtWRsXAC698BhHj81QiVycpmAcjWmPRfSU2gDzrVefN+9jzFRSdpEduRCBsfLyUZOaYufuX4unblwxgu/GMgaq6rWT6SVXeq2q7LyrIYXcrt39K2Yx3IhVO6yzALTU3C5cD9/3X7kzvBkldFSRDVywrSzS+RwcrjIhq582u7RKUdx/e27G7ev5Y59j/LU6TFGilUmKxHDExXGS1XOjJcZmTy36VUhG/LUmYn6H/DTY2UyYUA+DChkQ67p66KnkG1aQrP/UD/PNtSf19p2QhLYr6ZaP5FWpkVzcj7qfrLyKZO+zEoqIl9RGiuXomkRfTF9rq9Y08bJ4SIO9HW1TenQcmasSoARBEbsTpSm7U+OFDk9VqIcxYSBEcde/wNeG6t9Dc3/mNeyLuUoJrTkhYOTBOm171dTrZ9IK9vW207/aHHKhmurbdGczO18+2fI5U9B+jJTR5WVy4HqtCf42OAk+WxQD9gfOHyGh58dpBLFZMOg/qItiCETWNJjPXYq7nhoGKSLTuHMeIn1nW3kwiC5H4dcmLwz0+yPee2FQD4TJFl+oJIG9dkw4Or1HTPeVlzJ9Y4irWzPru3cse9RJspVCtlkh+TVtmhO5qYXciufai1EllAldsZKUyvCS9WY2Ke+qxI7lNNgvJYlr0TJgtVMkNShnx2vMFGusr4zRxQ71TimGsU8dmKEo4OT3Lh97ZT7qb19vr4zT0xSJ5/LGEEAXW0Z3H1KF4n9h/r59Xu+yTeeHeTk8CTfeHaQX7/nm+owIbIE5mq7KgLqfrIamJ+ns8hqs3PnTj948OCS3d9Vb/8/S3ZfcvkKA8hYQK0ZUIzTljFeuKWXJ06NMDRZqbffNM4tIL1mQyc//oObeeDwWR5+dhAz2NiVLEI9PVaiVI3JZYJ0cWp2SsbO3TkxXEw62qT3G8XONX2d9baSIiKyfGrvdq7W7icrgZk95O47mx1TuUsLqwVbc21VL6tDFEMYJoFyNU4y77HDjdvX8kT/6JT++MkGV2AO3x8Y4wP3P0lfV45N3XmODRU5NjTJljVtbOppoxI57dmASuwzukgcHkgWpwbpblpm4OYcPj2+DD8BERGZbrV3P1npVO7Swpy0j7oIyaLQchTXF6e6O+//8hOcHis3vb2T9M4PDEYmq3QXcqzrSEpjnjk7ycBoiTdcv4WBsRInh4scOjnC4YExRiYrFLLhkra3FBERkamUSW9xoRmxuRaYSl3tV2G+vxOBJQH+aLHCwGipfv5Iscrvf+kJQoMgMEIzqpFzfHiSddUchWxAJXIs9nNlNg7PWz91UdL0xaU3bl/LA4fParGpiKwKWmAvi0VBeotzkoWCZdW8yAWK064vxwYnaNbwM3Lw2Kni9Sz9yZES23oLDE2U65syhYGxJp/l7a99fv3c6ZtpPHV6jK89fZa+rhzrOvIXtbmG/vCJSKtbDRsKyfJRkH4ZqPWyVpguC2UkXWGqUTTn70+zrPzxoUmCwNjYmcOCYMqipFoA/fCzgxiwqacNM2O0WK2X16zvbLvgXRL3H+rnbfd8k5HJCpXIOTo4ydefPsPtP3YNt7/y2oX+GEREFoV2hpXFpCC9xZkZ23oLPHNmHO17JAvl0z4vROQQRc6xkRJvfcW54LgxcxR70srx+FCRK9YkZTW18pqauTbXmC1b/r57H2NwvDxl0XQ1hg/e/wQv2rpGf/xEpCVoQyFZTArSW1xXPkw3q1numcjl7GLeiXGHP9z//XpwvPfAYcrViFMjSZY7vRUnhibJhQHlKK5vqgSzb64x19vET52ZIPZk3ti5eVRjuP1T36C7kG2JEhiV5IisbtpQSBaT+qRP00p90gPgh7evA+BrT58hIOnWIbIc2rMhz1nXzqGTo7MG/JnAqMZOLjQ297RRjmLOjlfozIds6MwnJTGlKtt62xkcL01p/QgwMFpkohwxUqzWx6whSIekZ/zzN3XX+7kv1wYvjS8yGvvLT5+PAnmRlWu+/x0QmY36pF+m3OCJ/lFGi1VCwIIAYkXpsjwmKhGPnRyd8zZR7PS0ZRgpVnnm7CQAve0ZOvMZnugfI/Kk28zp0RLlKGZbb6F+7shkhTPjZeLYp2T+3evJdADaMiFmtqDaz9kC5YsJoOdTi6pFZSIr2+4dfdwJ2lBIFoWC9BbWkQ2YLEf17eJNAbq0uDAwxsvRlLHBiSqDE+cy47FDMf2dfvbsJM9ZZ3S1ZTk5PFkvn5m+O0AtYA8NNnTl6+Pzqf2cLVB+2SNH+Ztvn6zfZ/9IkV+/Z5T/9oYXz+sP7JHBCUKDwwNj9RKf9Z25KfPRorLLn94JkfPRhkKyWBSkt7DxcjylrECFSdLqqgts6O/A02cmZtTMT7+KGRQyIT3tGbrazi3Smk/tZ7NA+fRYkb9+5MSUzknlyBkYK/Mf/vxh/uhnrp/1j24taDs1XKQaO6FBJgyoRs6xoSLP29BRv+3FLCpTcLj89E6IiCwnBektLNlxNPliIaFPJjDymWBGRlOkVZ3v9ztjxr//p9u55+FjTJSrU2o/9+zaPue5zQLl4YnKrJ1vJsrRlECsMVjuzIWcGS/TXcgSWHJu1ZN3uYIgAJ+6S/CFLipTcNga9E6IiCwnBektbvq6XiMJwitzZCyrsRMpQJcVpBI7//3vnuSf/+AmTo6Um9Z+1oLpx0+NNHSdgYlylZNp1huSmvjzJfyzobH3wGGAKcHykwNjVCOnI58hBnKhJX3oY2jPGJu684yVzpX27Nm1nbfd802ODU3WN4TqzGf4zz/xgjnv/1IFh8rGX5zLrb2enm+RlUVB+mXGYc4AvfF2IitJJXL++pETdOaThaMT5ZF6IP2to0P84f7vU6nGs2TIz303n4qcwwPjPHV6nEePD9OeC+kptAHJwtjAYGC0lLZGdfIZoxzFTFYinjk7SRgYH/zS4/W+8pbevbuD24x6+2YuRXDYLBv/tnu+yYbOfL3DzmIGcRcSMLZKkFmbx8BoidOjJTb1tNXLrJayvd5Cfh5690Vk5VELxmlaqQWjiJxfezaov3BtzKBfLCPJumNJuU1MEqT7PMvPfvIlmzk5Up5R7nJ6rMh4KZqz1/utdz9YP2+0WOHkcJFiNcYMfqCvk7e/9vnnbfP4vnsf4+mzE0SxkwsD2nMhgxMVMoHxvL7ORW0VdyFt6VqllV3jPKpRzLGhIgBb1rSRCYMlm9NCfx6NvzM1E+UqfV1tfPK2GxZ1riJy4dSC8TJ1IfXoF3OeyOVoYpE2D3CSXVdxiHCywfyy8DV//cgJ1nZk6cxn6h1gQjNKabZ/olzl9GiJX7/nmzM6yty4fS1/uP/7VKN4yq6rAfDkwDhvu+ebvPGG5/DA4bN859gQY6UIM2jLBERxzK/f801Oj5Xrj6MSRYyXI4zkvw9ztbC8FNnsCynXuRQlPosxdzBOjRY5OVLi+it7lyy7v9Cfx+VWmiMi56cgvYXN502OZjtJ6s0RkUtvoa8FHJgsx5wZnzx3jYZ/rbV/u4MTFe76wqF64PXBLz0+a4Cey4RUqhFnxsr8/peeIBca5fRG7skLlonh0pxzgqQn/emxpFf90cFJ9h/qry+Sna1kAph3ADw9YKzd39NnJrj17gebnnuxQealKveYPo/uQpautgzDk5UlzUgv9OehnS9FVp7g/DeR5TKf2tUwmN/tRGTpTVRmX8BdiaFUjanGzmMnR3nhu+7ltX9wgA/e/wSxO9lw2n+eDapRTLXhXbILKe+pRM6xoUkmykn5RLmaZN5rWehsaESx89TpcZ49O0H/SJF3/vW3uWPfo/SPFqcEwPsP9Te9j2297ZwZL3F4YIxHjw/z7NkJitWIfGiznrutt53Jhp/XyGSFJwfG6B8tcevdD856XzWNmefaOwWNC4Dna/o8YHmC3YXOY8+u7VQiZ6JcxT35PJ/uRyLSuhSkt7D5/PmtxiprEVkJxkox3zs5SjVOAunaJmY17jMXjV/ov/3p/ewHxsr8hz97mCf6RxmZLPPMmQnGyxFR7FSimKNDRSpRNO8A+Mbta+kfLVOOktIeB6IYutqys57bGGSOTJY5NjRJNXI2defP+6IAksxzIRtOGbuQco9WCXYXOo/dO/p4w/VbGBgt8djJUQZGS7zh+i1aNCpyGVO5y2WoWYmLiFz+GsPyGWVsl/i+pnfBmahElKOIxtcGjbH88ESF9Z1t9e8L2ZAn+ke59e4HZ5TAPHD4LBs6c4wWq1SiJBtsQP9oibFSdcbOrHBue/X33fsYh09P4p7U2MPU+nloXnbTWO5RK68pVWPac2G9nGc+WmWb94XOY/+hfu55+BgbuvJcmS40vefhY7xo6xoF6iKXKQXpl6F8Jmn9FrsvaCHbfGUDIwyNjV1tTJSrDKQL0ERkpWueAjCgFE3N7J8ZLzFarM4ogbmTJKu9vjPPhq42Hj81Wn9XwEmy+MeGilzT19l0BhOVmMAgExrucHw4qenvasvwxKmRWevO9+zazh37HmVgtMiZ8XJ93h35cMG16a2yzftC5qGNl0RWnlVR7mJmrzGz75nZk2b29uWez8VyknZgC2UGz93QwVXrZtY01ura27Mhf/LGnfR1tZEJjY3dbawpZJKOEEAYGF35gHXtmabni8jiyAaL/68smuVVvwOZIJhSenF2vEJve7ZpCcyUeup0Jbtzrmd8MjzzvmqBZlsmBIwgMAKM02MlJisR5chnrTvfvaOPO2++jolyROxJ28ktvQXWd7ZdUG365eZSlfuISOtY8Zl0MwuBPwT+GXAU+LqZ7XP37y7vzC5MJoDrr+ylf7S48HMt6bdrZmSCZHFY7c9kLhMQx057PpzxNuvzN/c0fZv1tX9wgKdOjxOlfxA780kHBAe2r+/gsZOjF/1456sWvqgMSFYiA6pztG3KBRAEQT1jnQ2gnCa+rSG2r10iYGppDSQvwGtBev0UO3fOW3Y/lwcOn62XXgxNlFnfmZ9yjVpQ+J7XvZA79j3KRLlKDITpLq9BYGRCY1NnnvEmuyLXOpps6MpzfKhIjIM5papTiZxcJpgzEN29o4/uQpYr17ZjDQ98NQSr6u4isvKs+CAdeDnwpLsfBjCzTwGvAy7LIP15Gzrrb+vmMgGVakzM3G0Xkww49Q1M2nMZ8pmAchRTTbtDZAKjuyPLVeuSt6Dn8zbrb7xmx4zNNvLZsL7Zxmv/4ADfOzk6IxhYDJnQ2LGpGyBZ+HZ28jxniCyvjCWBci153ZUPiNyYSPuZ07DfQTY0YnfckgWY063ryk+pF58oV/n+wPis972xJ8/geKW+QVKYlpcYyWZNZlbfuAlg65o2bn/ltdzecI1mm+fUgsKpL/QnCUObsmtnbZOd6WqBZldblivWJDu7FqsxHbkMd958HXsPHD5vILpag9Xa34WJcnXK5kfq7iJy+VoN5S5bgCMN3x9Nx+rM7DYzO2hmBwcGBpZ0cgtV22nwzpuv46q17WCQDwM2dGYJm7wbnvRWDrj9x67hN16zo94tYH1nDvcke3bl2gKbetrIhuGC/oNem0dfVxvDkxX6utqm7Ib3G6/Zwda17WzqzrOY79QHBrmGdnWZMKA9G5IJjHwmINfwg8lnAjrzYbPLiJxXYJfuP5o7Nndz9foOrlzbzkf/zQ/x7d96LX/0M9fTU8gkHVHSADm0pFf3NX1dbOjM05YJSDdCJRskAXQ2DGd0Abnx6l5Ir1P7ALjx6l6yYciW3gLPWVsgHwY4cNXadm7/sWvo7ciRCZOMd1s2YH1njt9+/Q/OmP/5uo/s3tHHJ2+7gb0/9zL6utsIAztvl5LGa3bmM2zqaWPLmnY+eMtL2b2jb14dT1qlO8tSO99/j0Xk8mPN6gJXEjP7KeDV7v4L6fc/D7zc3f9js9vv3LnTDx48uGTze+unHuazj5yY12239uT5yjteOWWs1tv46OAEHbmklGVgrES5GpMLjWs2dk8pVWm8fWc+g7szXo4WrYNB7f6eODXCeDlKF7w2r33d1lsA4PjQ5JRNXAoZo6c9x0Q5olytUqwm47VFYZOVmA2dOdZ35uvZozdcv4VPPPgMo8Uq1TgmMMOBnrYM12zsBo/52jNDU97en+1fwtaePGcnqkxUkl0dO3NJlqo6y1sEmSDNdhp05TNcd8W5cqGX3flFzkxUZ5yzrj3DWDme0XavmVwAd7/xh/jcI0fZ962Ts9YRXy46c0G9Z3hN7UVd40MLbP47foYBbOjMk8sk7+z82me+MeXnvrYQ8t6ffDG/8qmH62UhkPwetGWTspF535cZQQD5TEg2MGKcjmxINhMyPFlmaHLm8/0DfR2MlyO2re2Y8e9u/6F+7vrCIQ6fTjLhV69r5+2vfT7ArNvEQ/MuIG/91MP135EwMG5+0Sbef8v1U/47MP3f/lzHppvvbS/lNedzrYXcn4jIcjKzh9x9Z9NjqyBIvxF4t7u/Ov3+HQDu/l+a3X6pg3Rgxh/Slz9nDU8MjDNWqtbHOvMZfnfa1uGXsw9+6XE+9JWnGC9HdORCfuGmq7n9ldcCU3cOnB6M1HZFbPwDfOP2tVNqZWt/kC8kgJgsVzk7USFO32WoBTWznfPEqRHKaa3s+o4cZsZYqTrn/d30X77E0YZdIWsvvuYKzhYSlEyUKgxOVuuPYXNXjmPDpaYvQgIgm3YLck/KGzJpfXM19qQWOo1WG38eswV/s81pa287g+NFDp06V4Jx49W9fHLPP2kyq7k1e15rP6Mn+kdnfYE6n2u9+aar+CfPW0+l6kxWIo4NTfDsmQmODE5wfKjIsaFJjg9NcmxokuI8tiDtzGfoKWQZL1WpRDFrO3K89oWbeeUL+ljXkSMTBslHkKwTqX8dGtkgIJj2FpSCTxGRlWW1B+kZ4HHgFcAx4OvAz7j7o81uvxxBejOr/Y/xan/80lpqm/qUo5hKNaZcjekfLfHM2QmOD06kwXuR48OTHBucZKQ4M3s+XT4TsLmnjS1rClxR/0i+35iWh4SNgXv6dTY0MkHyuXFxpIiIXH5WdZAOYGY/DvwBEAIfcff3znbbVgnSRaT1uSddRypRfC6Ij5yzYyWODSUB+/HhySlZ+NPz2HcgDIxN3W1csabtXADf08aW3gJX9BTIZYL67TJhQLYWzKcZ+DAwBfEiIpeBVR+kL4SCdBG5FKppwJ50UYrrwfxoscKJ4SLH06C9HsAPT3JyuDivWvgNnfl61r0xC3/FmgKd+XNdTTJBUF8EWvs6WxsLFMSLiCy3uYL01dCCUURkySWZbSgwtZuQu/Pcvs4kaK/GVOK4/nW5GnFqtFQP4JNsfDHNxk9SSVdUD4yVGBgr8c2jwzPut6eQPRfA9xS4ovdcFn5NITslMG8M4rNBQKggXkSkZShIFxFZQmZGPhOSzwBT9wKiGsVsW9tBeevU7HsliondOTNWnrJ4tZaFPzE0Wd8caHiywvBkhcdOzNxMrJAN0+z7uTKaLenXG7ryBNOCcmXiRUSWj4J0EZEWMVf2vRzFbOop8AObuuq175VqEry7O8OTlRkdaI4PFTkxPMngRAVINvV5cmCMJwfGZtx3NjQ29zQE8D0FtvS2cUVPbR+FmR3iMw0Bu2riRUQuLQXpIiItbkr2fZpqFFONnQ3dMc9Z1zEl+14zXqpyYrg4YzHr8aFJBkaT9pyVyHn27ATPnp2YcR+BQV9XW5J1TxevNmbk27LNNwjL1EtozgXxSacadacRETkfBekiIpexWvZ9eqA8vfPMxp42rtvSU8++15SrMSeHzy1eTYL4JIA/MVwkSjcgOzlS5ORIkYeeHZoxh3UduSmLV7c0tJPsastSmnFGorE7TdjQYjIMmveJFxFZTRSki4isQGZGLmP1do2Npvd9X9OeSxezxjNu1z9anFJGU8vAHx+apJjuEntmvMyZ8TLfPjZzIWtXW2ZGC8laEL+2I0cU+5xBfFI6E9Qz8I018qGCeBFZwRSki4isMknwGzbNvjfWu1eimEIu5Io17bxsWrted+fseHlKC8nGLPxouqHTaLHK906O8r2TMxeytmUCrlhTYHNDO8laAN/X1QYkLxTK1ea7uwbWEMSHU3dtzYYK4kXk8qYgXUREgPN3nqn1fa+V0GTDkHWdeX5wa8+Ma40Wmy1kTTLxZ8aTDZ2K1ZjDp8c5fHp8xvmZwNjU0zYjC3/FmjY2pxs6xe7EaUnPbI+nMWDPhjN3cBURaVUK0kVE5Lxm6zwTx42Be1pGU43pLuToasvyA5u6ZlxrshJxolY6M3yuE83xoUlOjSQbOlVj5+jgJEcHJ2ecb8CGrny9Dn56Fr49l/xpa6zLb6YWxNfKZ+r18LVAXkG8iCwjBekiInLBgsBom6V0pnHhamMZTSEbsn1DJ9s3dM64XiWKOTVSnJGFP5EG9JXIcaB/tET/aIlHjsycU297tt5OcnoA39OwodO5OQJEM67TGMTXFrM29oxXr3gRWUwK0kVE5JJbyMLVxmA+GwZs7W1na2970/NOj5WmZN6PN2zqNJlE2wxOVBicqPDdEyMzrtGRC9k8rQNNLYhf15mbsqHT1CC+uWa94tVmUkQuBQXpIiKypBaycLX2vbsTBsbG7jY2drdx/ZXMOHdwopIE7cONAfwkx4aKDE8mGzqNlyOe7B/jyf7mGzolde8zA/iN3fmm5S/VOGaWda31x6o2kyJyIRSki4hIS5hr4Wot016pJoF8NU6+rsZx/dy1HTnWduR44ZaZC1nHStUZLSSPpR+nx8rpfTjPnJ3gmVk2dNrYPbMPfG1ha36WDZ2i2IniaNY2k4El5TT1xa1pZj5UhxqRVU9BuoiItLxsGJANA8hNHZ9t4Wo1TrLvNZ35DNdu7OLajTMXspYqESdGivWse2MZzcmRcxs6nRgucmK4yEPPDM64xvrO3CwBfIHOttn/1MbulKvzazOpDjUiq4uCdBERuWzNtnAVZmbfa99H8dSe7/lsyFXrOrhqXceMa0Sxc2qkOLMOPi2pKaXB9emxMqfHynzr6MwNnbrTDZ1mBPBrCvS2Z+esW59vm8n6gtZgamZei1tFLl8K0kVEZEW62Ow7JDXltYB6OnfnzHg5CeAbNnKqldGMl5IVpyPFKiMnRznUbEOnbDClbKbeD763wIbO/HnLXc7XoQaYsVNrJkzKamqZeQXxIq1JQbqIiKwqc2Xfk2D9/Nl3SLLY6zvzrO/M8+Kta6Ycc3dGitPq4Bt2ZT1b29CpEvP9gXG+PzBzQ6dsaGyaVgdfe8GwqbutaeecZuqLWyvNj2eCgDA0smn5TL2sJs3Ma3GryPJQkC4iIpLKZQJyzMy+19pGzif7DkkA31PI0lPI8vzN3TOOT5Yjjg+fax95Yujcpk79o8mGTpXIOTI4yZFZNnTq686fC+B72riit8CWtDtNIdd8IWsztSB+tsWtYTBtk6dpmXktbhVZHArSRUREzmOutpG1oL0anT/7XlPIhTx3QyfPnWVDp5PD0+rg0yz8yZFifUOnUyMlTo2U+MazQzOu0duenbUOvrsts6ASl6RDzdyLW2eU0qT94tWhRuTCKUgXERG5QBeyadNs2feabBiwbW0729Y239BpYKx0ro3kYNqRZjj5vlhJAunahk6PHm+yoVM+TLPvhbQGPsnCX9Ezc0On+ah3qGF+i1uz4bTMvDrUiDSlIF1ERGQRzCf73rhhU/U82ffaNTd1t7Gpu43rr+ydcd3ahk7Hpu3GenxokpFiFYDxUsTjp8Z4/NTMDZ1ymSAJ2mepg7+QrPj5FrfWgviw1pmmXiN/bgdXLW6V1UhBuoiIyBK6kOz7bC0Yp193zg2ditWGxauTHBtMMvDHhiY5k27oVK7GPH1mgqfPzNzQKdnxNV/PwtcC+C29BTZ3z76h0/lMCeLnubi1FrzXMvMK4mUlUpAuIiLSIhYj+17T2Zbh2rbmGzoVKxEnhht3Yz3XTvLUSLKQNYo9rZEvAjM3dNrQmZ9R/74lDeQ78hcXbsxncev0TZ4yDZl5daiRy5GCdBERkRa3WNn3mrZsyNXrO7h6/cwNnapRzKmR0pQsfGMZTSVKXiQMjJUYGCvxzSYbOvUUsvWAfXoAv6Yw94ZO85Esbo1mDeK1uFUuRwrSRURELmNzZd8bM+61ryvVmHiOhavTZcKALb1JWcsPXTX1WOzOmbHylDr4Yw1tJcfLSQ368GSF4ckK3z0xc0On9lyYbuDUlpbRnAvgN3TlF7yQtZn5Lm7NNvaJn5aZF1lqCtJFRERWIDMjnwlpVmlSTQP2xpaRlapTjeeffYckQ72hK8+Grjwv3rZmyjF3Z2SymgTvw1PLaI4PTTI4kRSgT5QjnhwY48mBmQtZs6GxubH+vbaYtafApp62ZEfZS6CxnKiZ6R1q6qU0aVCvxa2yGBSki4iIrDLJ4ksoMDX7HsdOJT6XcW+sf5+rbWQzZkZPe5ae9iwvuGLmhk4T5Wq9D/yxaZ1oBkZLOMmGTs+eneDZszMXsgYGfV1tTctoNq8pULjAhazNnK9DDTBjk6d6WU2amVcQLwulIF1EREQACAIjH6TZ9/zUY40Z9/lu2jSX9lyG5/V18ry+mRs6lasNGzpNq4E/OVykGjuxw8mRIidHijzUZEOndR25KS0kk77wSRa+u5C9oDnPpba4dbYONbXFrdl6u8mGnvFa3CpNKEgXERGR88qGQVJekps6HseNQXu6iLV6/k2b5pLLBFy5rp0r182yodNoqZ6Bb9yZ9djQJKV0Z9Qz42XOjJf59rGZGzp1tWWmtpFs2Jl1bUduUbLe51vcGgbTNnmalpnX4tbVR0G6iIiIXLAgMNqaLFwF0mD90mXfId3QqaeNTT1tXP+cmRs6nRkv1zdyOteRJgniR9MNnUaLVb5XHOV7p2YuZG3LBGxuqH2v7cq6pbdAX9eFbeg0H0kQ75SrzeviA2sI4sOprSbVoWZlUpAuIiIiiyKXCcgxM/ve2Dayeomy75DUwa/vzLO+M8+Lts48PjJZmVE+Uwvoz4wnGzoVqzFPnR7nqdPjM84PA2NzT1t9V9YrGjLwm3sKTVtkXiqxO/ECFrfWS2nqPePVoeZyoyBdREREltRCNm2qBfEXk32v6S5k6S5k2bFp5kLWyUrEiaEmAfxwccqGTkcHJzk6OMn0DZ0M2NCVnxK4N5bStOcWN+Q63+LWWhAfNmzyFKafa5l5LW5tLQrSRUREpCUs9qZNcylkQ7Zv6GT7hpkLWStRzKmRJHg/Nlist5Q8kZbUVCLHgf7REv2jJR45MvP6ve1ZNk8rn7miJwniuwuZRQ+QpwTxsyxuzQTTSmlqX6eZeQXxS0tBuoiIiLS8hWTfL2TTprlkw4Ctve1s7W2Hq6cei905PVqa2kZyeJLjaTA/kW7oNDhRYXCiwndPzFzI2pELZ+zEWsvCr+vMXZINneaj3qFmFmEwc5OnTENmXh1qLi0F6SIiInLZmiv7Xo2SOvfFyr5DsqCzr7uNvu42Xnrl1GPuztBkpV73Pr0OfmgySWmPlyOe6B/jif6ZGzrlMkFaB3+uhWQtC7+xO7+ktebn61ATWJM+8aFpcesFUpAuIiIiK1Jt06Zm2ffGjHtj/fulyr5D8gKitz1Hb3uO667omXF8vFSdsRNr0pGmyMBYEgqXqzHPnJngmTPNN3Ta2D2zjeQVawps7mlr2nFnMcXulKtOmbkXt9YC9mw4MzMv5yhIFxERkVXFzMhnmm/aVE2D98aWkZWqU40vXfa9piOf4ZqNXVyzsWvGsXI15sTwzN1Yjw8lGzhF6YZOJ4aLnBgu8tAzgzOusa4zlwTtPTMXs3a2LX0I2Fia1Exjh5raJk+NmfnVtrhVQbqIiIhIqpZ9LzA1C924aVO9bWQa0F9M28jZ5DIBz1nXwXPWdcw4FsXOqZHi1Cz88LlNneobOo2VOTNW5ltHh2dco7stM6MOvhbE97ZnlyUYntqhprnpmzzVy2rSzPxKCuIVpIuIiIicx1ybNjVm3MvRuQ2cFiP7DskCzlqAvXPasdqGTseGJjnesJFTbXfW8VISAY8Uq4ycHOXQySYbOmWD5Po9MwP4DV35Za0try9unaVDTW1xa7bebrKhZ/xltrh1WYJ0M3s38IvAQDr0m+7++fTYO4A3kzT5vN3dv5iOvwz4KFAAPg/8iru7meWBjwMvA84AP+3uT6fnvAl4Z3ofv+3uH1v0ByciIiKrSjYMyIYzN21qzL5XLuGmTXNp3NDpxVvXTDnm7owUq1MWr9bKaI4NTTI4kUS+xUrM4YFxDg/M3NApk+74Wgva62U0PQU29bQt6oZO83G+xa1hMG2TpyAgnw2WvH5/PpYzk/5+d//dxgEzewFwC3AdcAXwJTO71t0j4I+B24AHSYL01wD3kgT0g+7+PDO7BbgL+GkzWwu8C9gJOPCQme1z95lFWyIiIiKX2EKy77XvL8WmTbMxM3oKWXoKWZ6/ucmGTuUoWbg6dK50phbA94+UcKA6ZUOnadcn2dDpXA/4qVn4Qm75A+EkiHfKDb0mu9qyCtLn4XXAp9y9BDxlZk8CLzezp4Fud38AwMw+DryeJEh/HfDu9Px7gP9hSUHSq4H73P1ses59JIH9J5fs0YiIiIg00UrZ95pCLuS5Gzp5bpMNncrVmJMjxSlZ+KQTzSQnR4ozNnT6BkMzrtHbnp2xE2utbKe7bfE3dLrcLGeQ/stm9kbgIPBraYZ7C0mmvOZoOlZJv54+Tvr5CIC7V81sGFjXON7knCnM7DaSLD1XXnlls5uIiIiILLrZsu+1RZW1evelyr7X5DIBV65t58q17TOORbEzMFaatYymWEmy1rUNnb5zfOaGTp35zJQONFf0tHFFmpFf35lblQH8ogXpZvYlYFOTQ/+JpHTlPSRlKO8Bfg/4dyTvlEznc4xzgedMHXS/G7gbYOfOnYv/my4iIiKyAPVNm5iZfY/ic5s0LXX2HZI6703dbWzqbuP6K3unHHN3BicqU0pnGrPwI8UqAGOlKo+fGuPxUzM3dMqnGzqdq4M/Vwu/sbttxW6StGhBuru/cj63M7M/Af4m/fYosK3h8FbgeDq+tcl44zlHzSwD9ABn0/Hd087Zv5DHICIiItLqksWQs2ffpwfwS5V9h+TFxdqOHGs7crxwy8wNncaK1XrAXmsjWcvCnx4rA1Cqxjx9ZoKnm2zoVHuBcMWaZDfWJPvexpbeApu728i3YK35fC1Xd5fN7n4i/fYnge+kX+8D/tzMfp9k4eg1wNfcPTKzUTO7Afgq8Ebgvzec8ybgAeANwP1p15cvAr9jZrWXdK8C3rHYj01ERESkFdSz7006rtSy7+UoTnddTb5fqux7TWdbhmvburi2yYZOpUpUbyE5pYxmeJKTw0ViTx5Hrb0kzOwNsqEzP7WMpqEWvjPfakszp1qu2f1XM3sJSfnJ08AeAHd/1Mw+A3wXqAJvSTu7APwS51ow3pt+AHwY+ES6yPQsSXcY3P2smb0H+Hp6uztri0hFREREVrP5Zt9rGzZVqjHxEgbvAPlsyNXrO7h6/cwNnapRzKmRUpp9n7Yr63Cx3r1lYKzEwFiJbzbZ0KmnkGXLmjauXNfBD121ljffdPWiP6aFsKV8tXQ52Llzpx88eHC5pyEiIiLSUmbLvleixdm06ULF7pwZKzfUwCc7sx4bmuTE0CTj5Zlbmv7w1Wv59J4bl3yuZvaQu0/fkwpovRaMIiIiItKC5sq+N2bca4F8NfIlz74DBGZs6MqzoSvPi7etmTHXkclqvWzm+NAk/aOlpvXyy01BuoiIiIhcMDMjnwnJZ4D81GPVNHhvbBlZqSatJJdrrj3tWXras7zgimRDp662LBu68uc5c+kpSBcRERGRRZEJAzIhFJiafZ++aVO1of5dpdgJBekiIiIisqRm27QJmJJxX+pNm1qJgnQRERERaRnZMCAbzty0aXr2fak3bVpqCtJFREREpOXNln2frW1k9TLPvitIFxEREZHL1oVs2tRqbSObUZAuIiIiIivSfNpGBrZMkzsPBekiIiIisqpMaRvZoma+LyAiIiIiIstKQbqIiIiISItRkC4iIiIi0mIUpIuIiIiItBgF6SIiIiIiLUZBuoiIiIhIi1GQLiIiIiLSYhSki4iIiIi0GAXpIiIiIiItRkG6iIiIiEiLUZAuIiIiItJiFKSLiIiIiLQYBekiIiIiIi1GQbqIiIiISItRkC4iIiIi0mIUpIuIiIiItBhz9+WeQ0sxswHgmWW46/XA6WW4X1k+es5XFz3fq4ue79VHz/nqcqme7+e4+4ZmBxSktwgzO+juO5d7HrJ09JyvLnq+Vxc936uPnvPVZSmeb5W7iIiIiIi0GAXpIiIiIiItRkF667h7uScgS07P+eqi53t10fO9+ug5X10W/flWTbqIiIiISItRJl1EREREpMUoSBcRERERaTEK0luAmb3GzL5nZk+a2duXez5y8cxsm5n9nZk9ZmaPmtmvpONrzew+M3si/dzbcM470t+B75nZq5dv9nKhzCw0s2+Y2d+k3+v5XsHMbI2Z3WNmh9J/6zfqOV+5zOyt6X/Pv2NmnzSzNj3fK4eZfcTM+s3sOw1jC35+zexlZvbt9NgHzcwudE4K0peZmYXAHwKvBV4A3GpmL1jeWcklUAV+zd2fD9wAvCV9Xt8OfNndrwG+nH5PeuwW4DrgNcAfpb8bcnn5FeCxhu/1fK9sHwC+4O47gBeTPPd6zlcgM9sC3A7sdPcXAiHJ86nne+X4KMlz1ehCnt8/Bm4Drkk/pl9z3hSkL7+XA0+6+2F3LwOfAl63zHOSi+TuJ9z94fTrUZI/3ltIntuPpTf7GPD69OvXAZ9y95K7PwU8SfK7IZcJM9sK/ATwoYZhPd8rlJl1A7uADwO4e9ndh9BzvpJlgIKZZYB24Dh6vlcMdz8AnJ02vKDn18w2A93u/oAnnVk+3nDOgilIX35bgCMN3x9Nx2SFMLOrgJcCXwU2uvsJSAJ5oC+9mX4PLn9/APx/QNwwpud75doODAD/Ky1x+pCZdaDnfEVy92PA7wLPAieAYXf/W/R8r3QLfX63pF9PH78gCtKXX7NaJfXFXCHMrBP4S+BX3X1krps2GdPvwWXCzP450O/uD833lCZjer4vLxngeuCP3f2lwDjpW+Gz0HN+GUtrkV8HXA1cAXSY2c/NdUqTMT3fK8dsz+8lfd4VpC+/o8C2hu+3kryFJpc5M8uSBOh/5u5/lQ6fSt8OI/3cn47r9+Dy9iPAzWb2NEnJ2o+Z2Z+i53slOwocdfevpt/fQxK06zlfmV4JPOXuA+5eAf4K+Cfo+V7pFvr8Hk2/nj5+QRSkL7+vA9eY2dVmliNZiLBvmeckFyldzf1h4DF3//2GQ/uAN6Vfvwn4XMP4LWaWN7OrSRabfG2p5isXx93f4e5b3f0qkn/D97v7z6Hne8Vy95PAETP7gXToFcB30XO+Uj0L3GBm7el/319BstZIz/fKtqDnNy2JGTWzG9Lfkzc2nLNgmQuft1wK7l41s18GvkiyWvwj7v7oMk9LLt6PAD8PfNvMHknHfhN4H/AZM3szyX/0fwrA3R81s8+Q/JGvAm9x92jJZy2Xmp7vle0/An+WJlgOA/+WJPml53yFcfevmtk9wMMkz983SLaF70TP94pgZp8EdgPrzewo8C4u7L/hv0TSKaYA3Jt+XNicksWnIiIiIiLSKlTuIiIiIiLSYhSki4iIiIi0GAXpIiIiIiItRkG6iIiIiEiLUZAuIiIiItJiFKSLiKxiZuZm9omG7zNmNmBmf5N+f7OZvT39+t1m9rblmquIyGqiPukiIqvbOPBCMyu4+yTwz4BjtYPuvg9tsCYisuSUSRcRkXuBn0i/vhX4ZO2Amf0bM/sf008ws+ea2RfM7CEz+wcz25GO/wsz+6qZfcPMvmRmG9PxDWZ2n5k9bGZ7zewZM1ufHvs5M/uamT2SHgsX/RGLiLQ4BekiIvIpki2u24AXAV+dxzl3A//R3V8GvA34o3T8K8AN7v7S9Lr/Xzr+LuB+d78e+CxwJYCZPR/4aeBH3P0lQAT87KV4UCIilzOVu4iIrHLu/i0zu4oki/75893ezDqBfwL8hZnVhvPp563Ap81sM5ADnkrHbwJ+Mr2/L5jZYDr+CuBlwNfTaxWA/ot8SCIilz0F6SIiAknd+e8Cu4F157ltAAylme/p/jvw++6+z8x2A+9Ox63JbWvjH3P3dyxsuiIiK5vKXUREBOAjwJ3u/u3z3dDdR4CnzOynACzx4vRwD+cWnr6p4bSvAP86vf2rgN50/MvAG8ysLz221syec7EPRkTkcqcgXUREcPej7v6BBZzys8CbzeybwKPA69Lxd5OUwfwDcLrh9r8FvMrMHgZeC5wARt39u8A7gb81s28B9wGbL+rBiIisAObuyz0HERFZ4cwsD0TuXjWzG4E/nqVcRkREUE26iIgsjSuBz5hZAJSBX1zm+YiItDRl0kVEREREWoxq0kVEREREWoyCdBERERGRFqMgXURERESkxShIFxERERFpMQrSRURERERazP8PjvCNEw+1FKgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,7))\n",
    "tmp = df[df.Mileage != 0]\n",
    "sns.regplot(x = tmp.Mileage,y = tmp.Price).set(xlabel='Mileage', ylabel='Car Price', title='CMileage vs. Price When Mileage is above 0 ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "384eddc5-29fb-480f-bad1-cf45283007b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['Price','Mileage']].corr(),annot =True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7ae893-15a1-4ea9-81ae-7c57ccce2408",
   "metadata": {},
   "source": [
    "Observation :\n",
    "\n",
    " 1- Mileage and Price showing linear relation here. \n",
    "\n",
    " 2- Price is high when mileage is low.\n",
    " \n",
    " 3- We can see Mileage value hover between 0-200 more. \n",
    " \n",
    " 4- correlation is showing -0.46 we need this feature for our model.\n",
    "\n",
    "Next Step :\n",
    "\n",
    " 1- We many need to understand why my mileage is zero. \n",
    " \n",
    " 2- There is some outlier in Mileage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e1df4c-48b6-4eea-ac35-0c690a08f720",
   "metadata": {},
   "source": [
    "# Price and EngineV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91cd7c26-e432-416f-9d18-a07c28f2678e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "sns.scatterplot(x = np.arange(0,len(df)), y = df.EngineV)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6895c56-8e39-408e-b6a7-087b9bfbf8a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,4))\n",
    "sns.regplot(x = df.EngineV,y = df.Price).set(xlabel='Engine V',title = \"Regression plot between Price and Engine V\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cd61fdd-e6fe-4d32-8859-c920ae7a1d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,4))\n",
    "tmp = df[df.EngineV<7]\n",
    "sns.regplot(x = tmp.EngineV,y = tmp.Price).set(xlabel='Engine V',title =\n",
    "                                               \"Regression plot between Price and Engine V when Engine V is above 7\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6b35f8c-7b5f-4f9c-83fa-5fba252aa011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['Price','EngineV']].corr(),annot = True).set(title = 'co relation between Price and Engine V')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e91117ff-c1fa-46aa-b217-89b10293f6f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.00     709\n",
       "3.00     549\n",
       "2.50     350\n",
       "1.60     318\n",
       "2.20     294\n",
       "        ... \n",
       "30.00      1\n",
       "15.00      1\n",
       "1.79       1\n",
       "0.65       1\n",
       "6.50       1\n",
       "Name: EngineV, Length: 73, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EngineV.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ca665d4-f1d4-4356-8b89-11e37c8615b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4195.000000\n",
       "mean        2.790734\n",
       "std         5.066437\n",
       "min         0.600000\n",
       "25%         1.800000\n",
       "50%         2.200000\n",
       "75%         3.000000\n",
       "max        99.990000\n",
       "Name: EngineV, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EngineV.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692ea1e1-cec5-4fc6-b971-42234ae9b841",
   "metadata": {},
   "source": [
    " Observation \n",
    "\n",
    " 1- Feature having 73 different values, there is no null value. Value range between 0-99. \n",
    "\n",
    " 2- 75% value is in between 0-3, ther is outliers in data. \n",
    " \n",
    " 3- co-relation is very low, Data does't behave like catagorical feature. \n",
    " \n",
    " 4- There is missing value for 150 observation . \n",
    "\n",
    " Next Step:\n",
    " \n",
    " 1- We can think of removing this feature from model building \n",
    " \n",
    " 2- We can attempt two model one without and one with this feature. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d34b8ac-f6a8-46d4-a63e-45ba1648bbd7",
   "metadata": {},
   "source": [
    "# Price and Engine_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65b67544-8d65-45df-b907-e18e9c9258a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df.groupby('Engine_Type').Price.mean().sort_values().reset_index()\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.barplot(x = tmp.Engine_Type,y = tmp.Price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da1e9e59-c541-4d1b-a96b-4bf45a4ca3ab",
   "metadata": {},
   "source": [
    " Observation :\n",
    "\n",
    "1- Engine Type showing can be helpful for predictiong Price fature.\n",
    "\n",
    "Next step:\n",
    "\n",
    "1- Engine Type is not showing any feature merge. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bdf72b4-0123-47a8-8112-8a7c5ab8aa52",
   "metadata": {},
   "source": [
    "# Registration and Price "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01fae259-3099-4876-9c2e-b1af8d2df9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df.groupby(df.Registration).Price.mean().sort_values().reset_index()\n",
    "sns.barplot(x=tmp.Registration,y=tmp.Price).set(title='Registration status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c14275-4802-4563-81bc-7b45f6b61827",
   "metadata": {},
   "source": [
    "observation \n",
    " \n",
    " 1- Need to understand why Registration value is no. \n",
    " \n",
    " 2- For no value average price is value low.\n",
    "\n",
    "Next step :\n",
    "\n",
    "1- Need to know Price range when registration is no."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cdd3ee5-710c-4bff-b912-28f96c14a4cb",
   "metadata": {},
   "source": [
    "# Year and Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ec2b6f5-d456-4059-abe1-3c3853dd966b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = df.groupby('Year').Price.mean().sort_index().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0a09164-332a-4776-abf0-fa273831146c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,7))\n",
    "ax = sns.barplot(x = tmp.Year,y = tmp.Price)\n",
    "plt.xticks(rotation=45)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e537b7ae-d72c-4356-a863-f789a2887907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Price'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FAAAAgKWsWpfPqeILDQkkAAAAAMyTle2ZGcUXGhJIAAAAAJgn7n7abKMFD9ThbGauMVr9AAAAAMAiMJwvjW9Yb5I82Kg+my/V9byiYgYSAAAAAOZJvlCRKHqN+AJGAgkAAAAA8ySRMKUSkoV1rGZSKhHEGwEJJAAAAADMk/UrWyWZ0omEmlIJpRMJSRbGFz4SSAAAAACYJze/983qbE3LElLRXZaQOlvTuvm9b673qUVCAgkAAAAA82Tzxm79X+evVL7oY1//1/krtXljd71PLRISSAAAAACYJ9sefVk7nn9dCZOaUqaESTuef13bHn253qcWCQkkAAAAAMyTOx8/oIRJqURCCUuEt0G8EbAPJAAAAADMk6FcUQm5RgtFuQddWJMWxBsBM5AAAAAAME+aUgnlS5K7JAtu86Ug3gga4ywBAAAAYBFY0RIUgbqC5NEnxRe6xjhLAAAAAJglO/f2avuu/TrYn9W6rlZt2bRhRl1QY41PJLS6Pa3jQ3mVXEqYtLItLUs0xtweCSQAAACAJWPn3l7dumOP0klTZ0tavQMjunXHHm2VIiWBccev62pV78CIzl7eOhbL5grq7miO8a7mT2OkuQAAAAAwC7bv2q900tSaScksuE0nTdt37Z+X8Vs2bVC+6MrmCnIPbvNF15ZNG+K8rXnDDCQAAACAJeNgf1adLekJsZZ0Uof6s5HHJ03af3RQuWJJmWRCq9ozkcdv3titrQoS0UP9Wa09gxLaeiKBBAAAALBklEtIWzPjqdBwvqi1Xa1TjBrXnklq39EhJc2UNFOh6Dp8YkTnr26LfA6bN3Y3TMI4GSWsAAAAAJaMuCWkZhbeqfiqjC9yzEACAAAAWDLilpAOjBbU1Zo6rYvq4Ggh8jnE7QJbTySQAAAAAJaUOCWk7ZmkXj85onQiIbNgL8f+bEHnr26KND5uF9d6o4QVAAAAACKKW8Iat4trvTEDCQAAAAARDYwWtKazWccGc2NdWM9e1hS5hDVuF9d6I4EEAAAAgIjKXVw3rG4fi2VzBXV3NEca39GU0iu9g0omTMmEqVAKurhe0N0+/eAFgBJWAAAAAIgobhdXdw/vVHxVxhe4OU8gzeyrZtZrZi9WxD5rZofN7Lnw6+crHrvFzPaZ2UtmdnVF/B1m9kL42DYLi4zNrMnMvhHGv2tm51WMudHMXgm/bpzr9woAAABgcdu8sVtbr7lY3R3NOjmcV3dHs7Zec3HkBjiDuaLWdDYrlTQV3ZVKmtZ0NmsoV5zjM58d81HCepekP5P0tUnxL7n7FysDZnaRpOslXSzpTZIeNbML3b0o6cuSbpL0pKQHJb1H0kOSPiyp393PN7PrJd0m6YNmtkLSZyT1KMjrnzGzHe7ePzdvEwAAAMBSEKeLa9wS2Hqb8xlId98lqS/i06+VdK+7j7r7AUn7JF1mZudIWubuT3gwt/s1Se+vGHN3eP9+Se8OZyevlvSIu/eFSeMjCpJOAAAAAKiLuCWw9VbPNZC/bmbPhyWuXWFsjaSDFc85FMbWhPcnxyeMcfeCpJOSVk5xLAAAAACoi7glsPVWry6sX5b0OQWlpZ+T9MeSflVju6hM4FPEdYZjJjCzmxSUx+rcc8+d6rwBAAAANLide3u1fdd+HezPal1Xq7Zs2jCvCVycEth6q8sMpLu/4e5Fdy9J+gtJl4UPHZK0ruKpayW9FsbXVolPGGNmKUnLFZTM1jpWtfO5w9173L1n9erVcd4aAAAAgAVs595e3bpjj3oHRtTZklbvwIhu3bFHO/f21vvUGkJdEshwTWPZL0gqd2jdIen6sLPqekkXSHrK3Y9IGjCzd4brGz8k6YGKMeUOq9dJeixcJ/mwpKvMrCsskb0qjAEAAABYorbv2q900tSaScksuE0nTdt37a/3qTWEOS9hNbN7JG2WtMrMDinojLrZzC5VUFL6qqQtkuTue8zsPkk/kFSQ9LGwA6skfVRBR9cWBd1XHwrjX5H0dTPbp2Dm8frwWH1m9jlJT4fP2+ruUZv5AAAAAFiEDvZn1dmSnhBrSSd1qD9bpzNqLHOeQLr7DVXCX5ni+Z+X9Pkq8d2S3lIlPiLpAzWO9VVJX418sgAAAAAWvDhrGMvbaLRmxlOh4XxRa7ta5+p0F5V6dmEFAAAAgBmJu4ZxIWyjse3Rl3XJZx/Wj3/qQV3y2Ye17dGX5+214yKBBAAAANAw4q5h3LyxW9e9fY2ODozqh68P6OjAqK57+5p564q67dGX9afffkWnRgoqllynRgr602+/0jBJZL228QAAAADQgOq9BUbcNYw79/bq/mcPa3VHk85NJzWcL+r+Zw/rkrWdkd9HnJ/B/9i1XyUP9xw0SS6VPIh//MoLIx2jnpiBBAAAABDJQtgCY11Xq4bzxQmxmaxhjDuDGfdnkM2F517etd4mxRe4SAmkmV1oZt82sxfD7y8xs9+b21MDAAAAsJAshC0w4q5hPNifVUs6OSE2kxnM7bv2K18s6vWTI3rpjQG9fnJE+WIx8s/AbGbxhSbqDORfSLpFUl6S3P15hdtlAAAAAFga4iZfs2Hzxm5tveZidXc06+RwXt0dzdp6zcUz6sIaZwbzld4BHRvIqVByJROmQsl1bCCnV3oHIo1fs6xJkuQ+/lUZX+iiroFsdfenbGJaXJiD8wEAAACwQC2ULTA2b+w+43WXWzZt0O/c/30d7h9WoVRSKpFQR3NKn37fRZHG5wolyaREmBuZSSXzIB7BH/zCJfrVu55W5bMTYbwRRJ2BPGZmPy7JJcnMrpN0ZM7OCgAAAMCCsxC2wJgNQVIjmZlk4fcRpZNB4lgqudxdpVIwOpOMVoP6wHOHNDnVLIXxRhA1gfyYpO2SNprZYUm/Kemjc3VSAAAAABaeuOWjC8H2Xfu1vCWtC7o7tPHsZbqgu0PLW9KR1zBeeNYyrWzLKJU0Fd2VSppWtmV0wVnLIo3f8fzrkoKZy/JXZXyhi1TC6u77JV1pZm2SEu4ercAXAAAAwKISp3x0ITjYn1XSpP1HB5UrlpRJJrSqPRN5HeeWTRv0a197WvlwGjFfLCpXKOoL/3e0EtRiOGPpXj2+0EXtwvpfzazT3YfcfcDMuszsD+b65AAAAABgNrVnkjp8YkSFoitppkLRdfjEiNoyyekHS9r+j/vGkseyfCmIR5GoUelaK77QRC1hfa+7nyh/4+79kn5+Ts4IAAAAAObIWGNQq/iqjE/jiQP9M4pP1pSqnoLVii80Uc8yaWZjfWXNrEVSY/SZBQAAAIDQwGhBK1rTyhdLGsmXlC+WtKI1rcHR+dlkYmTy9OU08YUm6jYefyXp22b2lwqaFP2qpLvn7KwAAAAAYA50NKX0yskRpZMJmQVrEfuyeV3QPT/zY+WVjpUTnu4z6wRbT1Gb6Pyhmb0g6d0KJnk/5+4Pz+mZAQAAAMAscw+238gVXK4guUlYEI/i8vVdVctVL1/fFWl8Szqh4XzptCY6LenFVcIqd3/I3X/b3T9J8ggAAACgER0bygXJW3kGMJyFPDaUizT+ni0/o41ntU2IbTyrTfds+ZlI4z/6cz8+o/hCM+UMpJk97u5XmNmAJs6qmiR392ibnQAAAADALNm5t1fbd+3Xwf6s1nW1asumDZG3FskVSrKExrIbk2SJIB71tY+cGg0SonD8kVOj2rm3N9I5XLK2UwlJla+WCOONYMoZSHe/IrztcPdlFV8dJI8AAAAA5tvOvb26dcce9Q6MqLMlrd6BEd26Y4927u2NNN7dVSxJpXDdYcmlYil6CeunH3hRJ4eDhjvlScyTwwV9+oEXI43/5H3f0+RUtRTGG8G0JaxmljCzaD8NAAAAAJhD23ftVzppas2kZBbcppOm7bv2RxpvZkpYsO6xvP4xYdG38TjUPywpSD7LX5Xx6RzPVu/2Wiu+0EybQLp7SdL3zezceTgfAAAAAKjpYH9WLenkhFhLOqlD/dlI49NJUzJhSicSakollE4klEyYMsloCWStecpG6aIaV9RtPM6RtMfMnpI0VA66+zVzclYAAAAAUMW6rlb1DoyoNTOeygzni1rb1Rpp/IVnLdMPj5zUqZGCSh7MPi5rTumCs6Kt0CuvfawWXwqiJpC/P6dnAQAAAAARbNm0Qbfu2KNsrqCWdFLD+aLyRdeWTRsijb98wwo99WpfMAtpwRrIgdGiLt+wItL49qakBkaLVeNLwZQlrGbWbGa/KekDkjZK+j/u/o/lr/k4QQAAAAAo27yxW1uvuVjdHc06OZxXd0eztl5zceQurE/s71N3R0aZZEIllzLJhLo7Mnpif1+k8W9Z06mu1pQS4ZRjwqSu1pTesqbzDN9RY5luBvJuSXlJ/yTpvZIukvSJuT4pAAAAAKhl88buyAnjZAf7s1rZ1qRV7c1jMXePvIayPAO6oq3pjGZAG910CeRF7v6TkmRmX5H01NyfEgAAAADMjbhrKDdv7NZ1h07ozscPaChXVFsmqY9csf6ME9pGM10X1nz5jrs3Rl9ZAAAAAKhhy6YNyhdd2VxB7sHtTGYQd+7t1f3PHtbqjia9+ewOre5o0v3PHo68D2Wjmy6BfKuZnQq/BiRdUr5vZqeivICZfdXMeiv3kjSzFWb2iJm9Et52VTx2i5ntM7OXzOzqivg7zOyF8LFtFm7UYmZNZvaNMP5dMzuvYsyN4Wu8YmY3RvyZAAAAAFik4q6hjLsPZaObsoTV3WejldBdkv5M0tcqYjdL+ra7f8HMbg6//y9mdpGk6yVdLOlNkh41swvdvSjpy5JukvSkpAclvUfSQ5I+LKnf3c83s+sl3Sbpg2a2QtJnJPUo6LT7jJntcPf+WXhPAAAAAOpk26Mvn1ZC+vErL4w8Pu4ays6W9ITYTPahbHTTzUDG5u67JE1uaXStggY9Cm/fXxG/191H3f2ApH2SLjOzcyQtc/cn3N0VJKPvr3Ks+yW9O5ydvFrSI+7eFyaNjyhIOgEAAADU0c69vbrhjid1xW2P6YY7npxR+ee2R1/W7Y/t03C+qFQiWL94+2P7tO3Rl+fwjMet62rVcH7iNh4zWUPZ6OY8gazhLHc/IknhbTn9XyPpYMXzDoWxNeH9yfEJY8J1miclrZziWAAAAADqZOfeXt26Y496B0bU2ZJW78CIbt2xJ3ISeefjB5QwKZVIKGGJ8DaIz4e4aygb3XRdWOebVYn5FPEzHTPxRc1uUlAeq3PPPXf6swQAAAAa1M69vdq+a78O9me1rqtVWzZtmNcOopVrCCWpNZNSNlfQ9l37I53HUC6YeayUsCA+HzZv7NZWBe/jUH9Wa+vwM6yneiWQb5jZOe5+JCxPLf+54ZCkdRXPWyvptTC+tkq8cswhM0tJWq6gZPaQpM2TxuysdjLufoekOySpp6enapIJAAAANLry7F86aRNm/7ZK85YAxV1D2JYJ9l5MVEwXlTyIz5c4aygbXb1KWHdIKndFvVHSAxXx68POquslXSDpqbDMdcDM3hmub/zQpDHlY10n6bFwneTDkq4ys66wy+tVYQwAAABYkhZCB9G4awg/csV6lVwqlEoqeSm8DeKYe3M+A2lm9yiYCVxlZocUdEb9gqT7zOzDkn4k6QOS5O57zOw+ST+QVJD0sbADqyR9VEFH1xYF3VcfCuNfkfR1M9unYObx+vBYfWb2OUlPh8/b6u6Tm/kAAAAAS8ZC6CC6ZdMG3bpjj7K5glrSwWziTNYQlrutxunCGlecMuBV7RkdG8xVjTcCCybrUNbT0+O7d++u92kAAAAAs+6GO55U78DI2PpDScrmCuruaNY9N70z0jFmYw1l+RiNuIZw595e/ce/2q2Rwnge1Zwy/Y//0BPpPfzE7z2kfKGkUkUsISmdSuilP3jv7J/wGTCzZ9y9p9pjC62JDgAAAIA5Enf2b7bWUDbyGsJP3ve9CcmjJI0UXJ+873t65tarIx3DTBPae1q19p8LVL3WQAIAAACYZ5s3dmvrNReru6NZJ4fz6u5o1tZrLo6czC2ENZT1djxbmFF8svZMUsVJRaBFD+KNgBlIAAAAYAmJM/u3ENZQzoZ6bmVycjg/o/hCwwwkAAAAgEjidlBdCMpluL0DIxPKcHfu7Z1+8Cwo1GhBUyu+0JBAAgAAAIhky6YNyhdd2VxB7sHtTNZQzpade3t1wx1P6orbHtMNdzw5o+Rv+679yheLev3kiF56Y0CvnxxRvlhcUmW4cVDCCgAAACCSzRu7tVWqawfVuI18XukdUP9QTiUP+tgUikWNhM2EMD0SSAAAAACR1buDamUjH0lqzaSUzRW0fdf+SOeVzRUnNLFxBU1ssrlizTEYRwkrAAAAgIZxsD+rlvTEjqUzaeQzUiNRrBWf7PL1XTOKLzYkkAAAAADmVZw1jHEb+ZRmGJ/sni0/c1qyePn6Lt2z5WciHqGxkUACAAAAMxAn+UH8LqgLpZHPUkUCCQAAAERU7y0gFoPKNYxmwW06aZG7oG7e2K13nLtcrx7P6sXXTunV41m949zl87Yu84bt39ETB/onxJ440K8btn9nXl6/3kggAQAAgIjiJj8I1jAWiiXtPzqova+f0v6jgyoUS5HXMG579GXteP51JUxqSpkSJu14/nVte/TlOT7zwOTkcbr4YkMCCQAAAEQUt4HLbGj0Etr2TFKHT4yoUHQlzVQoug6fGFFbJjn9YEl3Pn5ACZNSiYQSlghvgzjmHtt4AAAAABGt62pV78DI2BYS0swauEhBArh9134d7M9q3Qz3UYy7B6IUzODd+fgBDeWKassk9ZEr1uvjV144L+cvSWYW3gm/JMkr4tMYyhWVkGu0UJS7ZCYlLYhj7jEDCQAAAEQUt4FL3DWUcUtotz36sm5/bJ+G80WlEkHye/tj+yKXf87GGtCB0YJa06bRQkkj+ZJGCyW1pk2Do4VI45uSCeVLUsmDPRxLLuVLQRxzj58yAAAAENHmjd3aes3F6u5o1snhvLo7mrX1mosjz8DFTQDjltDGLf+clTWgpZJOjU7cNOPUaEleiraRRq08kfxxflDCCgAAAMzA5o3dZ9zx82B/Vp0t6QmxmSSAcUtoh3LBzGOlxAzKP+OevyT1DVefaawVn2xwtPq51opjdpGnAwAAAPNkXVerhvMTE52ZJIBxS2jbMkmVfGKs5IrcwCbu+UtStkayWis+mc8wPttqrdSMtoKz8ZFAAgAAAPMkbgK4eWO3rnv7Gh0dGNUPXx/Q0YFRXff2NZFnRD9yxXqVXCqUSip5KbwN4vNx/otBW6Z6ClUrvtgsjXcJAAAALABx11Du3Nur+589rNUdTXrz2R1a3dGk+589HLmJzcevvFCfeNf5akknVSgF5aefeNf5kbuwxk1gF4NsrvpazVrxxYY1kAAAAEAdnEnJZWUTG0lqzaSUzRW0fdf+yEncx6+8cEbbdlSqTGDPTSc1nC/q/mcP65K1nZFfP5kwFSfX0Ybx+ZA0qVjlh5+M+PK10sSlkT4yAwkAAADMm7jbYMTtwhrXbHRhTdVIFGvFZ9sF3e2nJUGJMI7pkUACAAAA8yRuAjYbTWx27u3VDXc8qStue0w33PHkjPZwnI0ENl+sPldXKz7bbn7vm0/b8iOZCOJRZGpMVdaKLzaUsAIAAADz5GB/VqP5gg4cG1LJgy00VrallStES562bNqg377/+zp8YljFkiuZMLU3pfTp910UaXx5BjSdtAkzoFulSCWo67padeDYoAZGCsoVS8okE+poTmn9quizd1WqV6eMz7YHnjuk/KQfd74UxKP8DJa1pHVsMFc1vhQwAwkAAADMl1JJRwfzY8lSyaWjg3l5Kfrsm0mSS+4u+cy2j4g7A3r5hhV649Sosrli2I21qDdOjeryDStmcBb19cD3j8woPtnJbH5G8cWGBBIAAACYJ0eHqicZteKTbd+1X8ta0rrgrA69+ZzluuCsDi1rSUdOAOOWoD74whFNXqqYsCDeKOLOgBa8+hNrxRebuiaQZvaqmb1gZs+Z2e4wtsLMHjGzV8Lbrorn32Jm+8zsJTO7uiL+jvA4+8xsm5lZGG8ys2+E8e+a2Xnz/iYBAACA0GiNUtVa8ckO9mdVKJa0/+ig9r5+SvuPDqpQLEVOAOOuoTxwPHgds2Dm02xifCmolScukfxxQcxA/it3v9Tde8Lvb5b0bXe/QNK3w+9lZhdJul7SxZLeI+nPzaz855MvS7pJ0gXh13vC+Icl9bv7+ZK+JOm2eXg/AAAAWMDiNJGRpN+691n9+Kce1Hk3/71+/FMP6rfufXaOzvR0HU0pHT4xokK4/rFQch0+MaL2pmitTbZs2hCWnhbkHtzmi64tmzZEGl8slVT0YLbOFdwWPYhjaVgICeRk10q6O7x/t6T3V8TvdfdRdz8gaZ+ky8zsHEnL3P0Jd3dJX5s0pnys+yW9uzw7CQAAgKUn7jYav3Xvs/rmc0fG9jEsllzffO5I5CSy1ofvqB/KvTzN5RVflfFpbN7YrevevkZHB0b1w9cHdHRgVNe9fU3kPRwRX63dSuZpF5PY6p1AuqT/z8yeMbObwthZ7n5EksLb8tW8RtLBirGHwtia8P7k+IQx7l6QdFLSyjl4HwAAAGgAcZvI7Hj+dUlhCaeNl3CW49P5ibM7ZhSfbDBX1JrOZqWSpqK7UknTms5mDeWK0w9WkEDf/+xhre5o0pvP7tDqjibd/+zhyAl0rUrbiBW4UP270MZV7208ftbdXzOzbkmPmNneKZ5bLSf3KeJTjZl44CB5vUmSzj333KnPGAAAAA3rYH9WnZO2W5hJE5lijU/5teKTvfctZ+ulNwYmJAsJC+JRrOtqVe/AiDasHt82I5srqLujOdL47bv2a3AkrxPD+bFtRDrDJjzMQkaTtKBst1p8KajrDKS7vxbe9kr6pqTLJL0RlqUqvC3/OeSQpHUVw9dKei2Mr60SnzDGzFKSlkvqq3Ied7h7j7v3rF69enbeHAAAABacuE1kkjXqDGvFJ3tif5/OWtaktkxS6aSpLZPUWcua9MT+0z6iVrVl0wadHM7rld4B7X39lF7pHdDJ4XzkNYx7XjupvuzEbUT6snntee1kpPGQLuhum1F8salbAmlmbWbWUb4v6SpJL0raIenG8Gk3SnogvL9D0vVhZ9X1CprlPBWWuQ6Y2TvD9Y0fmjSmfKzrJD3mUQvEAQAAsOjEbSJzzSXBTKH7+FdlfDoH+7Na2dakDavbtfHsZdqwul0r25oiz4BKUr5Y0mi+pHzRw9vo9aMDI4UZxXG6l3uHZhRfbOpZwnqWpG+GPW1Skv7G3b9lZk9Lus/MPizpR5I+IEnuvsfM7pP0A0kFSR9z9/Kfjz4q6S5JLZIeCr8k6SuSvm5m+xTMPF4/H28MAAAAC9Pmjd3aqqCU81B/Vmu7WrVl04bI5Ztfuv7tkp7VjudfVzHshHrNJWeH8emVS1BbM+Mfw2cyA3rbt/ZqaLSoTCohsyCBHRot6rZv7Y30HmrNpDDDEl2jr2GMq24JpLvvl/TWKvHjkt5dY8znJX2+Sny3pLdUiY8oTEABAAAQNFHZvmu/DvZntW6GydNic6af96+9dK1eP5Ub+xlee+na6QeFtmzaoP/0N88qW9H0pjWT1Kffd1Gk8fuPDalYchUqshUL48B8qHcXVgAAAMyTuFtYLAZxfwZxxz/w3KEJyaMkZXNFPfDcoRojJsoVS6clvi7NqIwViIMEEgAAYImIu4XFbNi5t1c33PGkrrjtMd1wx5PznrzG/RnEHf/N547MKD5ZrW4eS6V8EvVX7208MA3KTAAAQKU4nw3ibmERV3n2Lp20CbN3W6XI7yHuZ6O4P4N6/wzROEbzRR0fHNXRwZyOD43q+GBOfUO5ep9WbCSQC9hs/JIFAACLR9zPBnEbuMRVOXsnSa2ZlLK5QuQ9CGfjs1Hcn8G6rlYdODaogZGCcsWSMsmEOppTWr+qffrBaFjuroHRgo4NjE75vPdt+yedyOZ1YjinodHilM9tVCSQC1jcX7IAAGBxifvZYMumDbp1xx5lcwW1pJMazhdntIWFVN8Z0Nn4bBT3Z3D5hhV66tU+JUxKWLAm8ehgTr942YpI47Gw3fbQXvVlg5nC/qGcTmTz6s/mdHI4P6FxUS17XjtVNW6SlrWktbwlrR/1NfZsNQnkAkaJBAAAqBT3s8Hmjd267tAJ3fn4AQ3limrLJPWRK9bPqHy0njOgs/HZKO7P4In9feruyOjU8PgM5LKWlJ7Y36ePRz4LzJXv/Uu/jg6O6vhQTscHR4NEMEwI+4byOpGduoT0y//4z7Fe/2Obf1xdbRmtbG/Syra0VrQ1aVVHRitbm5ROBe1nzrv572O9Rr2RQC5g9S4zAQAsLKyLR9zPBjv39ur+Zw9rdUeTzg1n3+5/9rAuWdsZ6Vravmu/Tg3ndGqkoJIHM3DLmlPzNgM6G5+N4v4MDvZnVSy6hvNFlVwqloIklD/wz55CsaT+cObv+OCojg5UJoT5Kcf+wpe/E+u1L1m7XF2tGa1oy6irNa2V7U1a0ZbRyraMVrZntKqtST/3xZ01x//OezbGev1GQAK5gM1GmQkAYHFgXTyk+J8N4paAvnC4X4Oj49tFlFw6MVzQi4f7I73+5o3d2hqex6H+rNbO8A8hWzZt0O/c/30d7h9WoVRSKhGsP4y6h6IU/2dgknoHx2exSh58v66rJfI5LCXurmyuGM4A5tQ3NPUawks++7BOjRRivWYmmVBna1qdrWl1tWbGE8K2ICHc+r9/UHPsjl+/ItZrLwUkkAtY3F+yAIDFg3XxkOJ/NohbAjqcD9aAmY3H3KVsPvoeEps3dse6ZkfzReWKJZVcKnlJo/mZNSqJ34V1eEbxpeTT/++LQZKYDdYP9g0FawdHC9H3qKyWPC5rTqmzNaPO1rRWtGa08+WjNcfv+f2r1ZpJyiov0kmmSiAxPRLIBS7uL1kAwOLAuvjFYTbKkON8NohbAurhJoST9yL0WpsTVrHt0ZdPW3/48SsvjDT2tm/t1amKzpYll06NFnXbt/bOqAvrS6+f0onh/FgZbmdLWj9x9rLI72Gx2vlSr/qzOR0bCJK/Y0Oj6hscTwj7s1OXj379yX+Z9jXSSVO+WPt6+e83vE2r2jNa0RaUjna2ppVOTty6fqo1hG1NpDdzjZ8wAAANYLbWfrGGsn4WQhly3BLY5nRS2dzpM37N6WSk8dsefVm3P7ZPCZNSieAavv2xfZIUKYl86fWBGcWrOXtZRk/sH0+ESi71ZfM6e1km8jEWqlLJdXI4r+Nj5aK5sSYyRwdGp92D8Jf/8ulYr/9zF67SirYmrWzLaEW4XnBVx3gJ6Yq2jNqbUlp/y4M1j/Fv3vqmWOeAuUcCCQBAA4j7wX8hJC9L3UIoQ45bAruiJVU1gVzREu0j5Z2PHwiTx2BGKWFSoVTSnY8fiJRA1iqEjF4gKf3d80dqxr90/QwONA9G8kUdD7eTKN9O5fzffVARdpqoKZUwdbWm1dU2MekrN5BZ0dak37jnezXH3/2rP33mL46GQQIJAEADiPvBfyEkL0vdQilDjrU8JpFQS0oarlim1pKSLJGoPabCUK4ok2u0UJR7sJYyYUF8vuRrZJu14rOlPDs4lV/6yneDtYPhthMjMzypyclje1MqWDcYJoIrWjP62+8drjn+lc+/d8q1g5KmTCCxNJBAAgDQIOJ88F8oyUu91bOMd11Xqw4cG9TAyPj+gR3NKa1f1T6j48R9D3HWIKpUmpA8SkEy6aVoiU4maWONeKRgLWXJpZb01ElLo/jL/3NgrGz0+GB4G5aQnszmVZxmreg/vXKs5mNJM3W2pnV8ilnIv/7IT6urNZgt7GxNqyl1emnxVAnkdMkjIJFAAgCwJLC38OyU8cZJ3i7fsEJPvdqnRDjrliuWdHQwp1+8bMW8vYe4axD7JmeP08Qbzf6jg+Gm83n1DY2O3R6vSAqn8vsxu3vetGlDkAC2ZdTVNl5CuqIto2XNKZnZlA1kfvb8VbFeH4iCBBIAgAYRJ3lhb+H4Zbw79/bqd+7/vgZGCiqUSjo2MKrfuf/7+qPr3hpp/BP7+9TdkdGp4fEZyGUtKT2xv08fn8F7yBeLOj448RhR30PcNYjV1j9OFZ9suEZJZq34mRotFNU/lB+fDRwaHdtWYirv+uN/jPW6F3S3qytcM7ii4jaINamrLa33bXu85vhP/fybY70+MB9IIAEAaABxZ55mY2/hhdDFNc45xC3jve1be9U3lFPJJZdULJWUH8pF3kLiYH9WhWJJw/miSi4VS0W1ZhIzKiN+pXdAJ7N5JRKmZMJUKLmODeSUL0brQjqUKyo1abnifK9BnCl318BoQX3TzP5d+2ePj60dHBqN935aM0l1tabHtpKoTAT/6OGXao575D//XKzXBRoBCSQAAA1gNprgxFlDuRC6uMY9h7hlvPt6B1V0yRQ0f5FLRQ/ikZRKOjo4cfuIo4N5rV0erQGNJOUKJRVLrnxFt5REGI+iLZMMN2qfmGAta67/R8IvPfKy+rO507qO9mdzU+4bWPb9QyerxhMmdVZ0FH3qQF/NY3zn5ndpRVtmym1JpkogsTS0ZxIazJ3+/1x7Jvr/y42s/r8tAADAtOrdBGchdHHdvmu/coWJ5ZsdzdHLN+OW8RbKDVDKfUbCJLIwTWOUsqND1Ttw1opXM1oonLZlRUlSrhBtDWKpRrObWvGZ+N6P+oMEMGwe05fNqW8wN5YUTlc+evu3X4n1+r9z9U9MWDNY7jq6vCWtRGK8OcxUawjf1NkS6xywNHS2ZjSYG6kaXwpIIAEAaAD1boIzWwlsnA6gL79xSqdGCkrIlDRToeg6PpRToXgq0vjNG7vV+tAPtPeNobHYxrPaIifASZMKHnQOnRyPYrTGLGGtePXnVo+PROxhU23WpDKeL5bCJjIVm9BH3IPwF/78O9FOooafOq9rQvI3cR/CJq1oz+hnv/BYzfEf+1fnx3p9IKojJ09PHqeKLzYkkAAANIB6N8FZ19WqV48PntYA5ryV0begiNsBNF/0sJRxYgaXi1DeKEk3bP/OhORRkva+MaQbtn9H92z5mWnHn93RpEMnR6vGFyJ311CuqL7B8r6Cp597pUs++3BY3nrmmlKJ02YBy11FV7Rn9LvffLHm2P/5H6f/bwAsBLV+5UT8VdTwSCABzIuF0Hyj0fEzXNrq3QSn2hYUvQM53fBT0beguPPxAyqWXJPbm0TtADpQI7mpFZ/siQP9M4pPdrhK8jhVfLYVilPPVP7GPd8LZwtHg5nDbD7y2khJVZPHzpb0hO0kHvnBGzXH/2Dr1WpJJ6fcS3CqBBJAYyCBBDDnFkLzjUYXd/uAxWAhJNCxNmCfBXGb4Nz0tadVrmA81D+sZ149rjs+9FORt6ColvzNZAuKWrNbUWe9av1xf77+6D+Xr/8PL/WObTMxvvXEePlo31BOJ4enXiv5v7//2pSPZ1KJKRPKL//7t09IFjtb0kolJzYFmWr9YGV5NYDFi//TgSWg3h+8F0LzjUYXd/sAqf7XQZzXn40/QsRN/rY9+rL+5NHxJh+nRgpj30c9zltufWjCGrT2TEIvbn1v5HM4/5a/V6EiW0mZtO+/vS/S2N+45xlNXv6WKwXxF35/+nN4Yv/xGcWXmmLJdSKbG2siM9Y4Zqx8dOr1g7/yl0/HPod/17M23HYi2H5i5dj+g8FtWyap9bc8WHP8e3/ynNjnAGDxI4EEFrnZ+OAdN/God/fI2VDv5Ku8fUCZa2bbB+zc26uP/tVuDYfZx6H+YT33oz59+T/0RH4fN2z/zoRSv8vXd0VaN1Z+/V++a/wD8qH+YT2x/7ju+uVos1/bd+3Xj/pOv16i/hFiNpK/yvGT41GOMTl5lILGJW+59aFISeTk5FEKGrqcf8vfR0oiB0arzzzVii91w7niWBfRKOsHz//dB09rrjMTqaSNrRUM1gs2acXYPoTj+xHe8BdP1jzGH1731jM/AQCRrWpL61iV7smr2tJVnn26jWe1a+8bp//7vfGs6GvK64kEEljktu/ar3xxYtv7ZS3R297PRulkvbtHSvFnv+L+DOLOfhVK1T+Z1opP9sn7nhtLHsuGC65P3vecnrn1qmnHT04epWDdWNTmI79yV/XZlV+562kd+ML0yU/c2a+4yd9smK775XQmJ4/TxTGuVHKdmKb885e+8t2x0tET2byG8zPbiH5y8tjRnNKK1nAGsD3YTuJ/PnOo5vhX/uC9U64dBLBwfPEDl+o//fUzyubHf3+3phP64gcujTT+5ve+Wb99//c1OFpQseRKJkztTSnd/N43z9EZz64lkUCa2Xsk3S4pKelOd/9CnU8JmDev9A7oZDavRMKUTJgKJdexgZzyxYFI42ejdHLLpg36+D3PajBXVMmDBhztmaQ+/b6LYryz6OLOwt72rb3qz+aVTJhSyYTcpf5sPvLPYNujL+tLYQLjChp+fGmGs19x114dz1b/8FwrPlnc5iP1XruGxe+rjx8Iy0dHx9YMlhvJnMgGv8Om8k+vHKv5WCph6mrL6OhA7VnIv/nIT4+Vi3a2ZpRJnb6h+FQJJMkjMH+SVr1jatQteTZv7Naf//t3nHFTs80bu/XF694aqylaPS36BNLMkpL+H0n/WtIhSU+b2Q53/0F9zwyYH7lCSTIpEX44MZNK5pE785VLJy0cK59Z6aQkPX/oxFjyKEkllwZzRT1/6MS8/LKMuwZz/7GhsPPk+M/QzbX/2NA0IwNf/sd/npAoeUV8PhuwAAvZPx8dnLDfYDkJPD44OlZKOpWtfxfvn/WbNm0Y23h+RbjlxIrW4LajKSUzm7KBzM+cvyrW6wNRrWrP6FiV/x9WtUfbxD6TMOWq/EUlk1g6f8S4bP1KPfXqcVU2Nk4mpMvOWxn5GHGams3G+Hpa9AmkpMsk7XP3/ZJkZvdKulYSCSSWhHTSNJwPSrjMxsusMhH/zFYoDyg/PUwiCzNY7HPn4weUTJiaEuN/kS+USpFb98dV7zWYw2GJS+UEg/t4HFhMRgvFCZ1Ex76mSQDf/cf/GOt1L+huH5sBXNGW0cryGsL2prGk8Oe3/VPN8Z/6+cYoHUPjSyekar/+06dPWlf1xeveqv/0N88qmxsvs27NJPXFiGtgf/1d51ct6//1d50fafxdv/xTE9a0V8ajunzDyqpLEC7fEC2BW9fVooP9w1XjUWzZtEGHTwwrnbS67Kvb6JZCArlG0sGK7w9J+unKJ5jZTZJukqRzzz13/s4MmAcXnrVMB44NamBkfA1kR3Na61dFW6idtGCN1eR8MWqZhyQN5YqaXM2VsCA+H+KuwVy/slX7jg7JKpLwkkvnr4o2Psy5q8aBhaxUcg2MFHR8mgYy/+a//5P6hvLqz+YmfKg9E62ZpLpaM+pqS2tFa5NWtgcNZFa1N+m2b+2tOe6R//xzsV4XjSFu8jUbx4ibvPSct1LP/stxjVb8r9KUlN7+Y9GSp80bu/Xnv/j2My5/LP/h9kzX5W/e2K27fvmnYpVfxk3gPnftW/SJb3xPp4YLcgX/ni5rSelz174l8nuIu6/uUrYUEshqn9EmfJZz9zsk3SFJPT09LMnBorJl0wbdumOPzl6eOqNf0uevbtfLbwzKpbFf0hbGo2rLBK9bWR1T8iA+H8o/g2yucEY/g2qL3Tub0pEXu68tf9jw0+NRpcL1q9XimN7l67uqrte8fH1X5GM0JTXhA19lPIr2TKJqw5z2TLRPrbPxV/9a/ujhveofyk/YgL4/bCZTjFBt8MLhU1XjCZM6WzPqbE1rRWtGu/+l9prZJ255l7paM2pO1/6BTpVAIpqUVW+8lIr4q2Tt8iYdOnn6HxTWLm+KNP4XLj1H33zuSNV4FH/xoZ/Sr9z19IRfpxbGo4p7jLjJS/BvUrzZr7jljx+/8sJYFUCzUb4ZJ4HbvLFbt3/wbbESwEYuIa038zg9pxuAmV0u6bPufnX4/S2S5O7/rdrze3p6fPfu3fN4hsDcK3cgPZNfsjv39lbtFPbFGXYgvf2xfeE6wiB5LLn0iXedP29rAOP8DOKO37m3t+qHjds/+LYZHeNX7356QiOQhElfvTHaNhi/de+zNT+0fen6t087fvI2GGX/+coLIv03jDt+8jYgZVG3AZHibUNS9hO/+/enzRq89Plo+zDu3NurX/va0xNmPtKJ4EPrTLfUOdSf1ZrOFn3o8h/TRW9arr5sTscHRnW0Yr1geSP6E9m8+oZyeuPUSOSuvbVMnk1vSSf0wZ86VyvbM1rV1qQV7eN7Dq5ozWh5S1qJSX/kqLaO8NUInXil2bkO4rz+QriOpXjvQYq3n6gkXfHfHp2QRK5d3qTHb7ky8vjfuvdZ7Xj+9bF/U6655OxIv4fK4v4+n41j1Hs8MNfM7Bl376n62BJIIFOSXpb0bkmHJT0t6RfdfU+155NAAqebjX/o4m5j0egWwgeeuB/a4v43jDt+MXzgmuo95AolnQg3nz82OKpjg8Heg0EjmfGOov3ZnE5kg8QwbkKYTprWdrUGM4QVDWRWtme0qr1JK9ubxtYUrmibenZwvtT7OuA6BrAULOkEUpLM7Ocl/amCbTy+6u6fr/VcEkgAQFzursHRgo6HieCxwSARPDaWCI7fnhjK68RwXoOjhViv2ZRKqCssF+1qy6irNaMVrekgCWzPaFXYWGZle7Ah/fKWtJKUQAMAqpgqgVwKayDl7g9KerDe5wEAaEyFYknHB3M6PhSUiZZnBI8PjieDJ8LZwf5sTieH88pX22QsoqDMOa3O1vTY+sEJHUbbgqRwdUfT2Pct87SmGACwtC2JBBIAgDJ316mRgo4NlGcFR8fWC/aXN58fzql/KNiA/sRwXgMj8WYHM6mEOsOEsKs1nB1sn1Qy2takVe0ZrepoUmdrhtlBAMCCRAIJAGhY7q580XV8cHRs7eDxoZz6wtv+bJAI9mVzOpkdTwjjzA5KUkdzKkwIg+0muloqNp9vC5PBjjAhbG9SayYpMxJCAEDjI4EEACwI7q5iyTUwmtfxwfz4zODgaMW6wSAJ7M/mdWI4SAoHRgpV99mMKp00LQ+TwbFy0dbxxjEr2tJhQ5mwsUxbRukU5aIAgKWJBBIAMOtKJVfRfayz6LHBUfUN5nVsaLSigcz4XoP9YTJ4cjiv0UKVHb5noKM5FSSElTOEYVK4MtxmorLL6PLmlBKJGexCDgDAEkYCCQCYUrEUzAyW3DU4UlDf0GgwQzg0OrGj6FBQHnoiTARPZHOxZwdTCdPy1rSWt6TV1ZLW8vLMYNhpdGW492C5w+iKtoxa0snT9h4EAACzgwQSAJYId1fJNZYM5gsl9Q8HnUTLHUb7w03ny2WiJ7PjSeGp4bxGYs4OtmWSWt4azA4ubwlmBzvDpLBcNrqqPUgGV7c1qaMlqXQySUMZAAAWCBJIAGhA5fWCRXeVSlLRXdlcQX1hIhh0FB1PBseayAwHs4Mns3mdGskrzj70yYSNlYqObTkRJoUrKrqMrmpv0oq2YD/C5nRSqYQpmTCaygAA0IBIIAGgzsrrBcszg8WSq1B0nRjOq29oVH2DQQJYuW7w5PDEZPDkcF7D+WKs82jNJLW8JR02lAnLRivWEI7tQRg2lFnenFYqmVAyYUqaUTYKAMASQAIJALOocr1g+bZUkobzxaCbaDbcfL6cDGZzQTfRMBE8MRyUip4cjjc7mDBNSgYzYUI43lhmZVtTuOVERivag43okxbMDpa/AAAAKpFAAkAVPpYAaiwZDMpFXYViSSdHgq0m+rM59Q2WN54PE8HhchOZ8fvZXLzZwZZ0ODsYzgyOdxlNT1g/2NWW0er2Ji1vSSuVrEgGjbJRAAAQHwkkgEWvskTUXePlomF8NF9UX7jZfN/QqPqHgsSwclbw5KSvYozpwfLs4LKWcjOZ8cSwszUTbkpf7jIaJIZtTSklEqZUwpQwo2wUAADUBQkkgIZSWSI6ViZaGk8Ki6WSBkYKQUfRMCk8EXYTnbBusOJraDTe7GBzOjFhVnBZxRrC5eVksHV8/eDylrQyyYQS4ezghKSQhBAAACxgJJAA6mJyF9GSj5eIVsZHC0X1DwVNZMaax5T3GQzXC05uJlOIMTto0tjM4LJJDWUq1xOuDMtFV4azg+MzglIqkVAiIcpGAQDAokMCCSC2yV1Ex/YarFg3WPRg7eDQaHFs8/nJ6wSrfT84Woh1bplUYrxMtEoyuKzcabQ1mCnsbM0ok0qMJX+UjQIAAIwjgQQwQa0uopO3mSiWXPliSf3ZvE5kc5MSwJxODhd0IpubOEM4nFe+GG92sKM5NSH5K5eNLm/NjN9vGW8s096cChLAiuSvvA8hZaMAAAAzQwIJLFLVNpovVSkRHS8bDWYHy1tKnMhWLw+tTAYHRuLNDqaTps6WjJa3VmkmUzFruKwl2Ji+sy2tTCp52oxgMjlePkrZKAAAwNwhgQQaQLWN5k9bNzipoUyhWNKpkfzpnUQnJ4Jj3+dizQ5K47ODExLA1tOby3S2ZNTZmlZbJqVk0k5bM5ismCEszxaSEAIAANQfCSQwj7xyfeCkvQVPi1esJ3R3jeSLQdJ3WgIYlosOh+Wi2fHZwTjpYDppYeKX0fKWVJj8Zap0GR3/yqQSQdKXnFguWi4frUwKWUcIAADQeEgggTNUrUS03DhmfGZQEzqLljxI6Yol1+BIYbxctNxNdIqGMqOFUqzzbW9KVW0kU6u5TEs6qWQicdqMYOX3lV1HWUcIAACw+JFAAtJpTWMm7y3ofnqJqPv4/N5oeXZwim6ild8PjOQVY6cJpRJWda1g5TYTy1tS6gwbyyxrTimVTMis+prBsRLSisSQslEAAABMRgKJRWWqEtHyfffTO41WKnl5dnByuWj1bSdOZvMaiTk72NaUnLhOMEwAJ3cWLTebac0kZWZjCeFY8lcxQ5iY9D3bTwAAACAuEkgsWFOViI53FK1eIlopVyiFiV9u4oxglaYyp8JYnNnBZMImJYMTk79qjWXSycSE8eVS0cnbT4zF2H4CAAAAdUACiXkRt0S0rOSuodHChFnA00pFJyWGw/lirHNvzSRPKxFd1lzuJjqxoUxnS0ZtTckJpZ/lZK/ajCDbTwAAAKCRkEBiRqYrES25TusiOrlEtFJ5dnDyWsGJm8/nJiSJcWYHE6aJzWNaK8pFWzJVG8xkUokJx6gsG51q+4nybCEJIQAAABYLEsglLE4X0VrHm3Ij+iqzhdlcvNnB5nQiTAAnlYjW6Cza1pRSokpCd3pn0dO3n2AdIQAAAJa6uiSQZvZZSb8m6WgY+pS7Pxg+doukD0sqSvq4uz8cxt8h6S5JLZIelPQJd3cza5L0NUnvkHRc0gfd/dVwzI2Sfi98jT9w97vn/M3VSa2N5sfXCkYrEa2UL5YmNIqZat1gOT7VbON0EiYta564RrAy+TttTWFLWk3pZI1jVd+QPlFZPppgHSEAAAAwE/WcgfySu3+xMmBmF0m6XtLFkt4k6VEzu9Ddi5K+LOkmSU8qSCDfI+khBclmv7ufb2bXS7pN0gfNbIWkz0jqkeSSnjGzHe7ePz9vb3aN5IsaGi3MuES0zN01lCvWSAYnbUQfPmdoFmYHT9+Ivpz8hbOGLang8da02ptSNZO5attPpBKJiR1I2X4CAAAAmFMLrYT1Wkn3uvuopANmtk/SZWb2qqRl7v6EJJnZ1yS9X0ECea2kz4bj75f0ZxZkD1dLesTd+8IxjyhIOu+Zt3czi4bD5K+sUCzp1EhhQmfREzVmBcsJYyHG7KBJWlajNLTWxvTNNWYHy8ZmBpOUjQIAAACNoJ4J5K+b2Yck7Zb0yXBmcI2CGcayQ2EsH96fHFd4e1CS3L1gZiclrayMVxnTUL76+AHtfKlXxwbHk8XB0UKsY2ZSiQmloJ2tp285Ubn/YEdzetpSz8puo5O3mhhPBMOZQxJCAAAAoOHMWQJpZo9KOrvKQ7+roBz1cwpKSz8n6Y8l/aqCia7JfIq4znDM5HO9SUF5rM4999xqT6mrF187qV2vHJvyOcuaUxM6i07cfD4zoVR0eUtaLdPMDkq1u42O3a9YW0jZKAAAALD4zVkC6e5XRnmemf2FpL8Lvz0kaV3Fw2slvRbG11aJV445ZGYpScsl9YXxzZPG7KxxrndIukOSenp6YmwSMTc2XbBaTamEmtPJSYlhcLsswuxgGd1GAQAAAJypenVhPcfdj4Tf/oKkF8P7OyT9jZn9iYImOhdIesrdi2Y2YGbvlPRdSR+S9N8rxtwo6QlJ10l6LOzO+rCk/2pmXeHzrpJ0y1y/t7nw/ret0c9duFr92dxpjyUnlYpO7jY6uZQUAAAAAM5UvdZA/qGZXaqgpPRVSVskyd33mNl9kn4gqSDpY2EHVkn6qMa38Xgo/JKkr0j6ethwp09BF1e5e5+ZfU7S0+HztpYb6jSilkxSqWTTaRvXUzYKAAAAYL7YdHsBLjU9PT2+e/fuep8GAAAAANSFmT3j7j3VHkvM98kAAAAAABoTCSQAAAAAIBISSAAAAABAJCSQAAAAAIBISCABAAAAAJGQQAIAAAAAIiGBBAAAAABEQgIJAAAAAIiEBBIAAAAAEAkJJAAAAAAgEhJIAAAAAEAk5u71PocFxcyOSvqXep/HHFkl6Vi9TwJLHtch6o1rEAsB1yEWAq5D1PJj7r662gMkkEuIme129556nweWNq5D1BvXIBYCrkMsBFyHOBOUsAIAAAAAIiGBBAAAAABEQgK5tNxR7xMAxHWI+uMaxELAdYiFgOsQM8YaSAAAAABAJMxAAgAAAAAiIYFsYGb2VTPrNbMXK2JvNbMnzOwFM/vfZrYsjP97M3uu4qtkZpeGj70jfP4+M9tmZlant4QGNMPrMG1md4fxH5rZLRVjuA5xxmZ4HWbM7C/D+PfNbHPFGK5DnBEzW2dm/xD+bttjZp8I4yvM7BEzeyW87aoYc0t4rb1kZldXxLkOcUZmeh2a2crw+YNm9meTjsV1iKpIIBvbXZLeMyl2p6Sb3f0nJX1T0u9Ikrv/tbtf6u6XSvolSa+6+3PhmC9LuknSBeHX5GMCU7lLEa9DSR+Q1BTG3yFpi5mdFz7GdYg47lL06/DXJCmM/2tJf2xm5X8PuQ5xpgqSPunub5b0TkkfM7OLJN0s6dvufoGkb4ffK3zsekkXK7jO/tzMkuGxuA5xpmZ0HUoakfRpSb9d5Vhch6iKBLKBufsuSX2Twj8haVd4/xFJ/7bK0Bsk3SNJZnaOpGXu/oQHC2K/Jun9c3LCWJRmeB26pDYzS0lqkZSTdIrrEHHN8Dq8SMEHKLl7r6QTknq4DhGHux9x92fD+wOSfihpjaRrJd0dPu1ujV9T10q6191H3f2ApH2SLuM6RBwzvQ7dfcjdH1eQSI7hOsRUSCAXnxclXRPe/4CkdVWe80GFCaSCXyqHKh47FMaAOGpdh/dLGpJ0RNKPJH3R3fvEdYi5Ues6/L6ka80sZWbrFcyGrxPXIWZJWFnxNknflXSWux+Rgg/3krrDp62RdLBiWPl64zrErIh4HdbCdYiaSCAXn19VUK7wjKQOBTM8Y8zspyVl3b28TqhaPTuteRFXrevwMklFSW+StF7SJ81sg7gOMTdqXYdfVfBhaLekP5X0HQVlX1yHiM3M2iX9L0m/6e6npnpqlZhPEQcim8F1WPMQVWJch5Akpep9Aphd7r5X0lWSZGYXSnrfpKdcr/HZRyn4ELW24vu1kl6by3PE4jfFdfiLkr7l7nlJvWb2fyT1SPoncR1iltW6Dt29IOm3ys8zs+9IekVSv7gOEYOZpRV8aP9rd//bMPyGmZ3j7kfCssDeMH5IE6uEytcb/y4jlhleh7VwHaImZiAXGTPrDm8Tkn5P0v+oeCyhoIzr3nIsLGMYMLN3ht21PiTpgXk9aSw6U1yHP5L0Lgu0KVjgv5frEHOh1nVoZq3h9Scz+9eSCu7+A65DxBFeM1+R9EN3/5OKh3ZIujG8f6PGr6kdkq43s6awlPoCSU9xHSKOM7gOq+I6xFQsWBeLRmRm90jaLGmVpDckfUZSu6SPhU/5W0m3hIufFbaq/4K7v3PScXoUdDBskfSQpN9wLgxENJPrMCyp+UsFTUxM0l+6+x+Fx+E6xBmb4XV4nqSHJZUkHZb0YXf/l/A4XIc4I2Z2hYJqihcUXFuS9CkF68/uk3Sugj+ifSBc+y0z+10FpdYFBaWGD4VxrkOckTO8Dl+VtExSRkFTsavc/Qdch6iFBBIAAAAAEAklrAAAAACASEggAQAAAACRkEACAAAAACIhgQQAAAAAREICCQAAAACIhAQSAIB5EO5/+riZvbci9u/M7Fv1PC8AAGaCbTwAAJgnZvYWSf9T0tskJSU9J+k97v7PZ3CspLsXZ/cMAQCYGgkkAADzyMz+UNKQpLbw9sck/aSklKTPuvsDZnaepK+Hz5GkX3f375jZZkmfkXRE0qXuftH8nj0AYKkjgQQAYB6ZWZukZyXlJP2dpD3u/ldm1inpKQWzky6p5O4jZnaBpHvcvSdMIP9e0lvc/UA9zh8AsLSl6n0CAAAsJe4+ZGbfkDQo6d9J+jdm9tvhw82SzpX0mqQ/M7NLJRUlXVhxiKdIHgEA9UICCQDA/CuFXybp37r7S5UPmtlnJb0h6a0KGt6NVDw8NE/nCADAaejCCgBA/Tws6TfMzCTJzN4WxpdLOuLuJUm/pKDhDgAAdUcCCQBA/XxOUlrS82b2Yvi9JP25pBvN7EkF5avMOgIAFgSa6AAAAAAAImEGEgAAAAAQCQkkAAAAACASEkgAAAAAQCQkkAAAAACASEggAQAAAACRkEACAAAAACIhgQQAAAAAREICCQAAAACI5P8HJy95AVJIknkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "sns.regplot(x = df.Year,y =df.Price )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "733ca2d1-57ac-42dd-8a34-d4887788b0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['Price','Year']].corr(),annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db2e039-148f-4388-997c-19350c862627",
   "metadata": {},
   "source": [
    "observation :\n",
    "\n",
    "\n",
    "1- There is high relation between year and Price. cor-relation value is 0.49 we need this feature in model prediction.\n",
    "\n",
    "2- There is some outlier like in 1969. \n",
    "\n",
    "3- We can see contineous positive growth in price as year increassing. \n",
    "\n",
    "nextstep :\n",
    "\n",
    "1- Work on outlier and chek why there is drop in 2012. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62a4b1c-0214-4e99-ace6-f9a879ed1183",
   "metadata": {},
   "source": [
    "# Model and Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f7b4126f-6985-4d12-b86d-5d7d9a20f891",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = df.groupby('Model').Price.mean().sort_values().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b86c58c-df78-43f9-ae17-deb94832beb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x4320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,60))\n",
    "ax = sns.barplot(y = tmp.Model,x = tmp.Price)\n",
    "plt.xticks(rotation=45)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "38ceda9c-eeb4-4e53-aab8-cfb8e7ada059",
   "metadata": {},
   "source": [
    "Observation :\n",
    "\n",
    "Next_Step:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0287a6-8ab2-4675-a31d-97a22db62753",
   "metadata": {},
   "source": [
    "# Model Building "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c60b271-8d37-478c-b29f-2b36814077dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score \n",
    "from sklearn.base import clone\n",
    "from xgboost import XGBRFRegressor,XGBRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a67774fb-b6ea-4c23-9502-d2c3f0317697",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_build(data,model):\n",
    "    X = data[data.columns[data.columns != \"Price\"]]\n",
    "    y = data.Price\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "    print('Doing Model Train on whole data')\n",
    "    model.fit(X_train.values,y_train.values)\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_train = model.predict(X_train.values)\n",
    "    model.score(X_train,y_train),model.score(X_test,y_test)\n",
    "    print('MAE,RMSE and Rsquare for Test Data')\n",
    "    print(mean_absolute_error(y_test,y_pred), mean_squared_error(y_test,y_pred,squared=False), r2_score(y_test,y_pred))\n",
    "    print('MAE,RMSE and Rsquare for Train Data')\n",
    "    print(mean_absolute_error(y_pred_train,y_train), mean_squared_error(y_pred_train,y_train,squared=False),r2_score(y_pred_train,y_train))\n",
    "    print(\"=============\"*5,\"\\n\")\n",
    "    #dict_score  ={'MAE':} "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55821b8f-c139-41a6-b0ad-7989c409d0ff",
   "metadata": {},
   "source": [
    "# Model without Engine V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "91522727-c49e-4fad-9033-a905b7252ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Train.csv\")\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df = df.drop('EngineV',axis =1)\n",
    "df.dropna(inplace = True)\n",
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ed5c24a-2953-423d-bc1b-619a891ef92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "4327.39475948786 18206.057214079992 0.6147193956015071\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "2254.7045690757063 3953.1553540294803 0.9723969237208896\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "4152.832527906094 18425.239394979286 0.6053867908048893\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1357.4614428381608 3572.837702083043 0.9768843566714206\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "707888256177.2501 7301248524503.858 -6.196402251666687e+16\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5703.785839574595 10648.477524688398 0.7683277848005146\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "8415.467370059881 22113.896192901317 0.431571462367204\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5986.028446974236 11523.603035150803 0.7200399044819528\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2da0b1-0789-4710-a7d9-f453109f409c",
   "metadata": {},
   "source": [
    "# Model With Engine V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7dac217f-50ec-4095-8a9f-97b5594ad5b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Train.csv\")\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)\n",
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "04cbd7b8-37f0-4740-bd66-a80e6ad93d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2781.0399918004387 6531.833106003097 0.9362062637640576\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1734.327799722802 3282.3460310919545 0.9832306424762389\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2624.4045481003254 5296.551388169653 0.9580536419428732\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1260.1491071418254 4164.359165300804 0.9710377819785714\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "91792907933.82811 927098728472.8732 -1285167842455307.8\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6017.503310316382 12570.421784095222 0.6887046977190496\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5507.007500621119 10648.783574200637 0.8304460622915748\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4833.89840621118 10807.071043871918 0.76136550429448\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "acddf825-46bd-48f1-966f-5b612aa9eb23",
   "metadata": {},
   "source": [
    "# Observation \n",
    "     1- However EngineV is not showing good co relation but this feature is very helpful. \n",
    "     2- When we using Engine V, RMSE dropped by 1000 points so we can say EngineV is helpful for reducing high error. \n",
    "Next Step :\n",
    "    1- We will use EngineV in our model\n",
    "    2- Need to work on missing values for Engine V"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2981880-a3f3-4bb9-950a-67f2d97668d9",
   "metadata": {},
   "source": [
    "# Megrging Hatch and vagon under Body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3f645cb8-5719-465d-9365-b7e9ae360713",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Train.csv\")\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e0cf23dd-175f-4ca6-a5a2-9908604ac620",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b8f82518-ec89-497e-ae20-0a3e342dbd04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)\n",
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f09c23ba-c5c4-4749-b5b4-c5e8a257251b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2766.0265182671874 6606.017454281774 0.9347489788050558\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1738.4557928868644 3310.189167252057 0.9829493838342165\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2681.5742988541524 5434.007259145182 0.9558482107858022\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1260.4935377677011 3949.793166619192 0.9738669540954636\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "221027699762.30035 2024383932737.8186 -6127645645685682.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6019.882385481366 12577.558171305054 0.688240751452919\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5519.187783850932 10677.29768552876 0.8295368217755371\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4818.506219254658 10802.034680002007 0.7616114517038497\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4e08fb63-e0d5-419d-badb-4095bf1a7e66",
   "metadata": {},
   "source": [
    "# Observation \n",
    "     1- Mergeing Hatch and vagan is not a bad choice. not any change in model. \n",
    "     2- its always good to make model as simple as possible. \n",
    "Next Step :\n",
    "    1- Lets see after merhing Toyota and BMW are we seeing same performance. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539e3b04-36dd-4658-b84b-077448b8ba24",
   "metadata": {},
   "source": [
    "# Merging Toyota and BMW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "04d2d13e-f3d3-44ef-ab5e-ff6e8370ddc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Train.csv\")\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b288bec8-2d71-4d44-bb55-58267d3c3ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Brand == 'Toyota','Brand'] = 'BMW'\n",
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3aa46848-ab30-4848-9210-1d2c75a09a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)\n",
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "59211a87-a210-43ab-a8f3-453c11bcf92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2801.685785953119 5961.064286130245 0.9468680824463465\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1765.0997846111036 3482.8541204775993 0.9810025252571808\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2654.360310712083 5311.502977676853 0.9578164876551377\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1266.785148287262 4001.63547428625 0.973455317755375\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "543868319380.5665 4655499745243.065 -3.2407124205859964e+16\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6019.88924689441 12577.558284705783 0.6882399240708825\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5573.285147826086 10660.44858204309 0.8300743895465461\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4873.754796273292 10720.557979983112 0.7642282393374776\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fcf7483-df62-4a41-be4c-352738217ff6",
   "metadata": {},
   "source": [
    "Observation \n",
    "\n",
    "\n",
    "1- Model is showing little low score while merging Toyota and BMW. \n",
    "\n",
    "Next Step :\n",
    "\n",
    "1- We can apply outlier removal and test bot model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e9db7e-f835-4582-9abf-fc1e6bc729f8",
   "metadata": {},
   "source": [
    "# Date Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1c992651-8992-41af-bbf0-d3d25aa93795",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Train.csv\")\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2be13eb6-311b-457b-9b69-53a42dceaa26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Brand == 'Toyota','Brand'] = 'BMW'\n",
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "478d494f-0352-4726-9167-b87780baf544",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)\n",
    "df= df[df.Year >1997]\n",
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e04aa440-6ff0-4ff1-ad24-500d3e074cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3269.92995026094 9823.830931762172 0.8602281871527777\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1342.8332546449246 4097.932992452224 0.9743286948340845\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9f2cc4-8c05-4cdd-b5bb-8a67ff0e826e",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sort(df.Year.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "29a95628-3db7-444e-b42d-7769d7efcd4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3315.1618711895158 10235.1037030406 0.8482801748960752\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1340.921485667292 4139.403793130183 0.9736518263579822\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3318.762060929102 10046.314853757332 0.8538255701442627\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1344.9528139292397 4102.841948952568 0.9743606868563117\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3303.8263620709586 10169.183495596479 0.8502282147241469\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1319.977776191744 4148.087037211851 0.9735196097141651\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3270.9642097154083 10113.67172165737 0.8518589070794387\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1358.766586887345 4208.0156957478885 0.9729069477787681\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3318.2645844628596 9759.11189580798 0.862063743978986\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1343.3483491921138 4323.119573974182 0.9712710778014149\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3350.199423186492 10312.381306149817 0.8459804803552208\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1340.692121460643 4072.9035745227775 0.9745190449516965\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3318.939601464561 10137.969797567519 0.8511462346557326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1334.0383783465181 4134.732676850177 0.9736748768907364\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3319.86131697913 9906.22740900524 0.8578737079830727\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1348.3004987270915 4093.72574526684 0.9742913912489699\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3339.9717920613043 10233.619154340544 0.8483241840514384\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1337.1236274588573 4170.12503284974 0.9731915790268312\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3287.7715782053597 9859.298813474821 0.8592171031354464\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1322.1634443690236 3990.9611966863963 0.9756443855678636\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3337.825198273552 10306.283459998258 0.8461625740211491\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1337.2158816686858 4076.4005729793193 0.9746733109646033\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3263.6561858421173 9927.657682171663 0.8572581154569521\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1324.7009927004317 4019.9814146657877 0.9752464240797021\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3284.378993061182 10121.146280536488 0.8516398573563017\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1340.6161778174762 4279.80470741172 0.9718182897181175\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3226.3783025799135 9907.158311422376 0.8578469951044372\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1328.7678261371955 3991.4846107085427 0.9756082745339574\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3303.8294573012954 10370.21398455845 0.8442481282070927\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1360.3717435445794 4243.070942821653 0.9722309729626694\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3250.973350458308 9781.656018819735 0.8614257261559991\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1322.828454688215 3868.762137193639 0.9772714653747794\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3281.7708964828844 9853.085090912093 0.8593945011871339\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1329.5264168605686 3956.0481288904302 0.9760522993211739\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3334.7490621855472 10343.69732569976 0.8450436255850915\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1336.2085357536005 4105.0054815461945 0.9744455125768855\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n",
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3502.919660620016 10202.03828939278 0.8492588802433326\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1834.8082199312473 3395.34645808669 0.9834096357186303\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "3361.1418343432842 10180.81170242794 0.8498854983157969\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1337.0993279254405 4053.869299058978 0.9750711893189256\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "318250100746.61176 2068492872852.8696 -6196777409409406.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "6195.3660350953205 12205.74698388446 0.7379645066169009\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "6784.125695290859 14208.3376951826 0.7076222094143345\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "5101.825813518198 10601.146955820748 0.7851802319622103\n",
      "================================================================= \n",
      "\n",
      "-------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in np.sort(df.Year.unique()):\n",
    "    tmp = df[df.Year ==i]\n",
    "    print(\"XG Boost Model\")\n",
    "    model_xgb = XGBRegressor()\n",
    "    Model_build(df,model_xgb)\n",
    "    print(\"Random Forest Model\")\n",
    "    model_random = RandomForestRegressor()\n",
    "    Model_build(df,model_random)\n",
    "    print(\"Linear regression\")\n",
    "    model_lin = LinearRegression()\n",
    "    Model_build(df,model_lin)\n",
    "    print(\"KNN  regression\")\n",
    "    model_knn = KNeighborsRegressor()\n",
    "    Model_build(df,model_knn)\n",
    "    print(\"-------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f38cefe-eae0-40c4-9162-cb27cf002f08",
   "metadata": {},
   "source": [
    " Observation :\n",
    "\n",
    "\n",
    "1- Building model with date range is not a good idea. \n",
    "\n",
    "2- We see very high error with test data with models based on date\n",
    "\n",
    "Next step :\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c81bb28-8e4e-4585-aa17-2dbb47ef5765",
   "metadata": {},
   "source": [
    "# Removing outlier and feature selection approach:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ca6b07-43d6-415e-93dd-7f28376259d4",
   "metadata": {},
   "source": [
    "Approach :\n",
    "\n",
    " Outlier removal and below options : \n",
    " \n",
    "    1- Remove EngineV and remove outlier(Price and Mileage) =====>>  MAE ~2400, RMSE ~4000\n",
    "    \n",
    "    2- Remove outliers, rmove EngineV and merger Toyota and BMW =====>>  MAE ~2400, RMSE ~4100\n",
    "    \n",
    "    3- Remove Engine ,merge Hatch and Vagon   =======>> MAE 2400, RMSE 4100\n",
    "    \n",
    "    4- Remove Engine ,Merger both           ========>> MAE 2400, RMSE 4200\n",
    "    \n",
    "    5- Add Engine V and merger Brand and Body both   ====>> MAE 2100 RMSE 3500\n",
    "    \n",
    "    6- Add Engine V and merger Body only            ====>> MAE 2100, RMSE 3400"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ef71d9-2192-4160-b645-565b62700e94",
   "metadata": {},
   "source": [
    "## 1- Remove EngineV and remove outlier(Price and Mileage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0a3e5457-42c9-4dea-af8f-8a0c00d3162e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cb80b813-5dd3-442c-9c31-e368afe9caa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('EngineV',axis =1)\n",
    "\n",
    "df =df[df.Price < 64820]\n",
    "df =df.reset_index().drop('index',axis=1)\n",
    "\n",
    "df = df[df.Mileage <450]\n",
    "df =df.reset_index()\n",
    "df =df.drop('index',axis =1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6e27d2df-50a2-4aaf-a63e-0703f0aa9490",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "52626312-94a0-4d4c-9618-882b6b62f140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2362.90265422811 4025.1179032831456 0.8822552188425504\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1560.087696097583 2332.1542717803977 0.9610050668262425\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2426.3041882432885 4290.649644535551 0.8662078720238594\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "893.5565858550377 1603.3793467200371 0.9816054305831441\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "224749037592.4544 2447288487568.7954 -4.352660446180851e+16\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3399.892017851254 4954.971930557042 0.8069799538647967\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5591.512656992085 8052.5218966575985 0.5287533419424673\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4318.204178100264 6580.261430036488 0.5585949515293859\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a896736-8fb6-4af8-a05b-fd3042db8e13",
   "metadata": {},
   "source": [
    "## 2- Remove EngineV,merger Toyota and BMW together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "017e3f5f-dc03-402d-81a3-3a975f95a61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c0f2a327-2507-48f7-a3e4-72536ab1c002",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('EngineV',axis =1)\n",
    "df.loc[df.Brand == 'Toyota','Brand'] = 'BMW'\n",
    "\n",
    "df =df[df.Price < 64820]\n",
    "df =df.reset_index().drop('index',axis=1)\n",
    "\n",
    "df = df[df.Mileage <450]\n",
    "df =df.reset_index()\n",
    "df =df.drop('index',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "648e3333-cefd-44de-ab97-cb915c2d112d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d5c5954a-60e4-49d7-9ddf-02c6eda6eb97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2456.999378592056 4189.940221190479 0.8724148560006664\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1550.0311032490993 2328.3204308562285 0.9608096029116606\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2480.092835423011 4476.2870275316645 0.8543802411769815\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "901.3181954285933 1610.2786891699798 0.9813022837242807\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "65525323938.56887 900405896503.8369 -5891977982502499.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3399.893029147428 4954.972337301545 0.8069798574462501\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5630.024145118732 8083.30482625032 0.5251435212928198\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4319.835731530344 6556.7924572662805 0.564015086966855\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab39f75-c062-4115-9bf1-7910e9aa25b4",
   "metadata": {},
   "source": [
    "## 3- Remove Engine ,merge Hatch and Vagon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "79f89756-dc6e-4226-afd5-f05c97938d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)\n",
    "df = df.drop('EngineV',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b0eaf042-d0c7-4658-aaa9-3b97f88d6b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "142cef76-6dcf-4da9-9980-a2fbe68eb287",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df[df.Price < 64820]\n",
    "df =df.reset_index().drop('index',axis=1)\n",
    "\n",
    "df = df[df.Mileage <450]\n",
    "df =df.reset_index()\n",
    "df =df.drop('index',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c31b34f3-d36b-4423-be9e-c166e37fb384",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0bbeb624-e7eb-4de9-bd82-83038e4ea989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2429.024938091188 4095.420173672894 0.8781062649149777\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1539.5468826825386 2282.2416202512704 0.9627362130097948\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2448.543702216873 4297.284012600732 0.8657938030432759\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "901.1249717010878 1613.5994028302302 0.981300002326608\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "1700427610332.0713 18354802872347.19 -2.4484086417013504e+18\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3402.03432800132 4967.861136340067 0.8057793659932388\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5559.76916358839 8067.660549435079 0.526979799298678\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4301.016054749341 6567.627978221776 0.5582245799718302\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c92960-9c72-46e7-882f-5f3a0469a258",
   "metadata": {},
   "source": [
    "## 4 Remove Engine ,Merger both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "061a8831-2d51-415f-b919-041525627daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)\n",
    "df = df.drop('EngineV',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6cb54866-c160-4c33-a18f-e0d75c8b3f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Brand == 'Toyota','Brand'] = 'BMW'\n",
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'\n",
    "\n",
    "df =df[df.Price < 64820]\n",
    "df =df.reset_index().drop('index',axis=1)\n",
    "\n",
    "df = df[df.Mileage <450]\n",
    "df =df.reset_index()\n",
    "df =df.drop('index',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fc6e351b-4ab2-45ed-a49b-b53e8ac28d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "769c62d3-3b2b-4dc8-b15a-1dceffec7234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2489.60764962531 4202.371397261219 0.8716566656139423\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1580.69388990216 2367.8962509515677 0.959320401875112\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2538.6663484503915 4518.050407953898 0.8516503252307923\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "920.5491038064873 1637.5709002661001 0.9806393089945329\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "145815992485.68195 1409616944245.803 -1.4440647853151932e+16\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3402.0148186015836 4967.861647778698 0.8057784723324537\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "5603.832102902375 8105.602560052772 0.5225201321344051\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "4305.398713060686 6550.967578931253 0.5645662651470063\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a040ffb-4cc0-4284-bb98-5c8be31cd42a",
   "metadata": {},
   "source": [
    "## 5- Add Engine V and merger Brand and Body both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fac74aa7-4734-48a0-a706-a421d5a52d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0f605d78-2966-43c9-845e-5c39a15c7bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Brand == 'Toyota','Brand'] = 'BMW'\n",
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'\n",
    "\n",
    "df =df[df.Price < 64820]\n",
    "df =df.reset_index().drop('index',axis=1)\n",
    "\n",
    "df = df[df.Mileage <450]\n",
    "df =df.reset_index()\n",
    "df =df.drop('index',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3b4e683e-3b08-40ee-8418-d7ec9a0e6a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b9bd2b7a-04cd-4ae9-8aa0-2be78d31d4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2132.588027044211 3539.8233252973155 0.9089358102470261\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1275.847438342414 1854.0664911416281 0.9761904588840965\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2166.7417405692713 3789.004187448944 0.8956638937954793\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "807.0266879656939 1441.1142879890594 0.9853589838351451\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "45888411869.79349 799080852576.0853 -4640512032205874.0\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3403.5478607354885 4967.021067906779 0.8058579670821895\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "4695.427200527704 6935.9983902795875 0.6503750463466637\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3545.250831794195 5540.7897096237975 0.7093956561170305\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "835558b0-2bb0-477d-9309-1725a7f22b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 6- Add Engine V and merger Body only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "47b35d22-b327-473a-a8f7-0d385c480705",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.columns =['Brand', 'Price', 'Body', 'Mileage', 'EngineV', 'Engine_Type',\n",
    "       'Registration', 'Year', 'Model']\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "40a9a059-39a3-4c67-bdcb-fb45b07e3334",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.Body == 'hatch','Body'] = 'vagon'\n",
    "\n",
    "df =df[df.Price < 64820]\n",
    "df =df.reset_index().drop('index',axis=1)\n",
    "\n",
    "df = df[df.Mileage <450]\n",
    "df =df.reset_index()\n",
    "df =df.drop('index',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "49a860b0-e2b0-4ca0-b70b-cee9807d3747",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Model','Brand','Body','Engine_Type','Registration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9ae55e75-dfec-44c9-8902-b4232ac23698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2143.0536118318664 3496.377471777534 0.911157436571483\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "1228.5560009008725 1741.9109192014234 0.9791410048660248\n",
      "================================================================= \n",
      "\n",
      "Random Forest Model\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "2146.2396163652834 3700.2428518867832 0.9004949983787744\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "775.3774491942321 1343.6296195160626 0.9873464040178536\n",
      "================================================================= \n",
      "\n",
      "Linear regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "213215260578.27844 1832793816110.3003 -2.4412467390880496e+16\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3403.6007239445917 4967.021528551162 0.8058562509301651\n",
      "================================================================= \n",
      "\n",
      "KNN  regression\n",
      "Doing Model Train on whole data\n",
      "MAE,RMSE and Rsquare for Test Data\n",
      "4691.995419525066 6919.674554258225 0.6520187908041801\n",
      "MAE,RMSE and Rsquare for Train Data\n",
      "3524.7783924802106 5511.201415491176 0.7113735321965151\n",
      "================================================================= \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"XG Boost Model\")\n",
    "model_xgb = XGBRegressor()\n",
    "Model_build(df,model_xgb)\n",
    "print(\"Random Forest Model\")\n",
    "model_random = RandomForestRegressor()\n",
    "Model_build(df,model_random)\n",
    "print(\"Linear regression\")\n",
    "model_lin = LinearRegression()\n",
    "Model_build(df,model_lin)\n",
    "print(\"KNN  regression\")\n",
    "model_knn = KNeighborsRegressor()\n",
    "Model_build(df,model_knn)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a6fa32a5-fa32-491f-a78d-67af94c667e0",
   "metadata": {},
   "source": [
    "Observation:\n",
    "   \n",
    "   1- XG Boost is best algo for solving this problem\n",
    "   \n",
    "   2- Removing Engine V is not a good idea.\n",
    "   \n",
    "   3- Merging Brand is not a good idea. \n",
    "   \n",
    "   4- Merging of Body is reducing RMSE. \n",
    " - \n",
    "\n",
    "Next Step:\n",
    "\n",
    "1- Need to do hyperparameter tuining. \n",
    "    2- Need to apply some regularizer to overcome overfitting. \n",
    "    3- can apply polynomial degree or some SVM kernels \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fdf4cd9-0e9e-49c3-8baa-90475d22a0ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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