Final_SonuAgarwal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Analytics - BAN-210 Final Project\n",
    "\n",
    "## Fuel Economy \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "###  Problem Statement\n",
    "\n",
    "In this project I am analysing U.S. automobile fuel efficiency from the year 1984 to 2022.It is vital to understand how the automobile fuel efficiency has improved over time. Are there any other interesting insights or trends?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see what's inside the data\n",
    "### Data understanding\n",
    "In this project, there is dataset that contains fuel efficiency performance metrics, measured in **miles per gallon (MPG)** over time. The data is courtesy of the U.S. Department of Energy and the US Evironmental Protection Agency. The dataset also contains several features and attributes of the automobiles listed, thereby providing the opportunity to summarize and group data to determine which groups tend to have better fuel efficiency historically and how this has changed over the years.\n",
    "\n",
    "- Dataset link: http://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip \n",
    "- Metatdata link:  http://www.fueleconomy.gov/feg/ws/index.shtml#vehicle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries used "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install ggplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ggplot import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "from IPython.display import display\n",
    "from functools import reduce\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import model_selection\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "import warnings; warnings.simplefilter('ignore')# suppress all warning messages\n",
    "\n",
    "# Essential for matplotlib graphs in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the file  \n",
    "vehicles = pd.read_csv(\"Data/vehicles.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barrels08</th>\n",
       "      <th>barrelsA08</th>\n",
       "      <th>charge120</th>\n",
       "      <th>charge240</th>\n",
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       "      <th>city08U</th>\n",
       "      <th>cityA08</th>\n",
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       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>...</th>\n",
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       "      <th>c240Dscr</th>\n",
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       "      <th>phevHwy</th>\n",
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       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
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       "      <th>1</th>\n",
       "      <td>29.964545</td>\n",
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       "      <td>9</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.964545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
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       "      <th>4</th>\n",
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       "      <td>17</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
       "      <td>Tue Jan 01 00:00:00 EST 2013</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "<p>5 rows × 83 columns</p>\n",
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      "text/plain": [
       "   barrels08  barrelsA08  charge120  charge240  city08  city08U  cityA08  \\\n",
       "0  15.695714         0.0          0        0.0      19      0.0        0   \n",
       "1  29.964545         0.0          0        0.0       9      0.0        0   \n",
       "2  12.207778         0.0          0        0.0      23      0.0        0   \n",
       "3  29.964545         0.0          0        0.0      10      0.0        0   \n",
       "4  17.347895         0.0          0        0.0      17      0.0        0   \n",
       "\n",
       "   cityA08U  cityCD  cityE  ...  mfrCode  c240Dscr  charge240b  c240bDscr  \\\n",
       "0       0.0     0.0    0.0  ...      NaN       NaN         0.0        NaN   \n",
       "1       0.0     0.0    0.0  ...      NaN       NaN         0.0        NaN   \n",
       "2       0.0     0.0    0.0  ...      NaN       NaN         0.0        NaN   \n",
       "3       0.0     0.0    0.0  ...      NaN       NaN         0.0        NaN   \n",
       "4       0.0     0.0    0.0  ...      NaN       NaN         0.0        NaN   \n",
       "\n",
       "                      createdOn                    modifiedOn  startStop  \\\n",
       "0  Tue Jan 01 00:00:00 EST 2013  Tue Jan 01 00:00:00 EST 2013        NaN   \n",
       "1  Tue Jan 01 00:00:00 EST 2013  Tue Jan 01 00:00:00 EST 2013        NaN   \n",
       "2  Tue Jan 01 00:00:00 EST 2013  Tue Jan 01 00:00:00 EST 2013        NaN   \n",
       "3  Tue Jan 01 00:00:00 EST 2013  Tue Jan 01 00:00:00 EST 2013        NaN   \n",
       "4  Tue Jan 01 00:00:00 EST 2013  Tue Jan 01 00:00:00 EST 2013        NaN   \n",
       "\n",
       "   phevCity  phevHwy  phevComb  \n",
       "0         0        0         0  \n",
       "1         0        0         0  \n",
       "2         0        0         0  \n",
       "3         0        0         0  \n",
       "4         0        0         0  \n",
       "\n",
       "[5 rows x 83 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=43921, step=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "Index(['barrels08', 'barrelsA08', 'charge120', 'charge240', 'city08',\n",
       "       'city08U', 'cityA08', 'cityA08U', 'cityCD', 'cityE', 'cityUF', 'co2',\n",
       "       'co2A', 'co2TailpipeAGpm', 'co2TailpipeGpm', 'comb08', 'comb08U',\n",
       "       'combA08', 'combA08U', 'combE', 'combinedCD', 'combinedUF', 'cylinders',\n",
       "       'displ', 'drive', 'engId', 'eng_dscr', 'feScore', 'fuelCost08',\n",
       "       'fuelCostA08', 'fuelType', 'fuelType1', 'ghgScore', 'ghgScoreA',\n",
       "       'highway08', 'highway08U', 'highwayA08', 'highwayA08U', 'highwayCD',\n",
       "       'highwayE', 'highwayUF', 'hlv', 'hpv', 'id', 'lv2', 'lv4', 'make',\n",
       "       'model', 'mpgData', 'phevBlended', 'pv2', 'pv4', 'range', 'rangeCity',\n",
       "       'rangeCityA', 'rangeHwy', 'rangeHwyA', 'trany', 'UCity', 'UCityA',\n",
       "       'UHighway', 'UHighwayA', 'VClass', 'year', 'youSaveSpend', 'guzzler',\n",
       "       'trans_dscr', 'tCharger', 'sCharger', 'atvType', 'fuelType2', 'rangeA',\n",
       "       'evMotor', 'mfrCode', 'c240Dscr', 'charge240b', 'c240bDscr',\n",
       "       'createdOn', 'modifiedOn', 'startStop', 'phevCity', 'phevHwy',\n",
       "       'phevComb'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look at the data\n",
    "display(vehicles.head())\n",
    "display(vehicles.index)\n",
    "display(vehicles.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43921 entries, 0 to 43920\n",
      "Data columns (total 83 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   barrels08        43921 non-null  float64\n",
      " 1   barrelsA08       43921 non-null  float64\n",
      " 2   charge120        43921 non-null  int64  \n",
      " 3   charge240        43921 non-null  float64\n",
      " 4   city08           43921 non-null  int64  \n",
      " 5   city08U          43921 non-null  float64\n",
      " 6   cityA08          43921 non-null  int64  \n",
      " 7   cityA08U         43921 non-null  float64\n",
      " 8   cityCD           43921 non-null  float64\n",
      " 9   cityE            43921 non-null  float64\n",
      " 10  cityUF           43921 non-null  float64\n",
      " 11  co2              43921 non-null  int64  \n",
      " 12  co2A             43921 non-null  int64  \n",
      " 13  co2TailpipeAGpm  43921 non-null  float64\n",
      " 14  co2TailpipeGpm   43921 non-null  float64\n",
      " 15  comb08           43921 non-null  int64  \n",
      " 16  comb08U          43921 non-null  float64\n",
      " 17  combA08          43921 non-null  int64  \n",
      " 18  combA08U         43921 non-null  float64\n",
      " 19  combE            43921 non-null  float64\n",
      " 20  combinedCD       43921 non-null  float64\n",
      " 21  combinedUF       43921 non-null  float64\n",
      " 22  cylinders        43631 non-null  float64\n",
      " 23  displ            43633 non-null  float64\n",
      " 24  drive            42735 non-null  object \n",
      " 25  engId            43921 non-null  int64  \n",
      " 26  eng_dscr         27342 non-null  object \n",
      " 27  feScore          43921 non-null  int64  \n",
      " 28  fuelCost08       43921 non-null  int64  \n",
      " 29  fuelCostA08      43921 non-null  int64  \n",
      " 30  fuelType         43921 non-null  object \n",
      " 31  fuelType1        43921 non-null  object \n",
      " 32  ghgScore         43921 non-null  int64  \n",
      " 33  ghgScoreA        43921 non-null  int64  \n",
      " 34  highway08        43921 non-null  int64  \n",
      " 35  highway08U       43921 non-null  float64\n",
      " 36  highwayA08       43921 non-null  int64  \n",
      " 37  highwayA08U      43921 non-null  float64\n",
      " 38  highwayCD        43921 non-null  float64\n",
      " 39  highwayE         43921 non-null  float64\n",
      " 40  highwayUF        43921 non-null  float64\n",
      " 41  hlv              43921 non-null  int64  \n",
      " 42  hpv              43921 non-null  int64  \n",
      " 43  id               43921 non-null  int64  \n",
      " 44  lv2              43921 non-null  int64  \n",
      " 45  lv4              43921 non-null  int64  \n",
      " 46  make             43921 non-null  object \n",
      " 47  model            43921 non-null  object \n",
      " 48  mpgData          43805 non-null  object \n",
      " 49  phevBlended      43921 non-null  bool   \n",
      " 50  pv2              43921 non-null  int64  \n",
      " 51  pv4              43921 non-null  int64  \n",
      " 52  range            43921 non-null  int64  \n",
      " 53  rangeCity        43921 non-null  float64\n",
      " 54  rangeCityA       43921 non-null  float64\n",
      " 55  rangeHwy         43921 non-null  float64\n",
      " 56  rangeHwyA        43921 non-null  float64\n",
      " 57  trany            43910 non-null  object \n",
      " 58  UCity            43921 non-null  float64\n",
      " 59  UCityA           43921 non-null  float64\n",
      " 60  UHighway         43921 non-null  float64\n",
      " 61  UHighwayA        43921 non-null  float64\n",
      " 62  VClass           43921 non-null  object \n",
      " 63  year             43921 non-null  int64  \n",
      " 64  youSaveSpend     43921 non-null  int64  \n",
      " 65  guzzler          2590 non-null   object \n",
      " 66  trans_dscr       15044 non-null  object \n",
      " 67  tCharger         8452 non-null   object \n",
      " 68  sCharger         946 non-null    object \n",
      " 69  atvType          4090 non-null   object \n",
      " 70  fuelType2        1754 non-null   object \n",
      " 71  rangeA           1749 non-null   object \n",
      " 72  evMotor          1322 non-null   object \n",
      " 73  mfrCode          13113 non-null  object \n",
      " 74  c240Dscr         118 non-null    object \n",
      " 75  charge240b       43921 non-null  float64\n",
      " 76  c240bDscr        112 non-null    object \n",
      " 77  createdOn        43921 non-null  object \n",
      " 78  modifiedOn       43921 non-null  object \n",
      " 79  startStop        12232 non-null  object \n",
      " 80  phevCity         43921 non-null  int64  \n",
      " 81  phevHwy          43921 non-null  int64  \n",
      " 82  phevComb         43921 non-null  int64  \n",
      "dtypes: bool(1), float64(31), int64(28), object(23)\n",
      "memory usage: 27.5+ MB\n"
     ]
    }
   ],
   "source": [
    "vehicles.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barrels08</th>\n",
       "      <th>barrelsA08</th>\n",
       "      <th>charge120</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityA08</th>\n",
       "      <th>cityA08U</th>\n",
       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>...</th>\n",
       "      <th>UCity</th>\n",
       "      <th>UCityA</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>UHighwayA</th>\n",
       "      <th>year</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>charge240b</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.0</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.0</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>...</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "      <td>43921.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.10</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>18.71</td>\n",
       "      <td>7.11</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>23.69</td>\n",
       "      <td>1.06</td>\n",
       "      <td>34.84</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2002.76</td>\n",
       "      <td>-4360.53</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.69</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>8.87</td>\n",
       "      <td>12.82</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.09</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.86</td>\n",
       "      <td>...</td>\n",
       "      <td>12.62</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.12</td>\n",
       "      <td>8.85</td>\n",
       "      <td>11.76</td>\n",
       "      <td>3702.19</td>\n",
       "      <td>0.31</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1984.00</td>\n",
       "      <td>-32500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>18.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1992.00</td>\n",
       "      <td>-6500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>21.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2004.00</td>\n",
       "      <td>-4250.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>16.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>26.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>-2000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>47.09</td>\n",
       "      <td>18.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.30</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.20</td>\n",
       "      <td>145.0</td>\n",
       "      <td>145.08</td>\n",
       "      <td>5.35</td>\n",
       "      <td>122.00</td>\n",
       "      <td>...</td>\n",
       "      <td>224.80</td>\n",
       "      <td>207.26</td>\n",
       "      <td>187.10</td>\n",
       "      <td>173.14</td>\n",
       "      <td>2022.00</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>9.60</td>\n",
       "      <td>97.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>88.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       barrels08  barrelsA08  charge120  charge240    city08   city08U  \\\n",
       "count   43921.00    43921.00    43921.0   43921.00  43921.00  43921.00   \n",
       "mean       17.10        0.22        0.0       0.07     18.71      7.11   \n",
       "std         4.69        1.14        0.0       0.76      8.87     12.82   \n",
       "min         0.06        0.00        0.0       0.00      6.00      0.00   \n",
       "25%        14.33        0.00        0.0       0.00     15.00      0.00   \n",
       "50%        16.48        0.00        0.0       0.00     17.00      0.00   \n",
       "75%        19.39        0.00        0.0       0.00     21.00     16.04   \n",
       "max        47.09       18.31        0.0      15.30    150.00    150.20   \n",
       "\n",
       "       cityA08  cityA08U    cityCD     cityE  ...     UCity    UCityA  \\\n",
       "count  43921.0  43921.00  43921.00  43921.00  ...  43921.00  43921.00   \n",
       "mean       0.8      0.67      0.00      0.50  ...     23.69      1.06   \n",
       "std        6.2      6.09      0.04      4.86  ...     12.62      8.74   \n",
       "min        0.0      0.00      0.00      0.00  ...      0.00      0.00   \n",
       "25%        0.0      0.00      0.00      0.00  ...     18.42      0.00   \n",
       "50%        0.0      0.00      0.00      0.00  ...     21.70      0.00   \n",
       "75%        0.0      0.00      0.00      0.00  ...     26.07      0.00   \n",
       "max      145.0    145.08      5.35    122.00  ...    224.80    207.26   \n",
       "\n",
       "       UHighway  UHighwayA      year  youSaveSpend  charge240b  phevCity  \\\n",
       "count  43921.00   43921.00  43921.00      43921.00    43921.00  43921.00   \n",
       "mean      34.84       1.30   2002.76      -4360.53        0.01      0.25   \n",
       "std       12.12       8.85     11.76       3702.19        0.31      3.62   \n",
       "min        0.00       0.00   1984.00     -32500.00        0.00      0.00   \n",
       "25%       28.00       0.00   1992.00      -6500.00        0.00      0.00   \n",
       "50%       33.40       0.00   2004.00      -4250.00        0.00      0.00   \n",
       "75%       39.49       0.00   2013.00      -2000.00        0.00      0.00   \n",
       "max      187.10     173.14   2022.00       6500.00        9.60     97.00   \n",
       "\n",
       "        phevHwy  phevComb  \n",
       "count  43921.00  43921.00  \n",
       "mean       0.25      0.25  \n",
       "std        3.50      3.54  \n",
       "min        0.00      0.00  \n",
       "25%        0.00      0.00  \n",
       "50%        0.00      0.00  \n",
       "75%        0.00      0.00  \n",
       "max       81.00     88.00  \n",
       "\n",
       "[8 rows x 59 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's see the descriptive statistics of the vehicles data\n",
    "vehicles.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barrels08              0\n",
       "barrelsA08             0\n",
       "charge120              0\n",
       "charge240              0\n",
       "city08                 0\n",
       "city08U                0\n",
       "cityA08                0\n",
       "cityA08U               0\n",
       "cityCD                 0\n",
       "cityE                  0\n",
       "cityUF                 0\n",
       "co2                    0\n",
       "co2A                   0\n",
       "co2TailpipeAGpm        0\n",
       "co2TailpipeGpm         0\n",
       "comb08                 0\n",
       "comb08U                0\n",
       "combA08                0\n",
       "combA08U               0\n",
       "combE                  0\n",
       "combinedCD             0\n",
       "combinedUF             0\n",
       "cylinders            290\n",
       "displ                288\n",
       "drive               1186\n",
       "engId                  0\n",
       "eng_dscr           16579\n",
       "feScore                0\n",
       "fuelCost08             0\n",
       "fuelCostA08            0\n",
       "fuelType               0\n",
       "fuelType1              0\n",
       "ghgScore               0\n",
       "ghgScoreA              0\n",
       "highway08              0\n",
       "highway08U             0\n",
       "highwayA08             0\n",
       "highwayA08U            0\n",
       "highwayCD              0\n",
       "highwayE               0\n",
       "highwayUF              0\n",
       "hlv                    0\n",
       "hpv                    0\n",
       "id                     0\n",
       "lv2                    0\n",
       "lv4                    0\n",
       "make                   0\n",
       "model                  0\n",
       "mpgData              116\n",
       "phevBlended            0\n",
       "pv2                    0\n",
       "pv4                    0\n",
       "range                  0\n",
       "rangeCity              0\n",
       "rangeCityA             0\n",
       "rangeHwy               0\n",
       "rangeHwyA              0\n",
       "trany                 11\n",
       "UCity                  0\n",
       "UCityA                 0\n",
       "UHighway               0\n",
       "UHighwayA              0\n",
       "VClass                 0\n",
       "year                   0\n",
       "youSaveSpend           0\n",
       "guzzler            41331\n",
       "trans_dscr         28877\n",
       "tCharger           35469\n",
       "sCharger           42975\n",
       "atvType            39831\n",
       "fuelType2          42167\n",
       "rangeA             42172\n",
       "evMotor            42599\n",
       "mfrCode            30808\n",
       "c240Dscr           43803\n",
       "charge240b             0\n",
       "c240bDscr          43809\n",
       "createdOn              0\n",
       "modifiedOn             0\n",
       "startStop          31689\n",
       "phevCity               0\n",
       "phevHwy                0\n",
       "phevComb               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns with null values\n",
    "vehicles.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows is: 43921\n",
      "Number of columns is 83\n"
     ]
    }
   ],
   "source": [
    "# Rows and columns in the dataset\n",
    "print(\"Number of rows is: {0}\".format(len(vehicles)))\n",
    "print(\"Number of columns is {0}\".format(len(vehicles.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ho many years of data is given\n",
    "len(pd.unique(vehicles.year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First year is: 1984\n",
      "Last year is : 2022\n"
     ]
    }
   ],
   "source": [
    "# The first and last years in the data\n",
    "print(\"First year is: {0}\".format(min(vehicles.year)))\n",
    "print(\"Last year is : {0}\".format(max(vehicles.year)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regular Gasoline     29037\n",
       "Premium Gasoline     13205\n",
       "Diesel                1203\n",
       "Electricity            287\n",
       "Midgrade Gasoline      129\n",
       "Natural Gas             60\n",
       "Name: fuelType1, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Commonly used types of fuel\n",
    "pd.value_counts(vehicles.fuelType1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    30904\n",
       "M    13006\n",
       "Name: trany2, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of automatic and manual transmissions cars\n",
    "# A = Automatic\n",
    "# M = Manual\n",
    "vehicles_transmission = vehicles.copy()\n",
    "vehicles_transmission['trany2'] = vehicles_transmission.trany.str[0]\n",
    "pd.value_counts(vehicles_transmission.trany2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city08</th>\n",
       "      <th>co2TailpipeGpm</th>\n",
       "      <th>comb08</th>\n",
       "      <th>highway08</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displ</th>\n",
       "      <th>fuelCost08</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>UCity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>423.190476</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2250</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>-2500</td>\n",
       "      <td>23.3333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>807.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4300</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>-12750</td>\n",
       "      <td>11.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>329.148148</td>\n",
       "      <td>27</td>\n",
       "      <td>33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1750</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>807.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4300</td>\n",
       "      <td>16.6667</td>\n",
       "      <td>-12750</td>\n",
       "      <td>12.2222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>467.736842</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3050</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>-6500</td>\n",
       "      <td>21.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city08  co2TailpipeGpm  comb08  highway08  cylinders  displ  fuelCost08  \\\n",
       "0      19      423.190476      21         25        4.0    2.0        2250   \n",
       "1       9      807.909091      11         14       12.0    4.9        4300   \n",
       "2      23      329.148148      27         33        4.0    2.2        1750   \n",
       "3      10      807.909091      11         12        8.0    5.2        4300   \n",
       "4      17      467.736842      19         23        4.0    2.2        3050   \n",
       "\n",
       "   UHighway  youSaveSpend    UCity  \n",
       "0   35.0000         -2500  23.3333  \n",
       "1   19.0000        -12750  11.0000  \n",
       "2   47.0000             0  29.0000  \n",
       "3   16.6667        -12750  12.2222  \n",
       "4   32.0000         -6500  21.0000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets make a new set for corealtion\n",
    "\n",
    "vehicles_corr=vehicles[['city08','co2TailpipeGpm', 'comb08','highway08', 'cylinders', 'displ', 'fuelCost08', 'UHighway','youSaveSpend', 'UCity']]\n",
    "vehicles_corr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between various features of vehicle and UCity(miles per gallon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see the correlations between the Ucity and other featues of the car.\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(vehicles_corr.corr(),cmap=plt.cm.Reds,annot=True)\n",
    "plt.title('Heatmap with correlation of various features of the data',\n",
    "         fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary till now: \n",
    "- Years count **39** years starting from **1984** to **2022**.\n",
    "-  **Regular Gasoline** is the most commonly used fuel type then **Premuim Gasoline**\n",
    "-**Natural Gas** is the least used.\n",
    "- There are automatic transmissions double of manual transmissions.\n",
    "\n",
    "Correlation:\n",
    "- Ucity have high or strong positive correlation with these selected features such as - city08,comb08,highway08,Uhighway and yousavespend.\n",
    "- Ucity have moderate to strong correlation with theese features but in negative -co2tailppegpm,cylinders,displ, fuelcost08.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Chevrolet', 'Ford', 'Dodge', 'GMC', 'Toyota', 'BMW', 'Mercedes-Benz', 'Nissan', 'Volkswagen', 'Porsche']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counts of each brands or make\n",
    "mostfreqbrands=vehicles[\"make\"].value_counts()[:10].index.tolist()\n",
    "print(mostfreqbrands)\n",
    "vehicles1=vehicles[np.isin(vehicles, mostfreq).any(axis=1)]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Most frequent brands of cars used')\n",
    "sns.countplot(x = \"make\", data=vehicles1,order = vehicles1['make'].value_counts().index)\n",
    "t = plt.xticks(rotation=45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Compact Cars', 'Subcompact Cars', 'Midsize Cars', 'Large Cars', 'Standard Pickup Trucks', 'Two Seaters', 'Sport Utility Vehicle - 4WD', 'Small Station Wagons', 'Sport Utility Vehicle - 2WD', 'Minicompact Cars']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Most frequent vlass of cars used'}, xlabel='count', ylabel='VClass'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counts of each vclass\n",
    "mostfreqvclass=vehicles[\"VClass\"].value_counts()[:10].index.tolist()\n",
    "print(mostfreqvclass)\n",
    "vehiclesc=vehicles[np.isin(vehicles, mostfreqvclass).any(axis=1)]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Most frequent class of cars used')\n",
    "sns.countplot(y = \"VClass\", data=vehiclesc,order = vehiclesc['VClass'].value_counts().index)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Regular', 'Premium', 'Gasoline or E85', 'Diesel', 'Electricity', 'Midgrade', 'Premium or E85', 'Premium and Electricity', 'Regular Gas and Electricity', 'CNG']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counts of fuelType1\n",
    "mostf=vehicles[\"fuelType\"].value_counts()[:10].index.tolist()\n",
    "print(mostf)\n",
    "vehicles2=vehicles[np.isin(vehicles, mostf).any(axis=1)]\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title('Measure of fuelType')\n",
    "sns.countplot(x = \"fuelType\", data=vehicles2)\n",
    "t = plt.xticks(rotation=45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Front-Wheel Drive', 'Rear-Wheel Drive', '4-Wheel or All-Wheel Drive', 'All-Wheel Drive', '4-Wheel Drive', '2-Wheel Drive', 'Part-time 4-Wheel Drive']\n"
     ]
    },
    {
     "data": {
      "image/png": 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P7fxNnjxpnoO/Y9o1JSLWoSZvfj4zj2qbbwJW69ltVWpk+1ZghYiY0ravxoMj3n9u+xERU4FpwIz+Ri9JkiQtPmOWiEfENOAC4PDM/K/h7a1k5d42Mg6wJ3B+Zs4ELgZ2b9v3okbSAc5rt2n3X9z2lyRJkgbCWJam7AusArwlIt7Stp2Tme8B9gBObO0OLweOb/cfBHw+Ig4H/gi8om0/AjglIq4Gbm+/L0mSJA2MvifimfnY9uPH239z2+dKYOO5bL8R2Gou2/8OvGixBSlJkiSNMVfWlCRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjowtZ//eEQsD/wE2DEzb4iIrYGPAcsAp2fm4W2/DYCTgOWBHwEHZuasiFgTOBV4FJDAHpl5d0Q8AvgS8HjgNmC3zPxLP5+LJEmStDj1bUQ8Ip4FXAKs3W4vA5wM7AysC2wUEdu13U8FDs7MtYFJwH5t+wnACZm5DnAZcETbfjRwcWauC5wIHNev5yFJkiT1Qz9LU/YDXg/c3G5vDFyXmddn5iwq+d41ItYClsnMS9t+p7TtQ8AWwBm929vPO1Aj4gBfBrZr+0uSJEkDoW+lKZm5L0BEDG9aHbilZ5dbgDXmsX1l4M6WtPdu/49/q5Ww3AlM58Gkf75WWmm5BXg2i9fQUF8rgharQYoVBiveQYoVYPr0aV2HMGqDFOug8dj2j8e2fzy2/eOxXTRjmQlMBub03J4EzF6A7bTtw/v0mtRz36jMmHE3s2eP/Of7b/r0acycOWv+O44DQ0NTBybWYYMS7yAe29tuu6vrEEZl+vRpAxProPHY9o/Htn88tv3jsZ2/yZMnzXPwdyy7ptwErNZze1VqBPuhtt8KrBARU9r21XhwxPvPbT8iYiowDZjRt8glSZKkxWwsE/GfARERT2zJ9SuB8zPzRuDeiNi07bdn2z4TuBjYvW3fCzi//Xxeu027/+K2vyRJkjQQxiwRz8x7gX2AM4FrgN/y4ETMPYCPR8RvgeWA49v2g4D9I+IaYHPg8Lb9CODZEXF12+f1Y/EcJEmSpMWl7zXimfnYnp8vBJ42l32upLqqjNx+I7DVXLb/HXjR4oxTkiRJGkuurClJkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6sCoEvGI+Oxctp2x+MORJEmSlgxT53VnRHwaeDSweURM77lrCHh8PwOTJEmSJrJ5JuLAZ4GnAE8DzuzZPgu4tF9BSZIkSRPdPBPxzLwMuCwivpeZN41RTJIkSdKEN78R8WGPiYgvAisCk4Y3Zub6fYlKkiRJmuBGm4j/D3AKcDkwp2/RSJIkSUuI0SbiszLzY32NRJIkSVqCjDYRvyoinpqZv1kcDxoRrwLe2W6en5lvjYitgY8BywCnZ+bhbd8NgJOA5YEfAQdm5qyIWBM4FXgUkMAemXn34ohPkiRJ6rfRLujzeOCXEXFdRPx6+L+FecCIeDhwPLAl1Y1l84jYCTgZ2BlYF9goIrZrv3IqcHBmrk3Vp+/Xtp8AnJCZ6wCXAUcsTDySJElSF0Y7Iv7uxfiYU6gvAMsC/6R6kt8JXJeZ1wNExKnArhFxDbBMZg63SjwFeH9EnARsAby4Z/tFwNsXY5ySJElS34w2EV8sJSkAmXlXRBwB/Bb4F5VArw7c0rPbLcAa89i+MnBnZs4asX3UVlppuYWKf3EYGhrtYe/eIMUKgxXvIMUKMH36tK5DGLVBinXQeGz7x2PbPx7b/vHYLprRZgJ/o7qlTOLBrikLnPwCRMT6wGuAtYA7qNKTtfnPbiyTgNnUyPlottO2j9qMGXcze/bYN4CZPn0aM2fOmv+O48DQ0NSBiXXYoMQ7iMf2ttvu6jqEUZk+fdrAxDpoPLb947HtH49t/3hs52/y5EnzHPwdVSKemf+uJY+IpYBXArGQMW0DXJiZt7Z/7xTgrcADPfusCtwM3ASsNpfttwIrRMSUzHyg7XPzQsYjSZIkjbnRTtb8t8y8PzNPAV6wkI95JbB1RCwbEZOAnYCfARERT4yIKVSif35m3gjcGxGbtt/ds22fCVwM7N627wWcv5DxSJIkSWNuVCPiEbFiz81JwIbAIxfmATPzgoh4OvBLYCbwc+B9wHeBM4GHAecBZ7Rf2QM4MSKWpxYUOr5tPwj4fEQcDvwReMXCxCNJkiR1YWFqxKFKQ964sA+amR8CPjRi84VUO8OR+14JbDyX7TcCWy1sDJIkSVKXFrhGXJIkSdKiG21pymRqQuV2VN/vC4AP9LQPlCRJkrQARjvSfSzwPOA4ahn6TYCP9CsoSZIkaaIbbY34tsCGrVsJEXEu1f3kzf0KTJIkSZrIRjsiPnk4CQfIzPuojieSJEmSFsJoR8SviIiPA/9NdU95A/DrvkUlSZIkTXCjHRF/PdU3/CfU4jsrU8m4JEmSpIUwzxHxtpz9icDZmblP23YutRz9nX2PTpIkSZqg5jcifiSwPPDjnm37AY+gVsOUJEmStBDml4jvCLwyM28d3pCZNwN7AS/pZ2CSJEnSRDa/RPz+zLxn5MbMvBO4rz8hSZIkSRPf/BLxByJi2siNbdtQf0KSJEmSJr75JeJfBk6KiGWHN7SfTwLO7GdgkiRJ0kQ2vz7inwA+A/wlIq6mEvd1gS9REzklSZIkLYR5JuKZORvYPyKOAZ4JzAZ+lpm3jEVwkiRJ0kQ1qpU1M/NG4MY+xyJJkiQtMUa7sqYkSZKkxchEXJIkSeqAibgkSZLUARNxSZIkqQMm4pIkSVIHTMQlSZKkDoyqfaGkJdvMWbOZPn1a12GM2iDFet/9s7jzjnu6DkOS1AETcUnzNTR1Mocdd1HXYYzK0NBUZs6c1XUYo/aRQ7bsOgRJUkcsTZEkSZI6YCIuSZIkdcBEXJIkSeqAibgkSZLUARNxSZIkqQMm4pIkSVIHTMQlSZKkDpiIS5IkSR3oZEGfiNgJeC+wLHBBZh4SEVsDHwOWAU7PzMPbvhsAJwHLAz8CDszMWRGxJnAq8CgggT0y8+4xfzKSJEnSQhjzEfGIeDzwGeDFwPrAMyJiO+BkYGdgXWCjtg0q2T44M9cGJgH7te0nACdk5jrAZcARY/YkJEmSpEXURWnKS6gR75sycyawO/Av4LrMvD4zZ1HJ964RsRawTGZe2n73lLZ9CNgCOKN3+xg+B0mSJGmRdFGa8kTg/og4B1gT+BZwNXBLzz63AGsAqz/E9pWBO1vS3rt91FZaabmFCn5xGBrqpCJooQxSrDBY8Q5SrDBY8Q5SrADTp0/rOoRRG6RYB43Htn88tv3jsV00XXxaTaVGs7cC7gbOAe4B5vTsMwmYTY3Yj2Y7bfuozZhxN7Nnj/wn+m/69GnMnDlr/juOA0NDUwcm1mGDEq/Htn8G8djedttdXYcwKtOnTxuYWAeNx7Z/PLb947Gdv8mTJ81z8LeL0pS/AN/LzNsy8x7gLGBrYLWefVYFbgZueojttwIrRMSUtn21tl2SJEkaCF0k4t8CtomIR7REejuq1jsi4olt2yuB8zPzRuDeiNi0/e6ebftM4GKqvhxgL+D8MX0WkiRJ0iIY80Q8M38GfBi4BLgGuBH4NLAPcGbb9lsenIi5B/DxiPgtsBxwfNt+ELB/RFwDbA4cPkZPQZIkSVpkncxoysyTqXaFvS4EnjaXfa8ENp7L9hupOnNJkiRp4LiypiRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktSBqV0+eER8FFg5M/eJiK2BjwHLAKdn5uFtnw2Ak4DlgR8BB2bmrIhYEzgVeBSQwB6ZeXcHT0OSJElaYJ2NiEfE84G928/LACcDOwPrAhtFxHZt11OBgzNzbWASsF/bfgJwQmauA1wGHDGG4UuSJEmLpJNEPCJWBI4BPtA2bQxcl5nXZ+YsKvneNSLWApbJzEvbfqe07UPAFsAZvdvHKHxJkiRpkXVVmvI/wLuBx7TbqwO39Nx/C7DGPLavDNzZkvbe7aO20krLLXjUi8nQUKcVQQtkkGKFwYp3kGKFwYp3kGIFmD59WtchjNogxTpz1myGpg7OVCiPbf8M0rEdNB7bRTPmn1YRsS/wp8y8MCL2aZsnA3N6dpsEzF6A7bTtozZjxt3Mnj3yn+i/6dOnMXPmrPnvOA4MDU0dmFiHDUq8Htv+GcRje9ttd3UdwqhMnz5tYGKFivew4y7qOoxRGbTz9iOHbDkw58KgnbeDxGM7f5MnT5rn4G8Xw0a7A6tFxBXAisBywFrAAz37rArcDNwErDaX7bcCK0TElMx8oO1zc/9DlyRJkhaPMb+ulJkvyMynZOYGwHuAc4DtgIiIJ0bEFOCVwPmZeSNwb0Rs2n59z7Z9JnAxldQD7AWcP5bPQ5IkSVoU46LAKzPvBfYBzgSuAX7LgxMx9wA+HhG/pUbPj2/bDwL2j4hrgM2Bw8cyZkmSJGlRdDqjKTNPoTqekJkXAk+byz5XUl1VRm6/EdiqrwFKkiRJfTIuRsQlSZKkJY2JuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLkiRJHTARlyRJkjpgIi5JkiR1wERckiRJ6sDULh40It4L7NZunpuZb4uIrYGPAcsAp2fm4W3fDYCTgOWBHwEHZuasiFgTOBV4FJDAHpl599g+E0mSJGnhjPmIeEu4Xwg8HdgAeGZEvAI4GdgZWBfYKCK2a79yKnBwZq4NTAL2a9tPAE7IzHWAy4AjxuxJSJIkSYuoi9KUW4C3ZOb9mTkTuBZYG7guM6/PzFlU8r1rRKwFLJOZl7bfPaVtHwK2AM7o3T6Gz0GSJElaJGNempKZVw//HBFPokpUPkkl6MNuAdYAVn+I7SsDd7akvXf7qK200nILHPviMjTUSUXQQhmkWGGw4h2kWGGw4h2kWAGmT5/WdQijNkixwmCdC4MUKwzWuTBIsQ4aj+2i6eyvPiLWA84FDgNmUaPiwyYBs6kR+zmj2E7bPmozZtzN7Nkj/4n+mz59GjNnzpr/juPA0NDUgYl12KDE67Htn0E8trfddlfXIYzK9OnTBiZW8P223wblXBi083aQeGznb/LkSfMc/O2ka0pEbApcCLwjMz8P3ASs1rPLqsDN89h+K7BCRExp21dr2yVJkqSBMOYj4hHxGOBsYPfM/H7b/LO6K54IXA+8Ejg5M2+MiHsjYtPM/DGwJ3B+Zs6MiIuB3YHTgL2A88f6uUiStCSaOWv2QJUkDFKs990/izvvuKfrMDRGuihNeSvwMOBjETG87TPAPsCZ7b7zeHAi5h7AiRGxPHA5cHzbfhDw+Yg4HPgj8IqxCF6SpCXd0NTJHHbcRV2HMSqDVvbzkUO27DoEjaEuJmseAhzyEHc/bS77XwlsPJftNwJbLdbgJEmSpDHiypqSJElSB0zEJUmSpA6YiEuSJEkdMBGXJEmSOmAiLkmSJHXARFySJEnqgIm4JEmS1AETcUmSJKkDJuKSJElSB0zEJUmSpA6YiEuSJEkdMBGXJEmSOmAiLkmSJHXARFySJEnqgIm4JEmS1AETcUmSJKkDJuKSJElSB0zEJUmSpA6YiEuSJEkdMBGXJEmSOmAiLkmSJHXARFySJEnqgIm4JEmS1AETcUmSJKkDJuKSJElSB6Z2HYAkLclmzprN9OnTug5j1AYpVkka70zEJalDQ1Mnc9hxF3UdxqgMDU1l5sxZXYcxah85ZMuuQ5CkebI0RZIkSeqAibgkSZLUAUtTJEmSxgnnjfTHfffP4s477uk6jP/DRFySJGmccN5If4zXOSOWpkiSJEkdGOgR8Yh4JXA4MAR8IjM/1XFIkiRJ0qgM7Ih4RDwaOAbYDNgA2D8intxpUJIkSdIoDfKI+NbA9zPz7wARcQawC3DkfH5vCsDkyZP6G908PHLa0p099oKYOjSVWTOndB3GAvHY9o/Htn88tv3jse0fj23/eGz7o4vcr+cx53qgJs2ZM2fsolmMIuKdwLKZeXi7vS+wcWbuP59f3Qy4uN/xSZIkSc3mwCUjNw7yiPhkoPdbxCRg9ih+7xfUwbgFeKAPcUmSJElQI+GrUfnn/zHIifhNVEI9bFXg5lH83n3M5RuJJEmS1Ad/eKg7BjkR/x7wvoiYDvwTeBkwv7IUSZIkaVwY2K4pmfln4N3AD4ArgNMy8+edBiVJkiSN0sBO1pQkSZIG2cCOiEuSJEmDzERckiRJ6oCJuCRJktQBE3FJkiSpAybikiRJUgdMxCVJkqQOmIhLE1hETOo6Bv2nub0mS9rrtKQ93y54jBc/j+n4NqivzyCvrKkBFxGTMnNO+3lKZj7QdUwTyYjjuxwwJTPv6DisJdqI1+SFwP3AnZl5ebeRjZ0Rx+BxwN3UMbiv28gmjhHH+GXAcsA9wHd8D1g4nrfj24jX58nAXwEyc0angY2CC/qoEyP+aA4E1qCu0HwRyMyc3WV8E0lEvAnYBFgFOC8zP9RtRIqIg4GXA6cDxwEbZ+ZlvX8XE11EHAZsDkwCrgE+nZk3dBrUBBMRbwV2AM4EDgA+lZmf6TaqweZ5O75FxFuAralEfCZwbGb+b7dRzZulKepETxK+H7AHcArwBmCnDsOacCJiH2BnYB/qjek5bXRcY6j3kmkbrXkJsA31Hvxt4OaIWH8JSsJfBGybmS+irgqsDNwWEQ/rNrLBNnyeRcSkiFgFeHZmPhdYBvgd8JWIeHFEDHUZ56DyvB3fImJbYMfM3A54OPX+eltErNRtZPNmIq7ORMRkYCPgdcAWwMXAycDb/aBYbCYDbwYOpC5P7w58JiJ26DSqJUzPF88VgX8CVwKHAdtSX5SeDny0swDH3v3AuW10cTngEOBj1OitFlLPF7mnZ+ZfgaGIOBPYEngF8EzgVZk5s6sYB5zn7fg2E7ioXQVegXp93kkNeoxb1oirExGxOfB34C/AR6g/oB0zc3ZE7AV8AfhzhyEOtIh4CXAzMA34IXBBZm7f7psD3NpddEumiNgCeBd11WcjYN3MXLndtzbwhw7DGxPtGGwFXEaV5vwD2Ckz74+IZYClOwxvQmijf6e0ZOQS6ji/rh3jJwBLtWN975JyBWZRed6ObxHxPOoq4xeB7YH7gC0yc05ErAnc2GV88+OIuMbEiEumK1Gj4KsAPwbWAz7SkvCXUpNg7u4s2Inh2cDRmXkclYg/IiJWjogDqFGx27oMbkmUmT+iPrBfCewG3BERX4qITwCvAj7VYXh91VOa8xhgTeBC4BfAdcCe7bzcFPhpNxEOthHdIu4CzgCmA18FvgucFBGnUFdh3pmZ95iEz5/n7fjTcojhfKI3h51MDWZcSF1xfEsrfd2Y+hsYt0zE1XetI8ocqEunbRbzj4APUkni8cA7I+I7wKHAvs7sH73e+sSIWBYgM98O3BQRzwZeBtwL/DewK7CLk4v6KyIeGxErtJ/Xjogntbs+RI2E30J9QFwO/BZ4RWZe1U20/RERq/XcfHT7/2nAE4GDgLdRI1UbU5OJd8rM68c0yAEXEZtHxKpt5G+3iFghM++n3l+PAh4A3k0d768BW2fm1R2GPO553o57q/Z8iVy1/f/n1IDentR5fynwZGrQ6aXjfbKmXVPUVxHxFGC9zDw9IjalJroc0e47CvhFZp7TLs3PBO7JzL90GPJAiYgNgc0y8xMRsTM1En59Zv6/iHgfsFRmvqvtO7Xd/ld3EU9sbaTmEVTC/Q5gNeoL5x3Uh8U3gHOoUcnzOgqz79rl4AOAI4EnAZ+hnvvxwLrUFYH3A7My84GIWNo2cAsuIt4InAXMAI4FngZ8n+rGsznwSOCjjn6Pjuft+Na+JL0feBPVae27wIepKz/LAocD78vMm9r+Q4MwH8JEXH3TLhvtSCUgj6QmDG0CrEW9wW1J9WF9W2dBDriIeBTVRmtpqu74kdSEzBuo+tD3UlcYvt9VjEuSiJg83HozIjYD1gauor5kngh8hZqc+Rdgv8y8vaNQ+6qVn82iLudvA3yH+kLyv9SXk2WA/xo+L5ekto2Lw4j2rwGcD2xIjRC+gLqy+Gtg5cx8TmeBDhjP2/GtXf0dAp7S/vs5lZQvRX0G3gV8OTMv6CrGhWFpivpiOCHJzHOoWvDXAkOZuRfwaeqS34bAmyPiFR2GOpCG6+Qy81bqWJ4BPAM4JzNfAPweWJ368Nh4RC2d+qC9HsNJ+A7A86nzftPM/BXwXKoU5SrgcVTZwIQyfJ618rNnUfWzzwGWB15E/e3/sd335ohYymRmwYxIwg+mBja+SbXBvLPNC9kJ+B41MXONzoIdEJ6341vP63MvNeD0GOq9dcXMfDX1xfPvVPe1IyNiagzQKpuOiKuvIuK1VMJ9FbABNdHlc5k5s5VVvJIaYbBDyiiN+CB+PXUJ9STgaOB6apLmjHb/7sAvM/P3XcU70Y38QG4Tjt+fmU+NiI2oS6fnAZ/JzLvaPitn5t+6ibj/ImJv6m/7LVRSsxVwRmae2e7fHLjOMrSFFxG7Aa8G9s/MP0XEh6iOETtl5g0RMQWYPAiX5scLz9vxrZ3zh1EdUrYA9gf+X2ae1u5/CjCjzcEZGCbi6puI2JFaNXCdlni/lprgciVwambeOSg1XONRa9l0DHBUZp4XEWtREzKvoZI+JxCNsVZjegIwKTN3aNs2o0qELqI+NG6dyKNp7cvHD4EXZ+Z323m5HVWW9oPM/FyX8Q2qllhPaS3zHg78huoSsTNwX+s69QFgL+A5mfmnDsMdOJ6341v7EnQRNfny7FZG9ELgNcBXMvOznQa4CEzEtdi0euX7qQ+LGS3x/hiwT2ae1T5I9qSWn/0RVTPLRE1IFrdWC/pIqh7uUupLzTuofuvHtlGwxwBfAn5AJeizuop3SRARz6c+DKZSIzEfiIhdqMVTLgJOzsy7I2Ir6vLpPpn5984C7oOIeCZVfvY4qv//ENWK8WnAMzPznlYesQvVyeAtw1cGNDpRKwbuStXFngd8C7iJqg3/Zma+t2ff9wFfGO+dIrrmeTu+RcTGVGnlY4CvtgGMrwJPzcx12z6PAF5MdQbbA7hrEPMJE3EtFq0m9i3UqoHrU7OXf0Jd2tubmrl/TkvGXwF8z8t7o9eO7weptlmPpGoXd6Tqw/du27+QmTdFxKOpS9KOiPVRRLyQugLxYep12IlqE7kdtWLmNlRN+Bcz866IWCYz7+kq3n6IiO2p5/9tqiziz9SiGudS5+u6wAtaUvNo4J8TdYJqv0TENtSiZ+8EVqJasq3ftl1BdY74cmYe3VWMg8bzdnyLiO2A/6Jabu5GzXk6PzNPiIhzgDUy8xlt3xWAOZl5Z2cBLyITcS2yiHgBNfJ9MNUTeQdqadkvUG2FtqGS709n5hldxTmo2gfx+4E3Z+ZPo3qFn0BdWVgfCGo04HbgU5l5c1exLinaF6P3AW/IzEt7tv+EasH5/Ih4OZWc/5Cq4Z9QV3/aF5FjgEMy8ydt21HU+fhl4ALqi8ozgY3bRCstgJ4k/IDM/Gnbtjo1AvhCavAD4GJqsOMjnQQ6QDxvx7d2zh8LHJyZP4mIpaj+7ZtSzQi+GLXmyBMz8wldxrq42ElBi6QlJJ8Cds/Mi4DbMvNkqib21cDTM/MkqhfrPhGx3CDNZu5auzx3BvD2loQPZeY/M3NvqivCN6hV3S6kSlbsadtnrZftMcDPh5Pw9mFBZm4CPDwi9qG+hH6bKh2YM8GS8GdTz22v9mG5bLvrfVTd8v6Z+U+qs8ElVAmAFkCrgf0gcN5wEg7QvmifBdwCbJeZv6Mmrn29k0AHiOft+Nbq9E8H3jSchGctUPU/VDnmzgCZuQ1wVUQ8rrtoFx9HxLVIIuJoarn61dokon8vcBARB1GLIzyb6vE5J10xc4G0S6j7UYt0fDozZw2/OUXEKlRf6tdm5v9GxDRrGPsvIpan6kafQ3Wk+UzbvnRm3hcRH6e+kH6gyzj7Kaqf7w3A1zLzDW3b8IcmEZHAu70Ctmhal4h9gVOBs1qJ06SslTQPosqgXuqE99HxvB3f2mfaj6gyoWOzFk0aPt8fDlwNvCczv9hpoIuZI+JaJJl5OPVt9ZcRsVZLRIaXXP8e8NvMvCczbzcJXygXUmUNGwDviurPfn97c/orVZO8HIBJeP8MX8Vpx/1OavTxR8BmEbE/QD64wt4/qEWW/v17E0m7KnMv8Hhgp4j4IkA7L4f/9n9E1d1qIUStgktmfpWa1P4aYOeIWL7nysoc4H9NwkfH83Z8a6/PX6n1FnYBPtyThE/NWhH6m9QXqQnFRFyLLGsJ9XOBb0bEY3tq6jYGlrMcZeG0N5/7qC80Z1OrvR3RkvE5UQshrQj8tcMwlxQrtv9Pbh8Ot1MfCt8BtoiIAwAiYlfqQ+RrMOFqwpcByGpFOtQ+GJ8MbB4Rp7b77m218U/HhGaBRVm+XfkaTsa/RpX/7UstLjM8Ur4PNQiiefC8Hd8iYnggaWb7zLuZmkD7fOCj7f12VtSaGJsCE64JgaUpWiAR8WTggczMudz3QeoPaEPqj+gYYM/MvHpsoxxcbeLr0zLzo+32lHZ5bmlqctbOVB/224EDqUlcV3UV70TXvkCuSn0475jVr30KMLt9GXoENSFzC+ARVKutV2fmtR2F3BcR8VRqEvbXW03y8AjWzHbJ+Brqi8l51PyQ1/p3v2Ai4pHUe+Zs4F1Z6yxMzdaCtH3J249KRJ4OvCozr+ks4AHgeTu+RURQ9finZeYVbdvw67M69bqcSS1U90aq/euEO+cdEdeoRS0zewRweEQ8aeT9mfkOqr/tXdRM/z18U1tgM6hLcm8CaEn4lDYyfgE1SWu49dZ+JuH91SZZ3kKNRp4ZES/MzAf4vyPjP6MS9v0mWhLerEhd4XpRRDwB5jrCuDv1ofka/+4Xyu3Ula/ZwHseYmT8JOAJmISPluft+LYiVVq5W0SsD//x+gyPjO9FdVGZkEk4OCKuUeqp1VoW+AxVm/yR4VGGEfu+i2ozZJI4Sm3kdXJLvJ9B9WA//CFGxjcHfp+ZN3QX8cTXc85Pzlq1cC/gc8D2mfmdESPjy1Gv38D2sp2b6FkBNCK2pJaU/g217Pfv2/bhEaylqUnbN3QW8ACKEausRsQWwMuBe4AjM/OOESPjD29JpB6C5+34NuL1eRZ1vt8PfCkzf922T21fRFcGls3MG7uLuL9MxDVfc/mgWAb4JDAF+OBwmcrI/TQ6I96UHtbqFdcFLgPe25OM//vDWP014jXZCPhLZv4pql3nOVTbuAt6k/Eu4+2HEcdguFPP2lSrtyuppOYP7f4hJw0uuBHHeBNqAuZ1VOnJDsAsKhm/07//0fG8Hd8e4vNubarU8n6qTOU/kvEOwx0TJuKapxF/NHtSvarJzM9GxGepy6hzHRnXgomI11KXUf9C1cbdTq2cd3hm/ld3kS25IuLNVEJ0NbXa3k7UZMzPArtk5rc6DG9MRMQh1OImj6UuEd8OvB74NXC2f/uLrpWivYxaQXB94A3Aw6jlu5cCDku7Ii0Qz9vxLSIOBZ5BvT4foUpad6Sutp+RmZd3F93YskZc89SThL+JmlRxE/CpiNiDelObAxwdEU/sLMgJICIOpLogfI7qDbxHu9LwdOAjEXFwh+EtkSJiM+DFmbk1Vcd4G3WJ9EtUzfgpEfHwmMAdgSJiP2qC8Oup0ap9shaXOZoqkdouIoY6DHHgtZHw7TNzc+AqaqGeX1Ojt2cD/wSW6SzAAeR5O75FLXg2XP/9ALBTZn6f6gL0KKqmf6nuIhxbJuKar9YZYhPgedRozTepBWZeRC09ezv1YaGFENXDdi2qTm5dKuE7PCLeQs0WfwbVT1x9FD29wtumZaj++IcAj6bqTF8TEW/OzFOpJZb/NRHLUnosRy3YtR81UvXaiDiNKpn4ALUwipf2F8Bcvrj9Fbg4Ij4BbEMlkIcA/9WSk8Mz89axjXLged6Ob4+gutQcCvwLODQizqDWX/gUtXjd/d2FN7ZMxPV/zOWDYhI1u/mzVEK+J5Wk7Nfqtw5onSU0CiOPb1bf9XuoCZovz8wdqA+PlwKrZOYVE7QTx7gxYn7D8u3/11KlQvtk5rZZS18H8PB2/4SbmDmXzdOBHwIbZuaOmXk3VTIxlJk/bZ0NNEojSv1WaC0L7wK2phbtelHrynMnrZ91Zt7TUbgDwfN2fHuI12dpqgPQ06m2sHcCU4GHZeavshb2WWJM7ToAjS9zmc38V2qlwK8CHwI2bpMrtgWmtNHc+x7yH9R/GHF8dwCmUeU+PwOeAvyi7boj9UXZYzsGel6TA4FNIuJPVCJ0JrBeRHyMmkS3GdU6ksyc3VG4i92I83Jv6oNyaWrUajNgZruUvxewDpU8agH1HONDgWdTV8I+RSUlhwLva52ptgZ27SrOQeF5O76NeH1eQw3gPRz4NPAK4O/AahGxDdVK8m9dxdolJ2tqriLijdSI7I+pyWr7UaUor6V6hT+HGr217+pCaGUnO1FLKm8PnEDVMu4API5Kwvcdnj2u/mvtCfenrvh8g1rG/rNU3+bdgTuAUydyW842F2Rn4CiqBO1Q6svIl6kkZmXgdf7dL7yIeBV1lWXriDgHuCsz92htSzeiamS/kpnXdRroAPG8Hd8i4g3UJPc3Ab+kkvAfAv+Puhq8MvDGidonfH4cEdf/0SYP7QI8F/g48Fvgf4FPUN08pgAfsO/qwolaDGmzzNwqIt5KjYh/iSqDeDVVj/z3zPxHh2EuMaIWqpoCPAl4BzWSdgtwHPV6fCMzXzfcy727SPsrIqYBz6JWxX0L9bf+DWDbzHxBu/o1ZPeOBTOXtq4PA46NiLdRHVEOiIivA8dnpkvWLyDP2/Etao2F51ADeXtT7V9/TL0+O7f332WX5NfHGnHNbZLaVOBSasb52sBrqMukB7f6uktMwkevvdH0ugO4KyK+TH147Eq1LjskM+/NzD+YhPff8PmembPbxK0/UiUC+2TmNu01eCVVPsRES8JbD/SRHgWcSn1w7gkMUR+etHNzif2wXBgje1q3zUPUebZBm3twNzVhzcmDo+B5O77N5fNuJlWS8t9Ux5qXU+Wur4+Ipdv77xL9+jgiLqjJmHOoJbpvoXomfxJYKjPXBYhafvYvnUU4oOI/V8TbiErC/0Qdy02AA7NWd1sOuKd9WM+c4J04OhURj83MG7JWxNyb6krzJSCpc//iiFiNGmWbxAQ974e/WLT6zH8A1wBfBD4GPLfNBdkeWDoilnHS4IKJiEcDdwN3tEvz60XEXzLzfRGxHfCwqIW7tqTOtXd3GO7A8Lwdv6KtQtx+3pYqO7mcGgX/LPCE9vq8mirFdDAYa8SXaBGxOVUCcXVr0bYfcBHwXWrCy8uoson/pfom7956W2sUWgnKvpn59og4gKqPWwZ4P/UBsg21mMFfqHKIl0zk+uPxICJWB46gOqL8A3gz1a3mOcDhwOrUpNlnUaOUb8nMK7uJtj8i4tnAEZm5Q9QiXR+iLhX/CziFmjT1HuBcqlxqd2trF0xErEklh1+gjuvbqEm++wI3ZOZrI+JE6v1gReCtS2p97Gh53o5vrbnDqzPzwIjYFziSanM8nVo1cxdqoaofUa/VXn7eFRPxJVRETKcmCx5J9VXdihoF35b6cLiaWmL9QCphOc0/mtFrVxA2AjakvtSsQSXez6PqkE+hRmCnUTXhP87M6zsJdgkREU8GDqBWK92Eahe3e2b+b0QcRP09fBL4afuV2Zl5Rweh9k27KvMb4FdU///LqfeAFagSqbXb7aWpc/MvmXljJ8EOqIh4JvV3/wfqy90/gZMy86y2JsPpwB8zc7+2/7KtNaYeguft+NY+7/5JO7epnOF9VBvOg6mrPntSr80jgb9m5h87CXYc8rLAEqhddr+MGhU8mkrEL8vMi6mVrW4FngY8NjNfl5nvMgkfvYhYgVol8xHUpKGlgBUzc05mXkhdQn01NQp+SWaeahI+JtalXoufABdQbbT2B8jME6jX6khqIu0/JloS3hxNTQpcl1rc5EXAbVnLfZ9LLbH+UWBqZv7MZGahTKKO4Z3UFZeVgGdFxAqZeTtVI/vkiPhS2/9fnUQ5WDxvx7ePUQNMGwPLUoNQwwMZJ1Gj4N8Els7MX5iE/ycT8SXTX4GTqW+pX6QmURwWEeu0P5DPU6MOW7QZ6VoA7c3nBmA9akXM/wZujoijWs3i+cDxVK/g5ToLdAkREcNzYb5LlZ68MjO/RvUaXjUi3gyQmZ+hejpP5JaRBwJTI2KjrCXV76DaNJKZV1BJzU9ZQvv5Lg6ZeRn1Be9t1Kj4u6ie4bu0ZPwfVMvSd7T9vSw9f56349suwGrUFd+XUi0j39lqxv9K1Yefjn3c58rSlCVIRKyWbQXMiHghVa94aGbeFBHvp3olv6zVjK9KTRqc0WHIAyUi1gZoozRExHeB32Xm6yPi+dTxvRU4JjPv8ZJ0/7UJXRsDZ2fmb1qN+DnUVaCzqcva2wLXZeYHOgu0j1o70jsz86o2Gfgwql3Yu9r9CVyTmS9pt5fOTBeSWgDt/fT1VN3ytdQo9weAH2TmtyLixdQl+rOBL2StJKh58Lwd3yJiK2oO2Q2ZOSsi9qOuor87IlYCvk0tVPfGzJw90du/LgoT8SVERDyH6gzxReBDmfmviDiSaif0vNZB4nDgjcAWmfnbDsMdKK0N3uOplRd/Dpycmf+vvRl9CPgKNTL+Aqpk5drMPGou/YW1mEXEV4AXUl+AjuLBxag2pEa/HwBeQo1Yvjsz/95RqH3Rvnj8GLiXqtm8FriROh9PbiU5RMRfgQsz85UdhTqwWru2NwDvBD5DlUC9GdiU+nt/SWbeHxG7tNt7TNCyp8XG83Z8a3PMfgbcRs13+nX771vACZl5ekSs2PY5JzPf0lWsg8DSlCXH8KIlrwLe09ppfZSa1fx6gMw8mprZbz/bURpOpjPzD8B/UYn4GyLiw9RI66+ANVrCfRFVEnQieEl6jAyf0x8GXkyVA2xD1e+v1UYmzwbePtGScIDMvBk4i+qr/ARqVHEPamW7zSLiqW2/VahuMloA7e9/NjXn4M9UKcr3qYRkBtUi+Ng2GngGtRqxSfh8eN6Ob5l5G3WOL02VmxxDlaS8F3hjRDy1vZ9uRJVmah7sI76EyMwfR8Rh1GjN76jJmN+jRshXHr5slJkf7TLOAbQyNSoA8AtqefqtqFHW9ak68Hsi4vrMvIg65uqjiNiYmih0GTWD//FUi8hXUq/NbtTrs3lEvKR96E8oEbEycEfWQkVHUCP/1wFfpa6MPZn6Yr4e1Y2C9mVSo9SSwfUj4luZeW1EfIA6x15OrRXwLOq824lqY3gl1VlCD8HzdnyLiFWABzLzb9QciC8A9wGvBT5Hlf39A9g0Iq5uk5Nv7ybaweGI+AQWEVtFxGcjYs2o1ch+R7UO+n5mDrfQ257qvXpwd5EOpoh4DPD94cl+wNeotniHZOZJVOuy04G1qFGCh8WDq5eqDyJiaepKxMURsQf1QX4EcBCwTWZ+t7WN+xSVpE8oETEpItagJq69OSI2b/MQbqSuAPyemqR9NdXf99iIGOou4sHU/o5fTJ1rp0Ut3nMOdQXsuZl5HjVB7Tjqi/oM8CrYQ/G8Hd/a6/MY4DTg0IjYOjPvBc6kFun5A7XuyB+pzlRvoq5maBSsEZ/AIuJR1EjCNVR7p08CbweenZk7tn2eSNWJ/9S68AUTEcsAW1AlKee2/66iTQTMzG+3/bYCcniirPovInakWkTeSLXOmkG9Vidm5q1tn+WylhefcCJiJ+CZwNOpsomvAd8Bjs7MM1siuRSw0kS8IjAW2uDGdOCtwPOpL95bAasAe+eDS9v/e7VBzZvn7fgWtSLs2sBewAlUK9jjgA9k5g979nt0Zv65kyAHkIn4BDVcatJGCHeg2gqtSzXVPxb4bmae2mWME0VErEW1b1oXeBg1yei+zDy508CWMBExlJkzh+v225fM9akVY5emang/npmXdxpoH/VOAI6IR1Irt36B6uW7HFWzeZBJzKKJiKmZOavn9l7Ak6gkcX/gk5n5nnafk7Lnw/N2fBvx+kziwcnuJ1ET3Z9ITUL+U3dRDi5LUyaoloRPae2czsrMg4FLqFHxacCOlkksuOFj1kbDhj+Qb+TBqw33UJ1njotaDU59NnycWxI+/LpMyczfZ+bXqXrwa4HnUq/L5Il67rcvIMPv67dn5q+oK17LA48CdqQ+RLWAImL91oaQ1q5tynB5RGZ+gVrU5OvU5fkNImK5dp9J+ILxvB3fpmTmL6jSLKiOVJtQpUNaCE7WnEBGjrz0JOMPRMTymfneiHg2dRn1EP5zoqHmIyLWpFbDPI1aPQ9g+JLzo7NWx9wvInamPjhuH/Mgl0zHt8v/z2rn+lKtXdxmVP3i5yPijdRkr5smWplA1PLSqwLXU0un39fzd78e1QXpA8CKwBwgu4t2MLWEeyvguRHxQGZ+s/VEfqC1hn058I7M/FlEbA9MnqhlT4tLRDwNWCczT+/9Atl6TnvedqwNcKyTmV8c8frMahOVJ1MdwJairgRf2l20g83SlAlixKWjN1CjCl9st7cAXkc11r+tbZuw9bH9EhGbU2U+04DLMvNzbfsmVPeZl2Tmr9u2oTbzX33SPqyfkJnnRMRFwKzMfH67b0uqhvEdmfnNLuPsp/al7yiqbd69wJmtTd7w+Xo8sF/rIKOF0N4/H0vVKu9C9Qc/PTO/0Tr0fIUqm/i29eDz165GTQE+To16vz8zz+q5fwuq7tjztiMt6X4vNc/m0OH3lHbfllTr4zdkpsn3YmBpygTRk4S/htY1ot1ej7pkevpwEt72NwlfcD+llkh/HbAC/HvhiROoLzm/7hk1MAnvo3actwZeHhE7ZOaWwMMi4gdtl22pBXq+OVHLUKJWv30TsFvW6oLXUO3zhidqvwF4n8nMIluampC9NTV58KfA7hHxUup94MDhidkm4fOXte7CLOAKqtXd9hGxD/y7Pd6rqeTc87Yj7Tz+PfC/wP5Rq2YStUjd3sCxJuGLj4n4gOupWR5+LV9DXU66od1eFTg4M8+eqAlJP/Ues/bh8Smqf+paEbFPmzy0c2ae2/b1EtMYaB8UJ1Oz9l8aETtl5qbA0hFxVma+s53zQxO4RncmdVl4qXb7RGCliFgWuBt4Sxu1ndJVgIMsIpZpc0C+S33BOYr6gnc6lYy/FJiWmRe0/X1/XTAzgDuov+HNIuLdwAFUEu7nVff+QV1p+yT1HnsU8BZqgOPrvj6Lj4n4ABtRE74mQGZuBvw9Is5tty8c/uY6gROSvum50nBARBwDbJS16NE1wFatNGB6RKzSRno8xn0U1csWgMy8i+qs8GvgxS0Z34RKRn/V9pnIVybuAP4HuL99EZ9OTWybkpn/AoYiYulWy6wFEBHPpZLt90fEE3qS8fdRyfgZVAL56lYT7vvrgvsZcGUr8fsbtert1OFBJI9n5y4FZrTSvp8AbwaGsrXh9fVZfEzEB1hPkngIcHZEnBgRW2XmRsDqEXF2pwFOEFG9bV9PrZb32oj4SGaeSL057U21cFKfRcSGwI8j4rSI2LwlSHdSI+OXAy+LiB0zcwvgr1FtJSesdoXmq8B17QrBELXq3Z0RsTe17PQyXcY4wO6iVgxcA/hRRHyU+rx8JfB+atXMc4BvUitmasHNBtaIiF2pLzcnAWtHxO7dhqVmaWCViHg51SHlE9SVi1d1GdREZCI+4CJiT+oS6Z7U8uqHRsQLM/PpwNMi4iudBjjgIuIlwM7UxJTPUG9IW0bEBzPz/1EJ+o6Z+dcOw1xS3AbcBKxDdak4PyL2B1bMzE9R/dtfERHbZ+a2ra3khJaZ9/aMeN8D/KKds28AjslaYloL7s/UcvQnURM0b6Tav76YWlr9NGqZ9c+mC5csrL9RkzaPpCYEvhk4jzrO6kBPqeukdl7fSX3xfFdmHk6tQXJxhyFOSCbiAyqqF/LDqd6d7wf+TtVz/Qo4JCJelpmPA97ZYZgDZy51b4+geqRuEhHTWnK3G/CSNjJ+S2beNNZxLmlaN4obqdHvrwIfpOoVPwR8JyKOB26mzv9fdhZot2ZRV2iOAfbMzKs6jmdgtcvvpwInZOZPqSsuf6NKAGdT7dqut+xn4bVj9zGq48z32+bT/GIzdkZ+3s2l3OTHwGsy8zvt9reWhAGOsWb7wgESc1mhrV0m+js1SngZNcv5XKqG+aDM/MeYBzqgRrSAfA414e0maiGJd1Ot4L6dmf+K6ik+uWdSrMZA1IIqHwaeTPUXvoKq230t1Wf4bZn5l47C61T7Yn4y8J7M/F3X8Qy6iFiZ+qI3g7rq+IbMPL/dt4pXwRafuX22qb9GfN69mHo//RNVt3/rQ+2rxc9EfECM+KPZjRqZuR64GriBGgncFViFSkx2XVITkkUVEQdRid0VwPbU5ehHU2UoJwFnZ+Y9XcW3pBhxzv+7P3NEfJqq0V2R6gxyZhvZecSS/sUz2mJGXccxUUTEB6kuSdu3PuGuD6AJJSIOpcovz6WuqH0UOMXEe+xYmjIgRkzMPJhatfFY4AXUpIrvUbV2H6dGwk3CR6n38lxEPJNqobVzZr6W6tP8LeoLz8nAK6i6RvXRiCR8KGu1veHjfjawEtWW88zWGWTOkp6EA5iELx497wnvBb4LPBwmfBceLWEi4nHAJm0dhnuB64CzqI5gS/W0RVYfeZDHuYiY2vPzatRI4FbUa/dbakXHTanetp8HXpqZvxn7SAfTiITvMdQH7m8y86bWQ/h0ahT8NZn5JWCvdDGkvut5TQ6k+mND1eYC/ICauLlF2/e+MQ9QE1rWkt6TgAeoc21j+yZr0M3lHP47cFfrsLYd8DJgM+CtmXm/C1SNDRPxcSwitgGOawsdANxKtdW6gFpO/UVUbfhhmXlJZn4jM3/fUbgDqSfhex01+vVHYMOIOKC1hwP4Fw8mgXeMfZRLpojYl+oG9AH4d3I0uY36HgM8JiKW6zJGTVz54AqQRwEnealeg67n8+55EfFs6ovmjdQVxve0CbQrAf+MiIf55XNsTJ3/LupCS8JPAI4DPtFGbo+OiGupiWrHtF3Xov5olnOkdvQiYivg9sy8IiK2pVqUfTQzb4yINwJHRsRTqEt1L6UtHe6Hcf/MZULQUsBzgNWB30XElJ4uFZcDP/GcV79lZnYdg7QoRlz5PRA4hCqxPIFaWGll4KiIuAPYANglM+/tKNwljon4OBQRL6Tqvw/IzO9FxN3Asu3ubwKPAd4ZEYdRifgrTEhGrx3fz9CSa2BV6k1pw4i4LDMviIibgIOAFYA9MvPabqJdMoz4oFgP+FtmnhAR9wDfjogNM/OqVi40yxZakjR/I95bVwGeAqwPrE3NKbuHmv80lVrA6m2+v44tu6aMMxHxfGr55E0z85q2OuCXqR7hRwO3ZuY/IiKob7E32sd69FoS/kGqBu77EbFMZt7TOtHsTE3MOjczb+s00CVUuxqxG1WGtSzwOqpX/vHAZpnpKoaSNAojuk29mVoT4znACzLz2ojYhOqy9mPgI5n5r86CXYJZIz7+LA1MAlaIiKWoGcyzqcv03wKuiIhzgM0z88cm4aPXvuScDryqJeGPA06NiPUy86tU55nnUkulr9hlrEuiiHgu1TZyZ+BQ4CvU+X821c/5vDaT37pFSZqPniT8BcAOVEnr+cBpEbFWZv6EmgPxDGCZzgJdwpmIjzOZeR7wKmoU/EbgA5m5WWbuDmxL9fm8ieocoQXT+yVnCPgScElmXg2QmZ8DLgWeTk1i0dhaGbg8M2dQk2ZPo2rBt8vMo4GntZn8XsaTpIcQEVsPN3mIiKdRZZY3ZuYVmbkfcCFwVkQ8LjMvBnZv77vqgIn4OJSZ36L+cOYA98O/2w79MTN/mJkHZeYfuoxxEI34kvMn4L8z8+PwYJvIzPw0VSNnd5Q+iohlen4eHuG+GlgjIrbOzNmtLeF9wPR2/9/HOExJGkSXAV+JiKe2cr4fUu+tuwJk5luBnwNfbJ99rj/QIWvEx7GI2An4BPDezDy1Z7vLzS6CiNie6g2+X2ae2xYtmAN2RRkLEfFU6jLp13uXYo+I5akvoI+lrvpcD7wbeJFtOSVp3iJiA+DRPZ9rdwKfy8w3RMSbgPWACzLza23/R41czl5jz0R8nIuIHYBTqQ4qX+06noniob7kqP8iYkuqfdZPgLN6r+5ExJrUjP5XUiPg/+MCVZI0f21l6B9Ty9V/jWo+cDVwama+NSLeQFsAMDPP6i5S9bI0ZZzLzHOpZdUv7zqWiSQzvwm8Efhk65iiPhsuQcnMi6j++E8Hdo2IJw7vk5l/zMxvZeYrgTeZhEvS/LUr5b+kuoK9CHh8q/t+CrB3RHwoMz9Jlalc2l2kGskRcS3R2mI+v7f0ob9G9LJdKjPvj4i1qdZZVwJnDI+M97bckiSNXlusbjrVdergzPx0RKwM3EwtWveuLuPT/2UiLmnMRMQhwDOpOvBjgduB1wO/Bs7urRmXJD203tWGhwc7hrdFxPOA7wAvB75PtUGe7qDT+GNpiqQxERH7UT3CX0/N0t8nM39KLVS1ObBdayspSZqHdkVxz/bz0iOS8GdSiwC+EPgUcBXwcJPw8clEXNJYWY5aKXM/4F7gtRFxGjAL+ADwtcyc2WF8kjQo7gbeEBGXAP/VSvoeaJPh/wd4Qmb+ANgQeEZm3tJlsHpoU7sOQNLE8xAtNqdTE4V+kJk7tv0eBgy1kXFJ0jz0THq/OSI+CPw38KvMnN3TAvaYtnr0FFffHv+sEZe0WI2YmLk3taLp0sBnqBXdrgf2BfYC3gK80A8LSRq91orwKdSaGJ8DzgLeCyyXmXe63sjgMBGX1BdtAYmdgaOAbwKHAmdSK5veRS1p/7rMvLqrGCVp0ETELsCrgfdn5s8jYl3g68CJwO+BKzLzj13GqNGzRlzSYhcR04BnAc+nuqScB3wD2DYzX0At2LODSbgkzVtbJbPXM4E1gdUjYtnMvBZ4CbVi8VGAk94HiCPikhZZbxutdnsacDbwV+BhVOI9HTi5JeKSpPkYUer3HODWzPxDRBxGdZs6hhoBv6/ViE/OzNu7i1gLykRc0mITEdsA/wCuAXYBPgY8NzOvjIgDgD2AbTLzng7DlKSB0mrCDwEuA/6WmQdHxLup0fGPAT+z69RgsjRF0kKLiGdHxLnt5z2pSUOHUb1rbwSOAC6IiJOBN1A14SbhkjRKEfFSYDdgCyCBzSLiM5l5DHAttTbDlA5D1CJwRFzSQomIjYDfAL+iVsi8HDgSWAHYFVi73V4amAb8JTNv7CRYSRoQEfFUYBIwqV1NfB3wAHAu8CbgAqpDys3UypmPzMwZHYWrReSIuKSFdTRwfGauSy3K8yLgtrZM/bnU7P2PAlMz82cm4ZI0bxGxHfBVahDjlIjYDfgB8GdqpcwrM/O71Pvro4A1TMIHm4m4pIV1IDA1IjbKzM2BO6gWWmTmFVQy/lPgb51FKEkDIiJeQE2+3AvYGzgZeA1V5vddqkZ8Vls9c11gd9sUDj4TcUmjFhGbRMRT2s0/A3+g2maRmU8B1o2Is9rty4HjMvPmToKVpAEREc+nRsJfnpm/yMw7qIGMe4EpmXk/1Sf8MGpy5oGZ+dfOAtZiYyIuaVQiYnXgS8DXImJ3YB1qeeUXRsRBAJkZwCYRcVq7fV9X8UrSAFm6/f9JPdt2oxLxfwFk5qeoblQ7ZOavxjY89YuTNSWNWkR8jKoFP5m6NPpT6pLp+4FjM/M3bb8nZOYfOgtUkgZMROwIHAe8lXp/3QR4WesR7pL1E9TUrgOQNL5FxMrAHa1H7RHU7P3rqMuoXwKeTLXOWo/qooJJuCQtmMz8VkRMokpQ7srMJwFExFKtNEUTkKUpkuYqIiZFxBrUqPebI2LzzPwnNXForcz8PbAlcDW1auaxEeHSypK0kDLzm9QEzSkR8aK2zSR8ArM0RdI8RcRO1OptT6f6134N+A5wdGae2UZwlgJWcmKmJC26VqZyKrB/Zn6163jUPybikuaqtyYxIh4JPBb4AnASsBywEXCQybckLX4RsS3w+3b1UROUpSmSRuP2Nkt/c2B5aiGJHYENO41KkiaozPy2SfjE52RNSf8WEU8D1snM0zNzTkRMBsjM2RGxHjAT+ACwIjAHyO6ilSRpsFmaIolW5z0F+Dg16v3+zDyr5/4tqLZa+2XmZd1EKUnSxGJpiiQyc05mzgKuAP4BbB8R+wBExCrAq6nk3CRckqTFxNIUSb1mAHcAPwE2i4hHUyPl78/MG1xUQpKkxccRcUm9fgZcmZmfA/4GvAOYmpk3QI2cdxibJEkTiom4pF6zgTUiYldgW6pV4doRsXu3YUmSNPGYiEvq9TeqFOVI4NDMfDNwHnBJp1FJkjQB2TVF0n+IiPWpVTJ/0G5PbRM5JUnSYmQiLmmunJgpSVJ/mYhLkiRJHbBGXJIkSeqAibgkSZLUARNxSZIkqQMm4pIkSVIHTMQlSZKkDpiIS5IAiIhdIuKHc9l+ZETs1UFIkjShTe06AEnS+JaZ7+k6BkmaiOwjLklLsIg4EtgDmAFcBzwauAFYEXgC8C1gFeAq4E5gp8zcqf3uOsCFwJrA2sBxwErAFOD4zDx5LJ+LJA0aS1MkaQkVETsDLwM2ADYBVui5++GZuV5mvr1n25eBzSJi1Xb71cDngEnAGcA7MvOZwJbAWyPi2X1+CpI00EzEJWnJtTXw9cy8KzNnAb0j2JeM3Dkz7wK+DrwqIqZQI+mfpUbDnwCcHBFXABcBywBP72/4kjTYrBGXpCXbpJ6fZ/X8fPdD7H8i8P+Aa4FrM/P6iHgqcEdmbjC8U0SsAtyxmGOVpAnFEXFJWnKdD+waEY+IiMnAnvP7hcy8lEre30Ml5QAJ3BMRrwKIiMdQNeXP7EvUkjRBmIhL0hIqM8+jylEuA37G6EewTwQeD5zd/p37gZ2BfSPi18AFwBGZ+ePFHbMkTSR2TZEkSZI64Ii4JEmS1AETcUmSJKkDJuKSJElSB0zEJUmSpA6YiEuSJEkdMBGXJEmSOmAiLkmSJHXg/wP9bMI514MJTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counts of common wheel drive\n",
    "mostf=vehicles[\"drive\"].value_counts()[:10].index.tolist()\n",
    "print(mostf)\n",
    "vehiclesf=vehicles[np.isin(vehicles, mostf).any(axis=1)]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Measure of most common wheel drive')\n",
    "sns.histplot(x = \"drive\", data=vehiclesf)\n",
    "t = plt.xticks(rotation=45)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring numerical features by range and distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x936 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(6, 2, figsize = (15, 13))\n",
    "sns.boxplot(x= vehicles[\"UCity\"], ax = ax[0,0])\n",
    "sns.distplot(vehicles['UCity'], ax = ax[0,1])\n",
    "\n",
    "sns.boxplot(x= vehicles[\"cylinders\"], ax = ax[1,0])\n",
    "sns.distplot(vehicles['cylinders'], ax = ax[1,1])\n",
    "\n",
    "sns.boxplot(x= vehicles[\"UHighway\"], ax = ax[2,0])\n",
    "sns.distplot(vehicles['UHighway'], ax = ax[2,1])\n",
    "\n",
    "sns.boxplot(x= vehicles[\"combE\"], ax = ax[3,0])\n",
    "sns.distplot(vehicles['combE'], ax = ax[3,1])\n",
    "\n",
    "sns.boxplot(x= vehicles[\"co2TailpipeGpm\"], ax = ax[4,0])\n",
    "sns.distplot(vehicles['co2TailpipeGpm'], ax = ax[4,1])\n",
    "\n",
    "sns.boxplot(x= vehicles[\"displ\"], ax = ax[5,0])\n",
    "sns.distplot(vehicles['displ'], ax = ax[5,1])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unique model counts increased over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "with plt.style.context('ggplot'):\n",
    "    plt.plot(vehicles.groupby('year')['model'].apply(lambda x: len(set(x))), '.-')\n",
    "    plt.title('Unique models over the years')\n",
    "    plt.xlabel('Years')\n",
    "    plt.ylabel('Unique Model Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change in Ucity over the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Change in UCity over the years'}, xlabel='year', ylabel='UCity'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Change in UCity over the years')\n",
    "sns.scatterplot(x='year', y='UCity', data=vehicles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='displ', ylabel='UCity'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bivariate analysis of ucity and displacement\n",
    "plt.figure(figsize=(18,7))\n",
    "sns.scatterplot(y='UCity', x='displ', data=vehicles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='make', ylabel='UCity'>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x='make', y='UCity', data=vehicles1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(x='year', y='barrels08', data=vehicles)\n",
    "plt.title('Annual Petroleum consumption in barrels for fuel type 1 dropped over the years')\n",
    "plt.ylabel('Annual Barrels consumption')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some additonal exploration for visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotvar(consider, style='ggplot', size=(15, 7)):\n",
    "    linestyles=['.-', 'o-', '^-', '-.']\n",
    "    cycle_length=8\n",
    "    with plt.style.context(style):\n",
    "        plt.figure(figsize=size)\n",
    "        count = 0\n",
    "        for name, group in vehicles.groupby(consider):\n",
    "            temp = list(sorted(list(Counter(group['year']).items()), key=lambda x: x[0]))\n",
    "            x, y = [i[0] for i in temp], [i[1] for i in temp]\n",
    "            linestyle = linestyles[int(count / cycle_length)]\n",
    "            plt.plot(x, y, linestyle, label=name)\n",
    "            count += 1\n",
    "        plt.xlabel('year')\n",
    "        plt.ylabel('Count')\n",
    "        plt.title('Evolution of \"{}\" over the years'.format(consider))\n",
    "        plt.legend(bbox_to_anchor=(1.3, 1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "plotvar('drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'City MPG trend for fule type 2')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zKcw2nWMns237dyzL+jfAp4GfA/7zzONl4F2zx1mW9fvAh4HfXO97DwzkWzvYNQwNFTp6vnbo9mvo9vFD919Dt48fuv8aunH8Y1M1evtcenMpAPr7cut6XRCEuH64pdeswFVERDatUvcpVT1688m2nSMeN6k6PuWaR0+2fefZaSzLugVI27b9uG3bVcuy/orGftfZ548Cb7Vt+8MzDxmA18w5xsfLhGFnqk0ODRUYHS115Fzt0u3X0O3jh+6/hm4fP3T/NXTr+C9eK1J3A0LPp78vx+TU4p0jKytVPa73JNs6eWyaxoqToQpcRURkU8Io4upYpS0pwoulkzFuTFQpZBJtW9ndgU4C/9KyrPtpVBV+D40V1Vk14Pcsy3oQuAD8EvCJTg9SRETaK4wiilWP/Cba1QVBiBlv//1+OdrjKiIim1IsO9Qdn2SThR42IpmIUXUDak7Q9nPtFLZtfw74LI19rI8AD9m2/VHLsj5nWdZ9tm2PAr9AI4XYprHi+vtbNmAREWmLuhNAFG1q4ncre7lqxVVERDYsCEOujlfJpjt3OzFozBrL+tm2/bvA7y567F3z/vxx4OOdHZWIiHRS1fE2na20lb1cteIqIiIbNll08IOQeJP94ERERKSzpssuycTG79db3ctVnzRERGRDoihidKpGroOrrSIiItK8IAypOj6JTUw0b3UvVwWuIiKyIXU3wAsiYjHdSkRERLazuhsQRWwqVXire7nq04aIiGxIpebRZP9yERER2QKVmsdm55ljMYP6Fq64tjW/y7Ks3wHeP/PlZ23b/mfznvtl4L22bb+xnWMQEZH2mCg5pJNbUxJfRERE1m+q7G66+n/MNPGDgDCK2trLdSVtW3G1LOutwNuBu4G7gHsty/rBmeduA/6Xdp1bRETay/EC6l6gokwiIiLbnOeHON7m9rfOFwRbky7czk8cI8Cv2bbt2rbtAc8BRy3LSgH/D/DbbTy3iIi0UaXmYagjjYiIyLZXd32IWrdC6vlb8wGgbanCtm0/M/tny7JuopEy/FrgXwMfBl5s17lFRKS9JksOqaRWW0VERLa7UtUj1sKdPX64NSuube9hYFnWy4DPAh8AjgNHbdv+ny3LeuNG3m9gIN+6wa3D0FCho+drh26/hm4fP3T/NXT7+KH7r2E7jd/zAxJjVQZyyaaqE/b35Vpy/ljCYWAgT08u2ZL3ExER2cmKFYfUJve3zjIA1wta8l7NandxptcCHwd+1bbtj1qW9WHgZZZlPQ7kgf2WZf25bds/st73HB8vE4adWZ4eGiowOlrqyLnapduvodvHD91/Dd0+fuj+a9hu45+uuExNVwn99VcW7O/LMTlVacn5S1WP8WwCp5rY1PuYptHxyVAREZFOcr0AN4hIp1qTJRWLGTsvcLUs6wjw18CP2Lb9AIBt2z8z7/k3Ar/bTNAqIiJbb6pUJ5lQmrCIiMh2V3MDiFq36BczDeruzksV/nUgDfyBZVmzj33Qtu0PtvGcIiLSRkEYUqx65DOtv32MTtUY6EljqjmsiIhIS5QqbsuqCUNjxdXxtqaXazuLM/0K8CurPP9l4I3tOr+IiLRete4TRVFTe1vXMlly+JvvXOKFy9O8/s4DvPHuQy17bxERkd0qiiKKVbelPde3spdr24sziYjIzjFdbt3Mre+HfOPpa3zjqREMw2BvX4ZvPXudV922j0xKtycREZHNcLyAIIzakskUBCFmvIWlitdBnwxERGRdwihiuuK0JKg8c2WaL3z7EpMlh9uO9/P2Vxyh7gZ88JPP8M1nrvHmew63YMQiIiK7V83xoU01bT0/ItHhSFKBq4iIrEvN8QlCNjVzO1V2+JvvXMa+NMVAT5qffPvNnDzYA0BPDm473s93nr3Bq2/bRza9uarBIiIiu1mx4ralmGIURVvSy1WBq4iIrEup4m64gbnvh3ztyRG+9sQIhgFvvucQr3nZPmKxhTfUN9x1kGcvTPLQ09d5631adRUREdmIMIoo1Txy6daHe6axNS1xFLiKiMiaoihiouRsqMDDxWslPvftZxidrHHrsX7e/orD9OZTyx471Jfh9hN7+O7zN3jN7fvIadVVRESkaY4bEIatLaY4a6t6uaoRn4iIrKnuBvhhRMxs7rZxbaLKn37xDFEY8eNvvYn3venUikHrrNffeQA/CHno6WubGbKIiMiuVal7bav6u1W9XBW4iojsImEYUa17Tb+uVHOJ0dwNsFr3+dgDZ8mkYvyT99/N6cO963rd4Oyq63OjlGvNj1VERGS3K1a8tuxvha3r5arAVURklwjCkIvXS5y5Mt10QDhZdEkl13/LCMOIv/rqeUpVj/e96RSFXLKp873+zoMEoVZdRUREmhWEIZW617L2dYvFTBPfDwmjNpUsXoECVxGRXcAPQi6MlKjUPLLpOBevlXDWuT/F8QJczyfexA3wgUeHOX+1yLtec4zDQ/mmxzvQm+aOkwM8/PwNSlW36deLiIjsVnU3gIi27G+dYxj4fmfThRW4iojscJ4f8uJIkbobkM8mSMRNTBMuXS8RrKOcfaXmQRNpws+8OMFDT1/jPmuIu28a3PC4X3fnAYIw4qGntOoqIiKykiiKCMMIPwhxvYBS1cXoQJTnB51dcVVVYRGRHcz1Al4cKRGGIbnMS7/yM6k45arH8GiFI3vzq87KTpbq604Tvj5R5VPfuMCRvXne8cojmxr7np40d54e5GF7lNfcvp+eJtONRUREdpJq3Wd0ukoQNDKpwigiCBtBKwBRBIZBFEVtaYMz31b0ctWKq4jIDuV4AedHioRRSGaZG1g+m2Cy7DA6XVvxPTw/pFr3SSbWboNTc3z+/IGzpJMx3vfGU0t6tG7E6+44QBTBN7TqKiIiu1yx6jBddgnCANOARNwgm4pRyCYa/+SSFLIJenLJltyDV7MVvVwVuIqI7EB11+fc8DREEZnUyrOuhUyCkbEqxYqz7POVusd6EoHCMOLjX3mpGFM+25r+q/2FFHfdNMCjL4xSrGivq4iI7F41JyCdjJGIx4jHTWKm2d59rKvYil6uClxFRHaYmuNzbrhIzDRIrxK0ApimQS4T59L1MnV3aWn7qZJDah2rrXPFmF59dEPFmFZz/x0HiICvPznS0vcVERHpJlXHJ97mldT12opertvjykVEpCUqdY9zw9Mk4gap5NoBJ0A8ZpKIm1y8VsYPXroJ+UFIqbZ2H7jZYkz3WkPcffPQpsa/nL58irtPD/LomTGmy8uvDIuIiOxknh8SBiGmuTUrrIvFY2bHe7kqcBUR2SHKNY/zV6dJJc117UmdL5WM4QchV26U5/qy1ZzGDWm1NKTrk41iTIf35vh7myzGtJr779iPAXxNq64iIrILeX5AMxX+2800G+1w5gpDdeKcHTuTiIi0TRRFXLpeIp2Mk4g3F7TOymXiFKseNyaqAEyXXeKxlW+SNcfnYw+ca2kxppX05lPcffMgj58ZZ1p7XUVEZJdxO9wzdV0MY0GmVrspcBUR2QFcPyQIQhLxzf1aL2TjXJ+sMVGsM11ZfX/r154cYbrs8r43nqKQbX+rmvtffgDDgO88e73t5xIREdlOanWf2CqTyVsi6mwvV/VxFRHZARwvgBZUFjQMg3wmzpUbZTCMFffShGHE0+cnuPlIL4f3trYY00p6cknutYb47vM3CDqYmrQTWJb1r4D3AhHwJ7Zt/8Gi5+8C/hjoAb4K/KJt253dvCQiIiuqOh6J+PYKXCMirbiKiEhzKlWPWIsKNsRiJpl0nExq5VvEhWslyjWPl58caMk51+st9x7iPfefaNm17gaWZb0BeDNwB3Af8D9ZlmUtOux/AL9s2/bNNDZR/VxnRykiIiuJooiaG2ybisKzTMPA9TvXEmd7Xb2IiGxIqeaR3GSa8HyJuLnqXtmnz4+TSsQ4fbi3Zedcj0Q8xokDPR09Z7ezbfsrwJtmVlD30si2qsw+b1nWMSBj2/a3Zh76CPC+To9TRESW5/khUbR6scStEIsZOK4CVxERWSc/CHHcgHgLA9dVz+eHPHdxiluO9W16T610hm3bnmVZ/xJ4FvgSMDzv6YPA/HLNI8DhDg5PRERW4fohRNtvi0zMNHC8zqUKa4+riEiXc7yATt7OzlyZxvGCjqcJy+bYtv07lmX9G+DTNFKB//PMUyYs+BEygKY+iQwMdGaf86yhoUJHz9cO3X4N3T5+6P5r6PbxQ/dfQ6fGb0zV6K169OZSLX/v/r7chl8bhBF11+/Y34MCVxGRLler+3Ryy+dTL46TS8c5vr+7P3DsFpZl3QKkbdt+3LbtqmVZf0Vjv+usK8CBeV/vB642c47x8XLHevkNDRUYHS115Fzt0u3X0O3jh+6/hm4fP3T/NXRy/MOjZSpVl9Brbc28/r4ck1OVtQ9cRanicr0ntWIxx2aZprHiZKhyvEREulyp5pFMdObXed3xOXN5mttP7GnZTUra7iTwIcuyUpZlJYH3AF+ffdK27YtA3bKs18489FPA5zs/TBERWU6l7pPYZoWZ5nSwl+s2/RsQEZH1CKOIcs3r2F7T5y5NEYQRt59SmnC3sG37c8BngceAR4CHbNv+qGVZn7Ms676Zw34C+HeWZT0P5IF/vzWjFRGR+cIowvGC7dfDdVYHe7kqVVhEpIu5XqOaX6cqDT59fpw9hRQHB7IdOZ+0hm3bvwv87qLH3jXvz08Ar+zsqEREZC3eTPGj7VZReFYne7lqxVVEpIt1sgx9qery4kiJ20/u2bY3UBERkZ3EC0I6WoGxSabZuV6uClxFRLpYqeoR71D60DMvTgBwu6oJi4iIdETd9dnOc8Uxs3O9XBW4ioh0sVLN7VhhpqfPT3BgIMtgb7oj5xMREdntanWfeHzrItdojf6x8VjnerkqcBUR6VKeH+D5ITGz/b/Kx6frXB2v8vKTe9p+LhEREWmoOFtXUXhkvMIf/PkTnBueXvGYmGlSd1vbpmclClxFRLpU3Q06ttf0qfPjALzsRHsC1yiKqFR9KtXO3PxERES2uyAMGxPUWxC4lqouH/3SWSp1n6vj1RWPM02DIAg70stbgauISJeq1n06sNhKFEU8fX6C4wcKFLLJlr93teZTrnns6U1hmHSsOqGIiMh25vnhltRl8vyAj37pLHU3IB4zKVXc1V9gdObercBVRKRLlWouyQ70b706XmWi5PDyFhZliqKIat2jVPXozSexjvRzYCDH/oEs1XrnKiWLiIhsV64f0undrVEU8ddfu8DIeJUfesNJ9vSkKFa9NV5kKHAVEZHlBWFIzWnMhLbb0+fHiZkGtx7ta8n71RyfYtWjkGkErIeG8iQTMQD6cikScQPP16qriIjsbnXXx+xw5Prlx6/y3MVJ3nrfYawjfRSyCUrV1VdcG71c2782HG/7GUREpOUcN4Qoavse1zCMeObFSW463Es6tblbRt3xcfyQvlyK4/szZJZ5P9M0ODCQ49L1Eol4a9OSRUREukm15hPvQGbVrKfOj/O1J0a466ZBXvOyfQD0ZJNcn6it+rpO9XJV4Coi0oVqrr+hoHW64pJJxuZWONdy4VqJcs3bVJpwEIZMluokEzGO7ushm1791tOTS5JOxnG9YN3jFBER2Wmqjk862Zn74OUbZT719Qsc25fne199dO4zRiGboFzzCMKVuxh0qperAlcRkS5UrrkkmpiFDcOIh56+xpcfv0pfLsn73nSKfXuya77u6fPjpBIxTh/u3dA4wzCiXPW585ZB8NdXMdg0DA4MZDk/UlTgKiIiu5IfhPhBiGm2P1ybKjt87IGz9OSSvO9NpxdUMe7JNbKfylWP3nxq2dc3erm2P3DVHlcRkS4TRRHlqrfuwHWiWOcjX3ieBx4d5qZDvbh+yJ989nmeODu26ut8P+S5i1PccqyvqSB5/jhLVZcDg1mG+jNNvTafSZBLxTtyIxQREdluvA4VZnK8RgVhP4j4sbecXpIVVcgmAFYt0NTo5aoVVxERWcT1Q8IowlyjYkMURTx2Zoy/+c5lTMPgB19/gttP7KFS9/mrr5znk1+/wOUbZf7eK48uu4fmzJVpHC/YUJpwFEWUKh57+7MM9TYXtAIYhsH+gRznr06T0qqriIjsMm4HihSGYcQnvnqe0akaP/62mxjsW3q/7plpg7dagab5vVzX+myyGQpcRUS6jOMGRGsU7ytXPT790AXOXJnmxIEC33//CXpn0n3ymQQ/+fabefCxYb7x1DVGxqu8942n6C8sTAF66vw4+UyC4/sLTY+xVG30Zd2/J7vhAlL5TIJ8JkHd8TddGEpERKSb1ByPWKy9a66f+cZ5Xrg8zTtffZRTB5ffEjTbv71YWaMlzkwv16TZvslmpQqLiHSZcs1btQ3O8xcn+eAnn+HFkSLveOURfvLtN88FrbNM0+At9x7mR95ymsmSw4c+/SwvXJ6ae77u+Jy5Ms3LTvQ3PXtarnr0ZJMcHMhvuurx/j05HLXGERGRXaZab2/Lu8fPjPHgI1d4xS17ecUte1c8LpOKETONNVvidKKXqwJXEZEuU655JBNLf307bsAnv/4iH3vwHL35JD/3fbfxqtv2rRo8Wkf6+Lnvu42+fIqPfuksX3rkCmEY8dylKYIw4vYm04SrNZ9MKs6RffmWpAtl03H6cilqzvoKO4mIiHS7KIqoOatPUm+G54f87Xcvc/pwH+945ZFVjzUMg55cctU9ro0DIWxzK1flXomIdBE/CKm7/lyVv1kj41U+9sBZilWX++84wBvuPLCgKuBq+gspfuZdt/CF71ziG09dY3i0gheE7CmkODiwduXhWTWn0W/u2P7CiiXzN2Jvf4YzVxzSyfb3rRUREdlqfhC1db/osxcmqLsB73j1sXWdo5BNrL3i2gEKXEVEukjdDZYN3h549Ap+EPIP33kLR/bmm37feNzk3d9znCN783z2mxfxg4jX33lg3YGi4wYQwfH9hZbPEGdScfoLKUpVl2w60dL3FhER2W48P4A21hR+2B5lsDfNqUO9TE1X1zy+J5tkeKzStvGslwJXEZEuUnd8FseSQRhy+XqZO04PbChone/O04Ps35Plm89c515raF2vcb0AP4g4dainbX1X9/ZlmSw5RGtVpRIREely7WwFd228yvBohXe88si6J6cLuQTFiy5RtLWZT9rjKiLSRYpVj+Si1jUj41VcP9xQ9d/l7NuT5Qded2KukuBqfD/E8UJOHOghnWzfXGgqGWOwN0O1rr6uIiKyszW23rQnQHzEHiUeM7nj1PprWPRkkwRhtOX1JhS4ioh0iTCKqNQ9EosC14vXSgAc3deawLUZFcfn+P7Ckobl7TDYmyYMI626iojIjlZ1/LYUZnK8gKfOj3P7iX4yTbSZK2Qb23TWLNDUZgpcRUS6hOM2VhsXp+lcvF5msDdNPtPZ/Z+OG1DIJNa1MtsKyUSMof703N+DiIjIThNFEXU3IN6GHq5PnRvH9cN1bwWa1TPXy3VrCzQpcBUR6RJ114dFi41hGHHpeoljLUoTbobjBQz1ZTp6zoGeDNkOB+giIiKd4vkhUbR0knqzoijiYXuUAwNZDg7mmnptYaaTQUkrriIish7lmk9i0Z6XaxNVXC/kWIfThH0/JBE3yXU4iEzETQ4NZEm0sSm7iIjIVnH9ENqwJebKaIUbkzXutYaaDorzmUZa8Va3xFFVYRGRLlGquqSSCwO2CzP7W4/t31w14WZVHZ/DQ3nMLagu2FdId/ycIiIineB6y7e926xH7FGSCZPbT+xp+rUx0ySfSWjFVURE1ub5AUEYETOXFmYa6El1bJ8pNNKTTcOgJ9e5c4qIiOwGlbrf8v2t1brPMy9OcOepgQ23revJJrTHVURE1lZ3gxX2t5Y7vr+15gQM9qbbUvFQRERkN6u1oaLwE2fHCMKIe5osyjRfIZvUiquIiKytUvcwF02SXp+s4nhBRwPXKIoIwpB+peuKiIi0VBhGOF5ArIUrrlEU8cgLoxzZm2dff3bD71PIJShu8R5XBa4iIl2gXPNIxhdGrrP9WztZmMlxA3pzKVLJjaUaiYiIyPI8PwRaW1H4xZESE0WH+zax2gqNljh1N8Dzt64lnQJXEZFtLghCak5AIr64MFOZPYVUR/eaun7IYJ9WW0VERFrN9ZduC9qsR+xRMqk4tx7r39T7FLKNLgLFLUwXVuAqIrLN1dyls5tR1Pn+rZ4fkE7GyaZUkF5ERKTVHDfAaGF0Vqq6PH9pkrtODxCPb+6NZ4tAlrawQJM+fYiIbHOVmsfipKHrkzXqbmf3t9ackKN7c20p0y/tY1nW7wDvn/nys7Zt/7Nlnv8ZYHLmoQ/Ztv2HHRyiiMi24Qchnh+S2YJJ2kZhptbdYx87M0YUwb2bTBMG5rK7tnLFVYGriMg25vkh1ydqZFIr7G/tUOAahCExEwpqgdNVLMt6K/B24G4aCWhfsCzrB23b/sS8w+4DftS27W9uxRhFRLaTqZLDVNnl9OHejp+74vgkWlRROAwjHn1hjJMHe9jTs/ktPj0zqcKlLSzQ1NbAdblZXsuyfh74JzRuoA8Dv2Db9taWqBIR2aauT1YgFicWW9q/tS+fpLdDgWStHrB3T2ZJH1nZ9kaAX5u9z1qW9RxwdNEx9wG/YVnWMeCrwK/btl3v7DBFRLZeGEWMTtXw/BDPD0jEO1eIMAgbK73pZKIl73dmeJpixeUdrzzSkvdLJmKkErEtbYnTtk8gi2Z57wLutSzrnwMfAL4HuGPm/L/UrjGIiHSzSt1jvOiQzyy8iUVRxMXrJY53aLU1iiIiIvrzqY6cT1rHtu1nbNv+FoBlWTfRmEz+3OzzlmXlgcdo3JvvAfqA3+r8SEVEtl617uMFIRiNnuWd5HphS9/vEXuUQjbBzUdat3Lck0tQ3KF7XJeb5U0D/9i27eLMY0+xdOZXRGTXC8OIKzcqZJKxJXtKb0zVqDmd299acwL68qmOzjxLa1mW9TLgs8AHbNs+M/u4bdtl4F3zjvt94MPAbzbz/gMD+RaNdH2Ghjq3t7tduv0aun380P3X0O3jh+13DdOXp9g70BiTmYivOb5Wjn+q5NBbdunNbX6SeHy6xrkr07ztVccY3LP6GPv7cut+3/6eDFXHX/Y1ZtxhcDA/V8SpHdoWuNq2/czsn+fN8r529oZpWdYQ8MvAP2zXGEREutVEsY7j+cu2url4rQx0rn+rH0QM9GQ6ci5pPcuyXgt8HPhV27Y/uui5o8Bbbdv+8MxDBtB0Htj4eJkwbHEPhxUMDRUYHS115Fzt0u3X0O3jh+6/hm4fP2y/a3C9gEvDk+SzCaIIRifKZBMG5goFCVs9/htTVUrFGqHnb/q9HnzkChhw69FeJqcqKx7X35db9fnFMkmTkbH6sq8pVT3GxhLUM5tLdTZNY8XJ0LYXZ1pulteyrEPA54E/sW37y828n2Z1m9ft19Dt44fuv4ZuHz901zXUXZ9LY1UOH+gjZjZumPNnN6+OV+kvpDhxZE/bx+K4AflChmNHNtf/Dbrre7BTWJZ1BPhr4Eds235gmUNqwO9ZlvUgcIHG9p1PLHOciMiOVqy4GIYx8w8QRdQdn2y6NXtO11Kt+ZtuWQON3u+Pnxnj5sN9La+D0ZNNUq55hGGEaXa+w0C7izMtmeW1LOsW4G+Af2/b9u83+56a1W1Ot19Dt48fuv8aun380F3XEEURF6+VqDo+od+YdZ0/IxpFEWcvT3H68OqzqK1SrLgcP9Cz6b+/7fg9WG1Wdwf5dRrbdP7AsqzZxz4IfD/w27ZtP2xZ1i8AnwaSwNeBpu/NIiLdbLYo0/wK/oZpUK55nQtcHZ90cnNbcsIw4qFnrlOp+y1pgbNYYWY1ulzzls0Ia7e2Ba7LzfJallUA/hb4Tdu2/3u7zi0i0q2KFZfpqrviLOnoVJ2q42+oMFMURY2AOIgwYwbpRGxJteL5/CAkETOXFIeS7mHb9q8Av7LMUx+cd8zHaUwyi4jsSrNFmTLpl0KjdCLGZMllb3+27ed3vIAgDDHNjYVmYRjx1PlxvvbECBMlh2P7C5w61NPiUb7UEq9UdXdW4Mrys7x/DuwDfs2yrF+beexTtm3/dhvHISLSFfwgZHisQm6VpucXrzffvzUMI6p1nzCKGOzNUMgmqNQ9JksOfj3AMCCVNJcUX6o5AQcGsivu7xEREdkJxqZrJBel6cbjJtWqh+sFJBPtKU4YhCGTRYdrE9UNtZtbHLDu35PhR958ipuP9C0p7NgKL/Vy3ZqWOO0szrTSLO+/btc5RUS62ehUjSCIiKdXvnldvFaiJ5ekL7/2TGcQhlTrjXL+Q31pBnrSc8FpIZtkX38Wxwso1xpBbKniEgGpRIxE3MQA+tQCR0REdjDXCyhVXPLZpdlFRhRRrXstD1zDKGKq7HBtvEoQhOQyiab2jHY6YJ3VM1MxuFjdmpY4bS/OJCIia6s5PqNTNQrL3Dhnze5/PXmwZ9Ubk++H1NyAmAEH9mTpK6SIL5MSbBgG6WScdDLOYG8Gzw+o1DymKy7TFZfB3syyrxMREdkp5hdlWiyRMJmuuPQV0i05VxRFlGoeI2NVXC8gk44RT68/HNuqgHVWNh3HNA2KlR224ioiIusTRhHDo2WSCXPVG8/4dJ1K3V8xTdjzA2pOQCJucmgoR28u2VTqUSIeo68Qo6+QJghDDJQiLCIiO9dyRZnmSyVilKqtqaJbrXuMjFcp1z2yqRiFXHP1I6p1j//yOZvxYr3jAesswzAoZBKUtOIqIrI7TZUcqnWfnjXSfy9ca+xvXa4wUxRF1OoBx/YXKOSSm96XupG9NiIiIt1kuaJM8xmGQRhBzfXJbbC6sOMGXJuoMlVxSCdiG25Rc+bKNOPFOu+5/zh3nBroaMA6X08uSXGL9rjqk4mIyBby/ICrYxVy2bXnES9eL1PIJugvLN136vohhVyS3nxKxZRERETWYbmiTIvFYmx4hdEPQs5dnaZS9+jJJkhtot3N8GiFZNzk5Se3LmiFRkucrVpxVeAqIrKFro1XMQ1jzRXO2f2tx/YXlr1huW6wroJNIiIi8lJRprWCyXQixlTJ2dA5psoOQRCSTcc3HWwOj1U4OJjbdMryZhWySUpVjyiKOn5uBa4iIlukVHWZKDlk0mvPwE4UHco1j2P7lt/fGhlGx5qki4iIdLvVijLNF4uZeH6I4wZNvX8QhtyYqJJtovjSSjw/5PpEjUNDuU2/12b15BJ4fki9yb+PVlDgKiKyBTw/5PKN8rpnYWf7ty63v9UPQlLxGKk29ZkTERHZSdYqyrSUQaXe3L7O6bKLH0bEWlCdf2S8QhhFHBrcBoHrTEucrejlqsBVRKTDoiji6lgFIkissbdm1oVrJfKZBHt6lu5vdZQmLCIism6zRZnWG1QmkybT5fXv6wzDiOstWm2FRpowsC1WXGfb9m1FL1cFriIiHTZZcpguO2Qz67uhze1v3ZdfdnU2CFm1/6uIiIi8ZD1FmeZLxk3KdQ8/CNd1fLHi4AVRy3qhD49W6MklKWS3fpJ6bsW1osBVRGRHq7s+w2PldVURnjU2XadU9Zbt3xqGEYYB6aS6m4mIiKxlvUWZ5jMMgyiK1rWvM4wirk1Uya47DXltw6OVbZEmDPNXXJUqLCKyY4VhxOUbZRIxs6k+qeeuTAEsG7i6XiNNeKurDIqIiHSD9RZlWiweMymuY5WxVHFx/ZB4Eyu6qynXPKYr7rZIE4ZGsapsOr4lLXEUuIqIdMiNqSo1xyedam519NzwNLl0nMHe9JLnvCCiN7d036uIiIgs1HxRppekkiZTZWfVNjBRFHFtYmPvv5Lh0e2zv3VWz0xLnE5T4Coi0gHlmseNiVrTe1GjKOLslakV+7cSRWSaDIRFRER2o2aLMs0XM02CIMLxVk4XLtU8HM8nEW9h4DpWxjDg4EC2Ze+5WYVsYl2rz62mwFVEpM38IOTy9RKZdKzp1KSpsstUyVm2f6vnB6RT8XVXJhYREdnNmi3KtJhhQKXuL/tcFDUqCaeb2Du7HsOjFfb1Z1oaDG+WVlxFRHag2dY3YciGbjqP2KMAHD+wNHB13JD+gtKERURE1lJzfIpNFmVaLJkwmSo5yz5XqfvUHJ9kC3uqNz5DVDk0mG/Ze7ZCIZeg6vj4/vqqLLeKAlcRkTaaLjtMlh2ymeZvZJdvlPnmM9d49e37GerLLHk+BHJptcERERFZTRhFDI9WSCbMpjOf5kvETap1f0lbnNnV1s0ExcsZm67jeMG22t8K81ri1Dq76qrAVUSkTRwv4MpohXw63vSN0vMDPvn1F+nJJnnP604teT4MI+Km0fKUJBERkZ1mquRQrXubbh03ey+vOgvThauOT6XukWrhaitsz8JMML8lTmf3uSpwFRFpgzCKuHKjTCxmbKgIxAOPDDNRdPj++48vW4W47jba4Gxm5lhERGSn8/yQkfEquUxrChnG4wbF8sKA7fpElWSi9WHV8FiFVCK2bFeBrVSYXXGtaMVVRKTrjU3XqNT9DVX8vTBS5NvP3eCVt+7lxIGeZY/xg3DuxiEiIiLLuz7ZWLXcyCTyclKJGMWKQxg22uJU6z6l2uZXc5czPFrh4GB2201S9+S04ioisiMEYcj1iRr5DczuOl7Ap75xgT2FFG++59Cyx0RRhGEYaoMjIiKyikrdY7zokE23LoXXNA38MKLuNtKFR6c2V6l4JZ4fcH2yuuVpwmEYLdnTm0rESMRNSgpcRUS6m+s1fsGbZvMzpF/87mWmyi7ff//xFSsTun5IPp0g3qLZYxERkZ0mDCOu3KiQSTbfim4tMcOkWPWoOT7TFact9SZGxqtEEVteUbhU86guagFkGAY92UTHW+LoU4+ISIu5fghR1PTrzg5P8+gLY7zm9n0cXaZv69z7uyG9eaUJi4iIrGSi1KjI28r2NLOSSYPJ6RqjUzXisc1VKl7JdijMVK37ZFPLF5gs5JIUK1pxFRHpatWa1/Remrrj8+lvXGCoL82b7lo+RfglkdrgiIiIrMD1AkbGqhvasrMeiXiMSt1nquKSSbWnuv/wWIXeXJJ8Zmvu90EYEoYRBweXD5x7skmtuIqIdLuK45Focr/LF75zmXLN4z33nyC+ymv9ICSRiLW8V5yIiMhOMTJeJRbb2Jad9YqZBomY0bbCScOjlS1dba3UfA4O5hr1NJbJIivMpApHG8gw2ygFriIiLRSGETUnIB5b/43MvjTJk+fGuf+OAyvObM5yZtrgiIiIyFKlqstU2SHb5sykQjbZtiKJ5arHdMXdssC15vjkMwn6CylMw8CMmXNVlGf1ZJOEUUSl5q/wLq2nwFVEpIVcPwBY9wxste7xmYcusn9PhtffcWDN44MQtcERERFZRhCGXBktt7SK8FYYHisDcGiNyex2aFQRjjg0mJ/7LJOImYSLVlYL2c63xFEvBRGRFnK9sKm0mc996xI1N+An33HzmvtiG21wINOGXnGy/VmW9cNA3bbtz1qW9U+BNwHfAn7Ptu3OTXmLiGxTY1N1fD8kk+ruCd4roxUMAw4MdD5wLVc9Dg3lF2xJSsbNxsT8vPmAQq7xd9zJfa769CMi0kJVx1t3mvAzL07w7IVJ3nTPIfb1Z9c83nEDenPJtu7Zke3JsqzfA94IJC3L+ofAAPBfge8H/k/gV7dqbCIi20Hd9bk+Wd2yYkatNDxWYV9/tul6GZtVc3xymQT9PakFjyfiJjV34fxoz8yKayd7uSpwFRFpoUptfYWZpisun/vWRQ4O5njt7fvX9d6uH3Ign1r7QNmJ3gXcCWSAYeCwbdsly7L+DHh0tRdalvU7wPtnvvysbdv/bNHzdwF/DPQAXwV+USu4ItJNoiji6liFRNzs+sndMGxcy8tPDnT8vH4QcuJAD+ai7U6JxNI9rrl0AsPobKqw9riKiLRIGEXU3ID4Gim/QRDylw+eIwgjfvB1J9Z9kzWAbJvK7su2Z9i2HQCz/1QBbNv2WGUS2rKstwJvB+4G7gLutSzrBxcd9j+AX7Zt+2YaP2Y/1/LRi4i00XTFpVz1Nl0sqVL3+M+fepYzV6ZbNLLmjU3Xcb2w4/tbyzWfAwM50stsR0rGY4ThwsdM06CQSVCqbKNUYe2pERFZH88PZ/ahrh6I/u13rzA8VuF9bzzFQG963e+dTsVJxBW47lJfsSzr74Ac8G3gw5ZlfZjGSupjq7xuBPg127ZdAMuyngOOzj5pWdYxIGPb9rdmHvoI8C+B/9TyKxARaYMoirg2XiGb3nwi6Re/e4VrE1UesUe56XBvC0bXvOGxCkBHKwrXHZ9sKs6enuU/k8RMozGtuUghm9w+xZm0p0ZEZP1cL4Bo9aD1qfPjfPf5G7z6Zfu49Xj/+t/bDdm3J7PZIUr3+mXgp2iUxvgI8LvAf6SRJvzLK73Itu1nZv9sWdZNNALd18475CCN4HbWCHC4RWMWEWm7St3HnZnc3YwXR4o8eW6cbCrOueFpHC8glej8ZPHwaJlUIsbgOie2NysMIzw/5PgyKcKzYqYJy9Sd7MklGZ2qtXmEL1nrO7zhPTUiIrtNzfUxV8kSvjFZ4zMPXeTovjxvufdQU+8dRCG5HVBwQjbGtu2QxsQxlmXlgX9t2/Zvr/f1lmW9DPgs8AHbts/Me8pk4ccRA1iUELa2gYF8sy/ZlKGhQkfP1w7dfg3dPn7o/mvo9vFDa66hNDzF0EB+U31bPT/kC99+hsHeNO99y8188K+e5OpEnXusvau+rr+v9aui1yZqHDtQYE9/Z36vxpJxbjuyh/17Vi4SWXd8xiouvbmFdTYG+7OcHynS35fDjDsMDubb2rJvrcDVsG07sCxryZ4ay7JU2ElEZJ5KzV+xMJPjBvzFg2dJxk1++A0nG7OX6xSEEfGYSTqpNOHdaiZY/TfAT9AoohRZljUJ/DXw67ZtT63y2tcCHwd+1bbtjy56+gowv4HwfuBqs+MbHy8vKdzRLkNDBUZHSx05V7t0+zV0+/ih+6+h28cPrbkGzw+4cHmSfDaBU994yuqXHxtmdKrGT7z9JoYKCfKZBA8/e40T+1YOTPv7ckxOVTZ8zuW4XsDIeIXXHjzQ8vdeTjqTwqt7GL6/6vfCD0Kmp6uE3sJdoslY4/PNtRtFXD9kbCxBfZOT7KZprDgZutYnp9k9NQ/w0p6aN1iW9YesvqdGRGRXiaKIat1btjBTFEV86hsXmCg5vPeNp5qejay7Pn355Jp7Z2VH+xOgRCMLKgEkgXuBG8B/W+lFlmUdoRHc/vgyQSu2bV8E6jPBLTTSkT/f0pGLiLTJdNnFMIxN3R/Hpmp846lr3H5yD6cO9mIYBrcc6+Ps8HRjC1AHjYxXiaLO7G8NwwjHCzg0lF8xRXhWzDRYbpNrz0wv107tc11r1XRDe2pERHYbzw8JI5atEPztZ6/z3MVJ3nrfYY7tby4tKooiPC9gQG1wdrs7bNv+kUWPXQR+w7KsZ5Z7wYxfB9LAH1iWNfvYB2nUqvht27YfprGK+yHLsnpo3N//fUtHLiLSBmEUMTpVI7OJavtRFPHZb14kETd5+yuOzD1+67F+Hn5+lHPDxabqUWzWbGGmwx2oKFyt+9x8sI/kcptXFzEMg7hpEIbRgs85sxPxpaq3bDXiVlv1DJvdUyMislu4/vLbAi9dL/HFh69gHe3jNS/b1/T71uoBhw/lyStNeLdzLcs6adv2+fkPWpZ1ClixF4Ft278C/MoyT31w3jFPAK9s1UBFRDqhUvPwgpDMJqoJP3F2nIvXy7z7e46Rn5fiemxfgWwqznMXJzsbuI5W6Msn217TIghDDMNgb3+Wqcn1pSQnEibBosC1J9sYZ6nqMtTX/gKSa1UV3vCeGhGR3aTu+EuSaMo1j7/88nn68ynec//xplOZ/CAkAo7sLTA9VW3ZWKUr/QbwTcuyvgNcplFQ6RDwKuBntnJgIiJbYWyqTjKx/noRi1XrHl98+ApH9ua5+6bBBc+ZZiNd+OnzE/h+SHyF+hWtNjxW4UgH0oQrNZ+Dg7kV63IsJxE3qTsLU6dnV1yLlc6kCq812g3tqRER2W0qdW/BDSAMIz7+lfPU3YD3venUhlJoKnWfQ0M5kltQjl+2F9u2Pwu8DPh/aaQIXwH+gkYKsfakisiu4ngBpZq7qXY1X/zuFRw34Htfc2zZieVbjvXj+iHnRoqbGeq6laouxYrb9v2tnh+SiJvsKTTXbicZixFGC9OKE3GTTCpGqbpi4k9LrfVJaqN7akREdpVK3V9Q9feBR4e5eK3ED7zuBPtWKTG/kmrdpy+XojfXvrLy0l1s2x4D/mz2a8uyYsD7LMv6p7Ztv2rrRiYi0lnTZYeYYW64KNOFkSJPnBvntS/fz97+5VNcT+wvkE7GeO7CJNaRvk2Mdn2GRxspu4eG2tsGp+b4HN1XWLYmx2pmU4UXK2STFDsUuK614upalnVy8YNr7akREdlNPD8kCMK5m4B9aZKHnr7GvdYQd5waaPr9giAkiuDgYFaVhGUJy7L6Lcv658AFGntV/2ZrRyQi0jlhGDE2VSOd3lj6rh+EfPabF+kvpHj9nQdWPC4WM7GO9PHC5SmCoOn21k0bHqtgGsaq/VQ3y/ECMqn4hibFE/EY0TJ/DT3ZBKVtUlV4tT01P93msYmIdAXXD2BegPmVx6+yty/DO155ZJVXraxS8zmyL08irhRheYnVKAv8qzSq/V8AMsAx27ant3BYIiIdVa55+EFEtol+6PN946lrjBcdfvxtN615n73leD9PnBvnxZESpw/3buh86zU8WmHfnkxT+06bVXcCTh3q3dCkeMw0luuIQyGbZGS8M3U4Vv2bWWNPzRfaPzwRke2v7gZzv8tdL+D6ZA3rWN+yPV3XUq17FHJJ+tT+RuaxLOuzwFdpZDu90bbt24GSglYR2W1Gp2qkNtgCZ2y6ztefHOH2E3s4fWjtQPTUgR6SCZPnLk5u6HzrFYYRV8cqbd3fWnN8enLJBdWTmxEzDYiWSxVOUKn7y6YRt9qa1UJs2x6zLOu9wH+ybfuLbR+RiEiXqdY84vFG6Hp1pnn44Q3cfIIwJAzh0GBOKcKy2D3AI8DTwNmZx9r/KUFEZBupuz6VukfPBlJdV+rZupp43OTmw308f2mK731N1PS+0PUam67j+iGH2tS/tdETPuT4/o2nIa+04jr7vajU2r+LdL3LAR8H/oVlWS9YlvXrlmXtaeegRES6SXleReHh0TLAhm4+5Zny9KoiLMs4AnwE+DFgxLKsv6CRKiwismtMlZxGALUBT54b5+K1Em+59zD57PpXHW893k/N8bl4vbSh867H3GeHNhVmqtYDBnrTZFIb73kbixkQLf27n22JU94ugatt239q2/YbgO8H9gLftSzrv1uWpYblIrKr+UGI54fEzNnAtcKeQopsurlUnJrjU8gk6C8oRViWsm3bt237Y7Ztv4lGW7oRIG1Z1hnLsn5xi4cnItJ2QRgyNl3fUPA1Nl3nC9++zOG9Oe65eXDtF8xz+lAPiXh704WHxyqkkzEGelr/GSAMI8IwWrF68nqZhoFhNFZv5+uZmQTYNoErgGVZJnATcDONFOMbwB9ZlvUv2zQ2EZFtz/UCZnNnoijiymjze1SCMMT3Iw4N5ZUiLGuybftZ27b/CY1iif8W+PktHpKISNuVKi5hRNPpuo4b8LEHzhKLGfzw6082fZ9NxGOcPtTL8xenlgRt6xVFEY4bMFVyGBmvcP5qkWdenOARe5SvPznC2eEiB9u0TahS92eKPm0um8swDOJxk3DRXtZOrriua8rCsqz/jUYV4fPAHwHvs23bsywrB1wCfqd9QxQR2b4c76XCTMWqR7nmNZ3qU6n6HNqb31QjddkdLMv6OI2aE39n23YV+M8z/4iI7Gij03XSyeaKHkZRxF9//UXGi3V+6u0307vBwoe3HuvnuYuTXL5R5ui+wrpf9+Cjwzxij1Jz/eXqGs2Jx0xuu7N/Q2NbzWyrvj096Za8XzJuEoQh8z+tZFIx4jGDcgd6ua53rX0v8C7btp+Y/6Bt2xXLsn6s9cMSEekO1bo/V5jppT0q619xrTk+OaUIy/r9FfBblmX9EY2A9cO2bU9s8ZhERNqq5vjUnICeXHPbcL725Aj2pSne8cojHD/Qs+Hz33Skl5hp8NyFyXUHrg8/f4OvPTnC6cO97N+TIZOMk0nFyaRipBf9uV0tcCr1gMNDuQ11OVhOIhbD9QPmfxcMw6CQTW6fFVcgtjhotSzrL23bfq9t23/bhnGJiHSFSt2fu+FcGa0QMw32r3MfSRhG+EHIyQO9mEoRlnWwbftPgT+1LOsW4Gdo1Jx4CPgPtm1/Z2tHJyLSHpOlOs1mur5weYovP3aVl5/cwytv3bup86cSMU4d6uW5i5O8/ZVH1kzpfXGkyBe+fZnTh3r40Tefbls14tV4fkAyYba0vV4iYRDWlm+Js+WBq2VZ/4nGHprXWZY1NO+pBHCynQMTEdnugjDE8QIKM4UJhkcrHBjIElvnzGap5nFoMEcqqRRhWb9Vak581rZtbd0RkR3FD0LGiw75zPqLMo0X63ziqy+yf0+Wd3/P8ZbsHb3teD8vXJ5ieKzC4VW2BE0U6/zll8+xpyfFD73h5JYErQBVJ+DEgZ6Wnj8Zjy3br7Unl+TS9XLLzrOStX4C/gS4HbiTRkucWT7wrXYNSkSkG7heONeMOwhDRsYr3Ltgjm9l1ZpPXy7FQIv2ncjuoJoTIrLbFCsuURStO/h0vEYxJtM0eP+bT7UsDffmw72YpsFzFydXDFzrrs9Hv9Rotf2jbzlNOrnx9jOb4bgB+XSCQqa51Oq1xGPmsh3EC9kElZq34eJV6z7/ak/atv0w8LBlWV+0bXu4rSMREeky8ysK35is4QfRqrOwszw/AIO2VRCUHU01J0Rk14iiiNGp9bfAiaKIT379Rcam6/zk229uaZpsOhXn5IECz12Y5K33Hl7yfBhG/NVXzjNRdPiJt9/UsoJI6xHMtObz/AhoBPlH9hZa/hkjFlv+/XpzKYIwou4GFLItPeUCa6UKf8y27fcDX7Asa0kIbdv2HW0bmYjINlepe3OFma6MVgA4NLh6YaYoiqjVA04e6m1bMQbZ0VRzQkR2jZrj43g+Pbnkuo7/xlPXeP7iFG97xWFObKIY00puPdbPpx+6yLWJGnv6F05U/90jVzg7XORdrz7alnPPCsJGkOr7EVEEERHJuEkukySfiZNKxEklzbn+8q0UMw1YJhi+8/QApsGGeuw2Y613/zfz/vubwC8Ct858/QttHJeIyLZXrvkkZvazDo9WyKXj9OZXv7mWqj5792TItzh9R3Y21ZwQkd2oVPUawdI6nLkyzQOPDnP7yT28+rZ9bRmPdbSPz3zzIs9dnOC2Uy/9Kn78zBjfeuY6r7hliPtu2VwhqNVUaz4YUMgkyPYlyCRjJBOxllUNXstK34tUIoZ1tPXtfBZbK1X4kZk//kMafeO+YlnWt4E08PeBj7Z3eCIi21MYRjiuT36uMFOZQ0P5VdNyao5PLh1nb38b82hkp1LNCRHZdUo1l2Ri7aBsoljnE189z77+DN/3Pcfatg0nm05wfH8jXXh2P+el6yU+882LnDhQ4B2vPNqW887yo4ibDva2fWVzJTHTZNWGtG223qsetG373wPYtl0H/i/Lsv5B+4YlIrK9uX5AZBgYhkHN8RkvOtx5enDF4/0gJAgiDh/Mq/WNNE01J0RktwnCkJoTrFlN2PUCPvbAOQwD3v/m0ySa7ZvTpFuP9fO5b13i2niVes3hYw+eoy+f5L1vPNXWCsJhGBE3IL2FnQhM08A0jaaKZbXSegPXuGVZB23bvgpgWdY+ZiuSiIjsQvMrCg/P7m8dWn5/axRFVOs+x/YVSCXU+kaap5oTIrLbOG5AFLFmgPTAo8OMTtf4ibfdTH+hdcWYVnLL0Ubg+t3nrvHM+XGCIOJH33y67augjhfQk0tteVHHxExLnPgKhZraab1/w38APG5Z1hdoFEF+K/CBto1KRGSbqzre3C/t4bFG4HpwhcJM5ZrPQE+a3hZWN5RdRzUnRGRXqTo+ay1gjhfrPPz8KPfcPMTJg+0riDRfPpvg6L48Dz5yBcOAH3vrTQz2Zdp+Xj+I1l2kqp0ScbPRHWEL5uHXtZPXtu0P0whWHwMeBt5h2/aftXNgIiLbWaXmzVUFvjJaZm9fZtnVVMcNSCVi7B/QvlbZuOVqTgAfodG39e9v0bBERNqmVPVIrLG/9YFHhonFDN5w58EOjarhZcf3APD2Vxzh9KHezpw0irZsb+t8ybhJEG7NPtd1X71t208CTzbz5pZl/Q7w/pkvP2vb9j+zLOutNFZwM8Cf27b9L5p5TxGRrRZGEdWZfTdRFDE8WuHWY0ur6QVhiOsFnD7c15ay9LIrqeaEiOx4YRRRrnmr7m+9fKPMcxcnecNdB+cKJXbKfbcMceupQXKJzqTLen5IKhnfFm30EgmTcIsKNLXt6mcC1LcDdwN3AffONEf/MPAeGilOr7As653tGoOISDt4Xgg09t1MFB3qbrDs/tZK1efQUH5bzJDKjhG3LGtuaUE1J0RkJ3LcgIiV97dGUcTfPXyFfCbBa17WntY3qzEMgyN7Cx3bb+p4AX2FrU8TBkjGY0Th1py7nZ+mRoBfs23bBbAs6zngZuCMbdsvzjz2P4D3AZ9v4zhERFrK8YO5P8/ubz08tLAReaXq01tIdaRQhOwqqjkhIjte3fUxVlnUsy9NcflGme99zTGSu6DoYRRBPr09+r+bpsFWNcRp24qrbdvP2Lb9LQDLsm6ikTIc0ghoZ40Ah9s1BhGRdqjVfWYzf6+MlknGTQZ703PPh2FERMShwdyWV/+TnUU1J0RkNyhXPRLx5e+fQRjypUeuMNib5u6bVm5Dt1PM9otNJ7dH9lbcNNiqyLXtfwOWZb0M+CyNGWGfxqrrLINGMLtuAwP5tQ9qoaGhQkfP1w7dfg3dPn7o/mvo9vFDa69hsuozFI+TSsa4NlHj2IEeBva89Lup5vgMDcY5sL91BRv0PZBZG6k5ISLSLaIoolTzVuxX+tgLY4wXHX7kLafb2jd1u2i0wUlsm2uNmeaWbVBpa+BqWdZrgY8Dv2rb9kcty3oDcGDeIfuBq8285/h4mbBDlayGhgqMjpY6cq526fZr6PbxQ/dfQ7ePH1p7DVEUMXx9mlw6znQx4upohdfcvo/JqcrcMaWqR2oo17Jz6nvQHqZpdHwyVEREVuf6IUEQYppLwxTHC/jK41c5ui/PzYfbU823WvcJwohChws+rcTzQvb3b4/9rQCxmDHXx77T2ha4WpZ1BPhr4Eds235g5uFvN56yTgMvAj9Oo1iTiEhX8PxwriH6yHiZMIo4vExhJhVkEhERad5sYablPPT0NSp1nx99y+G2bMUpVz0yqTg1xycMo+2xymkYZLbJ/lZoTPqyRdug2vnJ6teBNPAHlmXNPvZBGj3oPj7z3OeAv2zjGEREWsr1X9rdMFuY6dDgS6t2YRgRM41le7qKiIjI6so1l3hsaRmeUtXlW89c52XH+zk01NpsmSiKKFU9erJJjuzLc32iylTZJZve2kloPwhJxM1t9ZnCNAxiprElgX3bvhu2bf8K8CsrPH1nu84rItJOdcefm2gcHq3Qm0su6B/negGFbEJFmWRbsSyrB3gIeLdt2xcWPfc7wM8AkzMPfci27T/s7AhFRBqKVY9kYmng+uXHrhKEEW+651BLzxdFEaWKx57eFAcH85iGQW8+xdi009LzbITjBQz0pNc+sMOSCXNnBa4iIjvRdMUlOdMAfHi0siRN2PNDerLbZy+KiGVZrwI+xMLiiPPdB/yobdvf7NyoRESW8vwQzwtIJxfeR29M1nj87BivvGUve1oYyIVhRLHqsb8/w7492blJ50wqTjxmEIRhoxjRFgmDiHxm+32miMdMHC/oeCC5dd8JEZEu43oBlbpHMhGjVHWZrrgcWry/1dg+JetFZvwc8EusXAzxPuA3LMt60rKs/2hZ1vab3heRXcHxlt/f+qVHrpCMx3jdnQdbdq4gDClVXQ4P5tg/sLB9nWkY7OlJUXeCVd6hvaJGQQ0yqe2TJjwrGTc7Vix3PgWuIiLrVKp5GDM14IdHZ/a3Di3e32oum+IkslVs2/5Z27a/ttxzlmXlafSE/QBwD9AH/FbnRici8pJKzSO2KP30wkiRM1emuf+O/S3bc+r7IZWqz9F9BQb7Msse05tLEWxRv1JorD7n04ktXfFdSTIeI9iCwFXLAiIi6zQ+XSc9M/N5ZbSCaRoc2JOde97xAnqySe1vla5h23YZeNfs15Zl/T6Nav+/2cz7qMd687r9Grp9/ND919Dt44el1zBWdtk7VCAZb9xrwyjiwc/b9OVTvP01J+Ye3wzHC6g7PrfdNERvfuUEkyiKmKr7JGMx4vHlg8f+vqVdBVpluuJwdF8PQ/3LB9atspGfo3gqQdWP6M2n5h4z4w6Dg3kKbdwupcBVRGQdao5P3fXpyTV+IQ+PVdi/J7PgZub5IYXc9tuLIrISy7KOAm+1bXu2NZ0BeM2+j3qsN6fbr6Hbxw/dfw3dPn5Yeg1BGDJyo0Q+E5+bAH76/ASXr5d4z/3HqZTrVFZ6s3Vy3AA/iDhxoIBb8xitrf7rLhFFjNwoLijCOKu/L7egh3urlSoe9UKSUd9v2zk2+nNUrnlMT9cI542tVPUYG0tQz2yudc9qPda339qziMg2VKq6c+k6YRhxdayyoA0ONHq7ppPbby+KyCpqwO9ZlnXCsiyDxl7YT2zxmERkF3LcAGb6pEOjFcwDj15hX3+Gl58c2PT7V2s+YRRx6lAP2XX2RS1kk429ph0WhCGx2PZtrRczDdiC5DIFriIiawijqJEmnGz8yhydquH54YKKwrM3meQK6UQi24llWZ+zLOs+27ZHgV8APg3YND6K/P6WDk5EdqWq42PO22pz/mqRqbLLG+8+tKm2K2EYUZzpyXr6UF9TBRRTyRjpVBzP72yRJtcL6ctv361Hje+H9riKiGw7dcfHD0IyM0UhrswVZnopcHW9kL7c9r3JiNi2fXzen981788fBz6+FWMSEZlVqnokEgsD13jM4NTBng2/p+cHVJ2AA4NZBnszCwLj9RroSTM8ViHRgv216+X7Eflt3FovZhoQdf7zjgJXEZE1TJUdYrGXfkEPj1XIpOL0F14qSuBv015rIiIi210YRZRrHvnMS6HJ+atFju0rrFgYaS21uk8EnDrYS34T+y4L2QREEVEUdWxyOiIim9q+YdpsqnAn/05AqcIiIqsKw4iJorMgtWh4tMyhoYU93yLQ/lYREZENcNxGKu7sfXW64jI2XefkBlZboyiiWPFIJWPcdHhzQStAIh4jn0ni+uGm3me9PD8gm44Tj23fMM0wDBJxk7DD+3+379+IiMg2UKl7hFE0t7+m7vqMTtUX7m8NQhIxg+Q2LaIgIiKyndVdf8GWyfNXiwCcPNRc4Or7IcWKx97+NMcP9LQsvbe/J4XrdSZwdd2Ivlxq7QO3WCJmdKya/KztuwYtIrINTBSdBQWXro5VATg0+FLg6swUURAREZHmlZfZ35rPJNjbt/4epjXHJwzh1KHNr7IuNvt+nUiNDaJw3VWPt1IyHqPieHRypFpxFRFZgR+EFCsuqXkpwMOjZWBh4OoH27uIgoiIyHYVRRGlmkdyZnU0iiLOXy1y8mDPuoPEUrXx+lakBi8nHjPpzSXnUprbJQwbGV7p1PbP4EokTMLOLELPUeAqIrKCctUlYuHs6pXRCoO9adILiiZEZLS/VUREpGmuHxKE4dyWnJHxKjXHX/f+1rrjk0nGOXGgp61bdvoLqbbvc3W9gN5sckPVjzstEY91PFVYgauIyArGi86CgktRFDE8Vlmw2hoEIclErKNl8kVERHYKxw2IltvfemDtwDUIQ7wg5PDe3KZ6va5HLp3ANNq7r9P1Q3ry239/KzRWoSMVZxIR2XquF1Cpewtmb6fKLtW6v6B/q+OF9ChNWEREZEPKNZfEvFoS568W2defIZ9dO+W3XPM5OJhfUPm/XUzTYE9Pinqb04W7JYMr1uaJguUocBURWUax6mKw8Jfy3P7WofzcY0EQrevmKiIiIksVK+5cEUTXC7h0o7yuNOFa3acnm2RPoXMrlL35FH7QXLqwH4TrWpn0/ZBUIt41HQoavVw7G7yqqrCIyCJRFDE+7ZBZVBzhuYtTZFJx9vXPr3Ko/a0iIiIb4fkBnh/O1Y24eL1EGEZrtsHxg5AwgsOLeqq3WybV6K8arKMqURRFlKoe8bhJEEREUUQibpJKxJZNa3a8gMHe9VdR3mrxWOdXXBW4iogsUncDXC+gkHtpJbVUdbEvTfGq2/bO3XD8ICSZiGt/q4iIyAbU3WDBqt35q0XiMYOjewsrviaKIio1n+P7Cx2//5pGI114bLq+6nGeH1CrB+ztz7C3P0tERM0JmCo5TFccghBiMUgnY8TMxmpzENJVGVyz4+4kBa4iIotMV5wls6GPnxkjjCLusYbmHnO9gD2FdKeHJyIisiNU6h7z45/zV4sc3VdYsOd1sWotYKAnRe8WFTHqzaW4Mbl84BpFEdVagGnCyQX9ZA3yGZN8JsHBKEfN8SlVXCZKDn4YEMPANCDTgb26rWKajQ1VnehtO6t7/nZERDogjCImig7p1Es3zTCMePSFMU4cKDDQ81KgGoSQa0O/OBERkd1gtv8qNPa6jk7VufP04IrHe34jKNw/kFvxmHZLJ2OkEjG8Ra1xgiCkVPMZKKQ4MJgjHls++DYNg1w6QS6dYN+eLHU3oFRziSLaXhm51ZKJRkucWIfShhW4iojMU3N8/CAiO28K+NzVaaYrLm97xeFFR0cdqWQoIiKy0/hBSM0J6JnZljPbBufUCoWZoiiiWg84dah3xaCwEwzDYKAnRc3x5h6rOT5BEHF8f4G+JlaCDcMgk4qTSXXnZ4l4zMQPAmIdythWVWERkXmmy86SggOP2GPk0nGsI31zj81W/1stnUlERESWV3f8BV+fv1okl46zt3/5AkXlqsfe/sy89NutU8gmiaJGRlax7JKMx7jpSF9TQetOkEyYbe1ru5g+cYmIzAjCsJEmPK9KcLHicubKFHfdNEhs3gyv4780SywiIiLNKdd9ZjNjoyji/NUiJw/2LLtf0vECkomVg9pOSyVjZDMJilWPg4M5ThzsIdUlbWxaKREzCdbR6qdVunNdWkSkDSp1nzCKFuwxefSFUaII7rl5aMGxYRCRzyQ7PUQREZEdoVhySCYaE8LXJqpUHZ9Th3qXHBeGEY4bcNPhvi2pZLuSo/sK5OJG16b5tkIirhVXEZEtMVmsz91EoXGzfOzMGKcO9dC/pMG5sWBlVkRERNYnDCPKNXduu83s/tYTB5a2wSnXfA4O5LZdgJjLJLbdmDotHjfp4IKrAlcREQDPDylWvAWpPmeuTFGqety7aLW10Sw9tqXFIURERLpVseIQRcylBZ+7WmRvf4ZCdmEmU93xyaXj7OlV67ntKG4aHWuFAwpcRUQAKNdcIhb2InvEHqWQTXDzvKJM0Ojf2pNVmrCIiEiz/CDk6niVXLpRJ8LzAy5fL3NymWrCnh+yb08Ws4PBkaxfp9v3KHAVkV0viiJGp+oLUn6mSg5nh4vcfdPgkl/MYRipf6uIiMgGjE/XCcKI+Eya8MVrZYIwWtIGJ4qimXYx2pazXcVMk07mCitwFZFdr+YE1F1/QWubR8+MYhhw96I0YYBI+1tFRESa5ngBN6aq5NIvTRSfu1okZhoc3bdwf6vjBvTkktuqIJMsFIsZoFRhEZHOmSzVF+xXDcKQx14Y46bDvfTmFqYEe35ANq39rSIiIs26MVHFNI0FmUznr05zdF9+SV901492XV/UbmMaBjHT6FhlYX3yEpFdzQ9CJkvOglQk+9IUlbrPvdbS1VbHC+nJaX+riIhIM6p1j4myQ3betpxS1WV0qr6kDU4URRhANr27q/Z2g3iscy1xFLiKyK5WrLhz+2hmPWKP0ptLcurgcv3kmCsoISIiImuLooirYxXSidiC++1sG5zFhZlcPySfSSi7qQsk4yZBh/a56qdBRHat2aJM6XmzvxPFOi+OlLj75qVFmYIgJBHb3c3GRUREmjVdcak4PqlF9SHOXS2SS8fZ159Z8LjrBvQVlN3UDZIJrbiKiLRd1fFxvIVFmR55YaYo002DS4+vBwz1ZVSWX0REZJ2CMOTqWIXcoknfMIo4f7XIyYM9S3qBRii7qVsk4zEFriIi7TZRrC8IWv0g5Ikz41hH+5Y0QY+iCIyI3rxmgEVERNZrvFgnCMK59jezRkYrVOv+kjRhzw/JpOIkE6re3w3icZMw7My5FLiKyK7k+SFTJXdBW5vnL05SdZYvylR3A3pzKRJx3UhFRETWw/UCro/Xlu19/vylCWDp/lbHDegvqJpwt4iZaocjItJWxaoLBkuKMvUXUpw80LPkeM8PGehNd3KIIiIiXe36ZJXYovY3s+yLk+ztyyyT4QT5ZQJd2Z5ipgkdil0VuIrIrhNFEaOTVTLzVltHp2pcvF7mnpsHl+y18fyQVCK+oIS/iIiIrKxa95ksOmTSSzOVPD/gxavTnDy0cKK4kVJskFKacNeIxTq34qpPYSKy61TqPq4fLqgm/OgLo5imwV2nlxZlqjk+h4fySwJakW5hWVYP8BDwbtu2Lyx67i7gj4Ee4KvAL9q27Xd6jCKyc0RRxMh4hWTSXPbeefF6GT+IlqQJ192Awd6M7rddpJEqHNGJZVetuIrIrrO4KJPnhzxxdpxbj/Yt2Ycz2+O1J6eiTNKdLMt6FfB14OYVDvkfwC/btn0zjU8eP9epsYnIzlSsuFRqHunk8mtk568WicUMju3LL3g8DKMlqcOyvTUCV6NRxLLNFLiKyK7i+QHTFWdBUabHzoxSdwPuu2XvkuOrjs9AT1pN0KWb/RzwS8DVxU9YlnUMyNi2/a2Zhz4CvK9zQxORnWa2/U0mvXzQGoYRZy5Pc/Jg74KCh2EYETMN0imlCXcTwzCIxwxCBa4iIq01XXEhMubSkHw/5OtPXuPYvjzH9heWHB8EkaobSlezbftnbdv+2gpPHwRG5n09Ahxu/6hEZKeaLDp4Qbggs2m+h56+xnixzmtuP7Dg8bob0FdIqVd6F0rGzY70ctUeVxHZNcIoYnSqtqBQxKNnRinXPH7o9SeWHO96AdlUnIyKMsnOZdLYnDTLAJruyDcwkF/7oBYaGlo6ydRtuv0aun380P3XsB3HH4QRw5M1Dh/oW7ZNyvCNMl95/Cp33TTEXTcPLdjLapQdThzt76qtOdvxe9CsVlxDyQ0JjAqDg/m2pnrr05iI7BrVuo/nR2RSjVlg3w/5xlON1dbjy7TAqbshR/d19gO5SIddAeYve+xnmZTitYyPlzsy2w6ND1mjo6WOnKtduv0aun380P3XsF3HX655jE9U6cktbWfj+yH/9XPPkknFeeu9hzAMg8mpCtCoJ1Gp+VTLaZyq0+lhb8h2/R40o1XXUC7WmJquMjZWpr7JVkamaaw4GapUYRHZNcanayTjL83uPnpmjFLV4/V3HVxybGOvDRSy6iUnO5dt2xeBumVZr5156KeAz2/hkESki02VHBLx5VN9H3xsmNGpOt/32uNkF+1/ddyAnlyy0RNUuk4i0Znvm346RGRXcL2AYsUlNVOUqbHaOsLRfXmOL7O3teYEDPSmdROVHcmyrM9ZlnXfzJc/Afw7y7KeB/LAv9+6kYlItwrCkKmys2wP1gvXSnzzmevcaw1x0+HeJc+7fkRfXvUkulUiHutICyOlCovIrlCsuBjGS0WZZldbf+B1J5b9ZRtEEf35dKeHKdI2tm0fn/fnd8378xPAK7diTCKyc1TrPmEUYS7a2+q4AZ/6+ov0F1K87b6ltd+iKMKAJauw0j1ipkEnmi9oKUFEdrwwjLgxVSOTWt9qq+MGFNKJudVZERERWd1U2Vm2kvDffPcy0xWXH3jdCZLLrMa6fkg+k1DbuS4WM40lExbtoJ8QEdnxSlUXPwiJzdwUH5tZbX3DXQeXXW11vYDBPq22ioiIrIcfhEyV3QU90gHsS1M8fmaM77l9P0f2Ll9wx3UD+grdU0lYloqZBqbR/rBSgauI7Hg3JqskEy9VEv76KqutQRASj5nkNlkVT0REZLeo1n2IWDAZXKl7fOahC+zfk+GNyxRBnBUBubTuud0sFjOWbX/UagpcRWRHc72A6bI7VyxirdXWmhMw1JdRA3QREZF1mizVSSReum9GUcRnH7pI3Q34gdednMt4WszzAzKp+LIpxNI9YqZJLKbAVURkU0o1D4PGLLAfrL7aGkURERG9eaUsiYiIrIcfhBQr3oJqwk+eG+f5S1O86Z5D7O3PrPhaxw3pL6ia8E4QV+AqIrI5E8U6mVSjUuHsauvr71x+tbXuBvTmUiTimvkVERFZj3LNIyKau69OlR2+8O3LHN2X59W37Vv1tWEUkdfWnB0h2YHPTgpcRWTHctyAmuOTTMQaq61PjnBkb54TB5autkJj/+tAr4oyiYiIrNdk0Zmrwh9FEZ/6+gWiKOI9959YtdKs74ck4uayfV+l+yxXUbrVFLiKyI5Vqrlze1XX2tvq+yHJRJxsSn3kRERE1sPzQ8p1l+RM0PLw86NcuFbiHa86umYKcM312VNIL3tPlu7Tl08tqSrdagpcRWTHGp92SCdjjb6ta6y2Vhyfvf26gYqIiKxXuebOVROOoohvPXudI3vz3HV6YM3XBmFEIauaEjtFJhVvey/eXRm4en4jfTCKoq0eioi0Sd31cXyfeNzk289co7jKamu17tGTTdKXV4EIERGR9ZqYlyZ84VqJyZLDfbcMrTkJ3Gg9Z5BOKU1Y1m9X5sRNl10u3SiTTccZ7ElTyCWVXy+ywxSrLjFM/CDk7757acXV1iAICUM4NJjTaquIiMg6uV5Ape7Rk2usmj72whjpZIxbj/av+rooiijXfE4fHyTy/E4MVXaIXbniCpBMmMRNg2sTVezLU5wbnmKyVMfzw60emohsUhRFTEzXSaVMHj8zxlTZWXG1tVz3OTyUUw85ERGRJpRrHgaN+2q17vPcxUnuODVAfI0iPaWqz97+DIN9K7fJEVlOW1dcLcvqAR4C3m3b9gXLst4O/FsgBjwK/Kxt2247x7CaeNwkP/M/l+sFXL5RBqAnl2RPT5o9gYJYkW5UdwPcICKVNHjo6WscP9Cz7GprtebTl0vRqxRhERGRpowX66RTjc/RT50fJwgj7r5pcNXXVGo+PdkE+/ZkOzFE2WHatuJqWdargK8DN897+E+AH7Vt+3YgC/z9dp2/WclEjJ5ckkI2Qd31uThS4omzo9yYquIrgBXpKsWKi2nAxWslpsour7vz0JLVVt8PiQw4qBRhERGRpjheo15MIh4jiiIee2GMg4PZVQPSuusTj5kc3pufq/gv0ox2pgr/HPBLwNV5j8WAHsuyYkAaqLXx/BtiGAbpZJxCLkEuneT6RA378hTjxTphqGJOIttdFEVMFOtkkjGePD9BMmFy+6mBJcdU6j6HB3Md6TsmIiKyk5SqL7WbuzpW4cZUjbtvGlrxeN8P8f2I4/sLba88KztX21KFbdv+WQDLsuY//I+BLwNF4EXgL9t1/laImQaFbIIgCBkerXBjssqBgRw9uaRmikS2qZoT4AUh8bjJcxcmufVYP8lEjMq8Y6q1gIEepQiLiIhsxGy7OWj0SU/ETW4/sWfZY8MwouL4nDrYO1eBWGQjOlZV2LKs/cD/AdxOI2j9g5l/fqmZ9xkYyG9qHOPTNT7yty+wvz/L/XcepL8nverx/X05AAZpNFmernu4kcfhvXl6csmuSDEcGlq+b2W36PbxQ/dfQzeN/8qNMgP9Oc5cnsLxAl575yHgpf+XXT8gmQ647cRgV622dtP3YCU74RpERHa72XZzPdkkrhfw9PkJXna8f9mgNIoiylWPQ0M58pnEFoxWdpJOtsN5HfC0bdvnACzL+hDwsWbfZHy8vKmU3XLNw3V8HnjkMg88cpnjBwrceWpgblVmvv6+HJNTlSXvMVVzGLleJJ9JcGAgSza9ff9HHBoqMDpa2uphbFi3jx+6/xq6afxhFHHuwgTpVJxvPnWVQjbBnlzj/8/JqQpRFFGsuJw61MfU5NL/t7erbvoerGQ7XoNpGpueDBUR2W1m280BPHNhEtcPufvm5dOEy1WPPb1pBtZYKBJZj04uNzwNvNKyrH0zX78H+G4Hzw9APpPg57/vNv7R997CG+46yHTZ5ZNfv8Af/PkTfPLrL3LhWokoWj0wTiZi9OaTeEHAmeFpLl9f+zUi0n41x8cPoeb6nB2e5uUnBzDNl7IiylWfwd6MZn1FREQ2YH67OYDHXhhlsDfN4aHckmNrdZ9sOsHBARVBlNbo2IqrbdvPWZb1W8CDlmX5wFng5zt1/sV68ynecNdBXn/nAS7dKPPk2XGeuTDBE2fH6csnuePUAG+45yir/W+WTsZJJ2Gy5JBJxxnsVT8qka00XXaIx+DpFyeIIrhjXlEm1wtIJEyV4BcREdmg2XZz6ZTJjckaV0YrvO0Vh5cEpq4XAHB0X37BBLLIZrQ9cLVt+/i8P/9X4L+2+5zNMAyDY/sKHNtX4O+96gjPX5ziiXPjfPWJEb75zHXe+aqj3LVGT6p8NsHVsQrZVIJsupPZ1yIyKwyjxiRSKs6T58bZvyfD3v7GZFIYRdSdgFOHe1XNUEREZINm281BoyiTaRoLJokBgiDE8QJOH+ojEVcxJmkdRVnzJOIxXn5qgJefGmCq7PD5b1/mU9+4wKXrJd756qMr/s9nmgapZIxLN0qcPqQPxiJboer4BGHERMnh6liVt73i8NxzparL3v4MuW28H11ERGQ7m2s3l4rhByFPnhvnlqN9S+6tlbrP0X0FMimFGdJa+olaQV8+xS/+4B389VfO8LUnRrg6XuW9bzzFYO/ym8tTiRjlqse1iQqHVTlTpOOmSg6JuMnD9iiGwVxZfs8PKWRiDBUUtIqIiCzm+QHj03UMwyAWM4jHTEzDwDAaizOm0fjH9Rvt5jJmnGcvTFBzfO5elJXoeAG5dII+tZuTNlDgugrTNHjT3Yc4MpTnE197kT/+9LN832uP87IV+lTlMnHGpx3ymaT+hxXpoCAMmSo7ZNMxnjo3zokDPRSySQDqjs9Nx3Pg+1s8ShERke3nxmSN8ek68bhBGDVWVokADBYXe5ltI/foC2P05pKcPNiz4HnHDTh4YGmhJpFW2J05rUZjFcb3w3UdfvpwLz///bexd0+Gj3/lPJ//1kX8YOlrDcMgn4lz5UYZxw1aPWoRWUG17hNGEZdvVJiuuHP7bRrVvg36CsmtHaCIiMgawijixmSVyzdKy37ObIe66zNerFPIJcimE+QzCQrZJIVckkIuQSG78J9MKs5UyeH81SJ33TS4oCiTH4Qk4iY5Ve6XNtmVgWtfPsXRfQWCEIpVj0rdW7M3bG8uyT/4exavftk+vvv8KB/53PNMlpwlx8ViJrGYweUbm+s3KyLrN1lySMZNnjo/TiJucsvRPqBR/bA3n1RxCBER2db8IOTStRLXxqsUyx4vXJ6iXPPaft7rkzXiMbOpdjWPnx0D4K7TC4sy1eo+e/uzmGp9I22yKwPXeMxkoCfNzUd6OX2wl/58mmrdp1TxcLyVV0pjpsnbX3GE97/pFONFhw99+lnsS1NLjsuk4lQdjxuT1TZehYhA42Y/XXGJxQyeeXGSW4/1k0w0AlXPj9ijpuciIrKN1ZxG7/FK3acnnySXjZOIm5y7Os3IeIUgbM/qa7XuM112yKTWP7kbhhGPnxnj1KEeeudtiwvDCAyD3pwynKR9dmXgOsswDLLpOAcHc9x6vJ9j+wskYzGKFZdy1VsxlfiWY/38/PffRn8hxZ8/cJaHnr625JhCNsH1yVpHZstEdrNq3SeK4MyVaRwv4OWnGnvQgyAkHjPUokpERLatqbLD2eEpDKNRK2VWIm7Sk00wNlXj3HCRmtPaOg1RFHFtokIy3txq67mrRYpVj3tuGlrweM0JGOpNq7OGtJV+umbETJOeXJITB3uwjvazf08Wx/ep1pf/RdFfSPHT77yF247383cPX+HMlakFz88GxZeul/B87XeVhcIowvUCKnWPqbLDtYnKij9rsrrJUp1kwuDJcxPkMwlO7G8Uiqg7IYO9aaUsiYjIthNGESPjFS5eK5FNxUkllq56GoZBIZckiiLOXJlibKpGGLVmG1ql7lOueaSbbFnz2JlRsuk4Nx/pnXssiiKCMKS/oAwnaS8tRSwjlYiR6svQ15/lu09dpVLzF8yCzYrHTd5z/3EminU+8dUX+dl337ogLTERN/H8kOHRCsf2F9ac0fL8EM8PSCVjxEzNKWxnlbrHlRtlgjAiHjNJxk3icZNErPHfeMwkNlNCvlLzKFZc6p6P4wTU3GBhSnoEhtnYp3nzkT5975vgByHFiodpwtkr07zytr2YM53RgyikN6fq3iIisr14fsCl62UqdY+eXGLNz4epZIxE3OTqWIVi1eXwUH5T549mguZ0srn6D+WaxwuXpnnVbXuJzVtZddyAnlySVJPvJ9IsBa6rSMRjnDjQw8VrJcpVj3x2aZW0RDzG+950mg99+lk+9uA5fuZdt8ztrwPIpuMUyy7jxTqDvZm5xz0/xPUD6m5ApeZRrnsEfkhEI+A9srdAXlXZ1s3zw7kS7e0URhFj0zVGxqtkkjHSyRhhGM18LxuVbcMQ5k+IjpVdpqarxMxGb7RYrFF9evGNqlhp/Jzs7cu2/Tp2inLNIyLi2QtThFE0V014to+cbqIiIrKdVOseF66VMICeJvaDmqZBTz5Jte7zwuUpMptou1isuNQcv6nzAzx5bpwwipbp3RpyZF9mhVeJtI4C1zXEYybH9he4fKNEqeJRyC0NJvsLKX7oDSf5sy+e4TPfvMgPvu7EgqAkn01wdaxCFDU24M8GqRgGBhCPG6QSJrGZdA3PDzg3PM1Ab4p9/bmOBGTdzPUCXrgyTSGTYG9/hkyTaS/r5fkBV0bLjZ+DbGJuZW/2vyvpzacI19FDNJ9JcH28Rm8utWzKkCwURRHj03VSyRhPnhtnb3+G/XsaQb/jhuzbpwkAkVmWZf048C+ABPB/2bb9h4ue/x3gZ4DJmYc+tPgYEdm4IAyZLrtcGS2TTsYWLHI0I5uO4wch565Mk47Bvv5sU3tUwyhiZKLaVEEmaNxzH3thlCN78wz2zV+ICcik4mTb9NlLZD79lK1DPGZydF+By9fLlCouhWVmqE4f6uVN9xziwUeHOTiQ49Uv2zf3nGkaZFIxrk9UlwSpy0nEY/TkTKZLHtOVKQ4N5ujNJZv6xbSbFCsuURhRdTxeuOLQn0+xtz9DOtm6H+9yzePi9RIm0JNvT8U80zSIxeDaRJVj+wptOcdOMlF0KNc9/CBkeKzCW+87DDQqG5pGo0CaiIBlWYeA/x24F3CAhyzLetC27WfnHXYf8KO2bX9zK8YostNEUUTdDag6PsWyS7nuEUURuUx801uC4jGT3nyKS8ONeaZmgtepsoPnhaRnFmKiKOIbT11jdKpGEEb4QYgfvPTfYOa/XhBSrLjcf8eBBe9Xc0KO7s3pM6p0hALXdYqZM8HrjTLTFWfZ9Ir7X76fq2MVvvjwZfYPZDm+/6XgIxGPNdVL0jAMctnGrNrF6yV6s0kODuY2PEO3U0UzqbuZVIx43CSdjCjXGgWP+gsp9vZlN5UuOtsM/MZEjUw63vbV79nG3gM9aaWKr6Lm+AyPlSlkEnz1iasA3H6iUU247vrs6Ulrr7DIS94KPGDb9gSAZVl/CbwX+FfzjrkP+A3Lso4BXwV+3bbtesdHKtLFXK9Rx6JYcShVXPyoUQU1mTCX3SK0GaZh0JNLcn2iimHA3r61g9cgDLk2XiWTfulz0RNnx3ng0WF6cslGvY6YQSzWqNWRSZrEYwliscZWp2w6zsuO71nwfjGTZRd0RNpBgWsTTNPgyN485ihMll0K2YW/hAzD4AfuP8Eff+Y5Pv7lc/zc993W9P6BxeIxk95ckmq90Yz6wGCO/kJKlVJn1JwA1w/nquLNVnOOoohi1WVyJggc7Ms0nX7regGXb5Sp1H0K6yie0AqG0VidHx6tcNPh3jXTkHejIAy5dL1MKhHDMOCp8xOcOFCY+3/NDyL6NrH3R2QHOgiMzPt6BHjl7BeWZeWBx4APAGeBjwC/Bfxm54Yo0p3CMGKsWGNiuo4bRBBFJOMm6VS86Xv42HSdh54a4faTA5w82LPm8cZM8HptvAqsHbxOlhz8IJxrE1dzfP7u4Ssc3pvjp995S9Ofc2r1gL39GU0US8cocG2SaRoc2pvHMCtMTNeXBDSpZIz3v/kUf/KZ5/iLB8/xD95ptaSnVTadIAhDhm+UmSw6HBrKtW0vZzeZKjvEY0t/0RqGQS6dIIoipssuE8U6A72ZuaDfNBvHmIaBYbBMoSSHyzcqGAb0LLOvuZ2SiRjFisdEaWFBL2m4Nl7F8wPy2QSXrpeYLDm8/s5G6pLnh6QS8ab37ojscCYwv4eGAcw1Krdtuwy8a/Zry7J+H/gwTQSuAwObq3LarKGh7t9O0e3X0O3jh81fQ93xuTBSpB7A0GBhw5/3wiji648P85lvvIjnhzx+dpxX3raf97z+JNn0yp9B+vtyAPT1RUyVHXzD5MDg8mm7nh9yZaLG4f29cxWBv/ilF6i7Pj/2tjvZ09/c/8NRFGHGXW46MbipbMBu/znq9vFDd12DIp8NMA2DQ4M5TMNgdLpGT3Zh8DrUl+E9rzvBXzx4jr/59mW+93uOteS8MdOkJ5+k7vicuTJFXz7FYG9mbuZstwnCkIlifdXrNwyDbKaxAjtZchifrsHc9yqi8fmt8e+Y2UiPMYxG1b9cOkF8iwpj5TIxro1X6c0lm0ox3+mmyw7jxfrc/tWnzk0Qj5nccqwfgLobcHCFm7bILnYFeN28r/cDV2e/sCzrKPBW27Y/PPOQAXjNnGB8vEwYtqa/5FqGhgqMjpY6cq526fZr6Pbxw+avYbrscPlGmVjMIJOKU/I21ot9uuzwyW9c4MJIidOHe3nnq47y6AujPPT0NZ45P8a7Xn2MW4/3L3ldf1+OyanKSw9EEc+eG2Visrzsyuv1iSqTkzX8mcn44dEy33xqhFfdtpdMwlj4XutQrfsUsgmmp6rNX/SMbv856vbxw/a8BtM0VpwM3Z0RTwsYhsGBgSyGAaOTtSUrr7ce6+e1L9/PN566xsHBLHffPNSyc6dTcVLJiErdY6rskkvH2dufIZdJ7KoU4krdbxTiWUcqjmEYy/binRVFEVHUmPWMokaJ+q0MfmKmiWmEXJ+scriLZsLayfUCroxWyKYbKfp+EPLMhQluOdZHKhGb+R5G9GS110Zkkb8DfteyrCGgAvww8PPznq8Bv2dZ1oPABeCXgE90epAi3SAIQ65P1BidqpHLxDe8yhpFEU+eG+cL375MFEW8+3uOcfdNgxiGwVvuPcxtx/fwmYcu8BdfPsctR/t456uPUljl/mYYBj3ZBNcmagDs68/NPef5ATcma3Ofg8Iw4nPfukQ+k+CNdx3a0Pj9IGKgR1lh0llKSt8EwzDYvyfLYF+GUnXpTNub7j7EyYM9fO5blxgeW34my/UCpkoOw2MVXrg8xYWRIlG09qx1Yy9knJ5cAj8MeXGkyAuXp5go1QnCcM3X7wTj03WSydb8CBuGgTnTZzURN7fFil0mHWO86FCpN7XwsSOFUcTlG2VMk7kPCWeuTFN3A+44OdO71Q3ozaXUPkpkEdu2h2mk/T4IPA78mW3b37Es63OWZd1n2/Yo8AvApwGbxorr72/VeEW2q7rrc/5qkfHpGj25xIaD1krN42MPnuOTX7/Avj0ZfuE9L+Oem4cWfPY4MJDlH33vrbzl3kOcHZ7mjz7xDI++MLrqZ8T5wev1yZc+d45O1TFNY26i/xF7lJHxKu945ZENFbB0vYBcOrZrM/5k6+gnbpMMw2D/QBbPDylVXfLzWnCYpsEPvf4kH/r0s3zsgbOcPNBDpe5RrftUZv7xg6VBpnW0j+99zbF1V5VNJWKkEjF8P+TKjTIjpsFQf4b+HVygxvMDyov+vncawzBIJ2NcHatw6lDvrlpNX2xsqkal7s/tNw7DiG88OUIhm5grYOH6IYf3prdymCLblm3bfwb82aLH3jXvzx8HPt7pcYl0i6lSncs3KiTixqaq6D5/cZLPfPMijhvw1vsO8+rb9q2YOWaaBq99+QFuOdbPZx66yGceusjT5yd49/ccm9vfutjilde+XJqx6drcFptyzeOBR4c5caDAbcukIK9H3Q04fmDt4lEirabAtQVMw+DQUI4LIyG1erCgzHg2Hef9bz7NX375HBeulcim42TTcQb7GntTc+k42XRi5r9xLl4r8eXHrvLBv36Gd73mKLfNKzu+lnjcpCeeJAhDbkzUuD5eIzBjJAh3XMW3YtUDDAzD4EuPXKFS93nDnQfo7VCwXnd8yjWPPT3pTVX+DcOI8WKd6YrLqYM9S1Z6U4kYpYrHVMlhT8/uDMoqdY+RiSqFeRM533r2OlfHq/zwG05imgZBGM6V6hcREWmV2RYyY9N18pn4XGGjZtVdn7/59mWeODfO/j1ZfuAdJ9jbv75U24GeNH//HTfz2JkxvvjdK3zwk8/yru85zp0n+5fNEJsNXkfGa0yXPeIxY+64v3v4Cl4Q8s5XH9tQdpkfhCRiplr2yZbQp7wWicdMju3Pc3Z4GscNFqReHBjI8j/98MvX9T6Hh/LcfKSPT37tRf7yy+d52Ykp3vmqo019II+ZJvmsSRRFjE5WqVZcjuzNd+xD/Xr3nW5UFEWMTdVJp2IUKy4PPX2NKIKnzo3zqtv2cf8d+0kn23OtfhDynedu8LUnRnC8gJhpsLc/w97+DPv6s+zbk2Fff2bZKoC+H3Jjqsa1iSrXxquMTFS5PlGbW3V/490Hef2dB5e8LpuOMTJepZBN7ro0WD8IuXStRCYZm/uZmijW+fJjw1hH+uZmi+v1kKH+9K5elRYRkdaqOT6Xb5RxvYCeDbTFi6KI4dEKT54b5+kXJ3C8gNfdeYDX33Gg6QDYMAzuuXmImw738rlvXuJTXzuPfbGPH3jdiWXb/RmGQW8uQaXmz+1tvXCtxJPnxrn/jgMM9m5sMrzmBBwYyOp+K1tCgWsLJeIxThzo4eyVaUzT2HCQMdSX4ae/9xa+8dQ1vvrECBevlfje7zmGdaSvqfdp9PdKUa06nL0yxf6Bxn7cdv2y8YOQ6xNVxot1cukE/YUUuXSCZKK1e0brboAzcxP51jNjRBH81Dtu5omz4zz09DUeOzPG6+88wH3W0IZnRheLogj70hRffPgKkyWH04d7ue1YP6NTNa5P1jh7ZZonzo7PHV/IJhoBbV+GIIKLI0VGp+qEM3tTUokY+/dkuNcaZP+eLC9cnuYrj1/l2P4Cx/YtLMYUi5kQhdyYrHJoqLMtJ7ZSFEVcHasQhsxlMURRxKcfukjMNHnXa47O/VwFUUhvbuemxouISOeEYcTYdGOiOZWINb0tabLk8NT5cZ48N85E0Zmpft/Hq2/bx8HB5VN816uQTfL+N5/iqQtTfPKr5/jwZ5/j/W8+zcAyWVmGYcyNPQhDPv+ti/Tmkrzujv0bOncYRhigXumyZRS4tlg6Gef4gR7OX53GNOMbTtGNmSavv/MgNx/u45Nff5E//9JZ7jw1wDteeYR0k/1bU4kYiZjJtYkaxYrH4b25lq9Ilmsel6+XCMKIQjaB54cMj1aIjIhkzKSvkKKQTZJJNt+Qe7HpskPMbAQxj58d5/j+AicO9HDiQA+vum0ff/fwZf7mO5f57nM3ePO9h7j12PKpNOs1Ml7lb79ziYvXywz1pfnxt93E6UO9S44r1zxuTNa4PlHl+sx/XxwpzVV9Pn24lwMDWfbvydJfSC0Y0y1H+7k2UeUTX32RX/j+25b06M1mYowX6/QX0rsmHXay5DBVcujJv7SX6LEzY1y8VuLd33Nsrrpio0hEYkMFJkREROar1n2ujJapuz75TGLdn1nqrs+zFyZ58tw4l66XATi+v8D9Lz/Arcf6W3qPMgyDN9x9mEI6xse/fJ4//vRz/NAbTnDT4b4VX/PtZ28wOlXnR95yekmbvTCMqLs+s3Wfopl/RbPtn6OZSeIwZG9/dsNFqUQ2a3d8Au6wfCbBkb15Ll0vU8iu/5fecvYPZPnZd9/KV54Y4RtPjfDiSJHve+1xTi0TOK3GNA16com5HrAHB/PsWRQ8bYQfhNyYrDE6XSObipGJN36kkonYXEPqIAgZL9a5MVnHNBqtZvryKXKZ5gP72T2hmVScC9dKTJYc3nj3S+m1Bway/OTbb+bscJEvPXyFv/zyeQ4P5XjbK45wZG9zq5WlqsuDjw7z+Nlxsqk473r1Ue65eWjF72c+kyCfealYEDSC6/6+HFPTq/c5SyVj/PAbTvLhzz3Pp75xgfe/6dSC741hGKQSMa6OlTlxsGfH7VlerO76DI+VyWVf+hVVrLh88btXOL6/wN03Dc47NuTovuxWDFNERHaIIAwZnaxxfapGOhmjZ50FmK7cKPPtZ69jX56aaRGT5k33HOLlJ/e0fWXyxIEefvb7buVjD5zj//27s7zpnkPc//L9Sz7bTVdcvvL4VW4+0rds9l652qjZkUyamBhgND53GMz815jpem8Y5HbJ5LlsT/rpa5P+QhrPDxkZr25oX8R8sZjJm+85hHW0sff1T794hpcd7+feW/ZybF++qfdOp+IkwpAro2WKFZdDg7m5ALNZ5ZrH5RslgiCiJ7vyNcZiJrmZ2bkoiqjO9J/NZ+Ic39/TVGBfqXsEM3toH3thjHQyxi1HF1bFMwyDmw73cupgD0+cHePBx67yXz73PLcc7ePEgR6y6TiZVKMYVjbV+Cc+L63b80O++cw1vvHUNYIw4jUv28fr7jjQ9Er37FjW+/05OJjjLfce4ovfvcLDz4/yilv3Lng+lYxRrno8d2GSPT1p+gsp0snYtmjd00p+EHLxWplE3JwL0KMo4nPfukgQNnrdzV5zGEaYBioSISIiG1aueVwZLeP54aqfZ+abKjl86ZErPHNhkkwqxt03DXHH6QEODmQ3fF8Ow4hyrVF8Mp001/X5rC+f4qffZfGZhy7y4KPDXBuv8p77jy947d9+5zJRBH/vlUeWvL5a98lnExwcymnfqmx7ClzbaKgvg+uHTBTr6565W82hwRw//3238ZUnrvLw86M8c2GSgZ4Ud988xJ2nBsit88N7zDTpzSWp1j3OXJnm0FCO3lxy3b9ol6yyNhHQGYZBOhUnnWqsoF2frHJgYP37PSaKdZLx/6+9O4+S66oPPP5979W+9r6qJbXU0pVssGzZxsZ4wXuwDYYEDAESQliSM5A5M0kmhzNhZsJkkjOZzMl2JgsBckhgGCBhMxiDwQZb3uVNsrF9ZS2WurX1vtReb5k/XnW7W+pd6u6q1u9zjq2uqlev7uvbVbd+d/ldk3zR5pWjI+ze3jznWmLTNLhsezMXdzfw5MunefylU7x6bHTWY0MBcyqQnciXmciV2bGpjlsu37Cq2XyvvqiVIycneGBvL10tCdoaZ44kJmJBXNdjJFNgcKxAJGTRXBchGQuti6k7k+tay7YzY03Ry6+PcKB3jFuvmFkfhZJNQyq8Lq5dCCHE6prMzTE4XiAasqa2jJlPoWTz6P5TPPXyaQzD4Ppd7VzzprZlDwJMnbdoU7Jd2hpiRMIBTg3lGM+WiIats6b2nikYsHj3dd20Ncb46TN9DN1X4J6bttKQivBa3xivHB3hxt2d1CVnjgA7jovremxoTkjQKmqCBK4ryDAMOhrjlG2HbM6eMe1xuQIBk5sv38D1u9p5+fURnjswyE+f6eOhZ4+jNtZx2famWbdVmU0sEvRHt05PEDANwiGLaChAOOTvCxuwTIIBc0ZQMDnKatvzj7IuRjIWpH/En5JTn1w4OCzbLmPZEslYkL2v9uO4Hpdtb1rweaGgxfW7Orj2ze3kiza5ok2uYM/4efq/sWiQ91zfxua25ILnPt8Mw+Duazfz+e+9zLcePswn3rnzrMbQNA3ilazFZduhtz+DaRikEyEaUxGi4UDNjsIOjOUZzRRndPTkCjb3P3WMjqYYV13UOuN42/EkSYQQQoglG8sUONA7iusu7vuM63o8d2CAnz9/glzRZtfWRm7c3XnOAxOO65LN2cQiQTa1paYGA5LRIOPZUiU/SYloODBv0k/DMHjrxW201sf41sOH+OIPXuHuazfzwN4+GlMR3npx61nPyRRsNrUkzjnoFmK1SOC6wkzToKslyZGT44xnSv4Ho+Evdjfw1w0wuXagcjsctBacPhsMWOzqaWJXTxMDo3mef22QfQeHeOXoCOl4iMu2N3FpT9OcG1RPClj+6KvnediOx3iuhDPh4Xr4ZcPDNE0iQRPLMhnL+L1/0fi5/+n42e4C9PVnCAcDCyYdmsiVmPytPHdgcCrR0WKZpkE8Glz0yPRaiUeCvOf6br7y4wPc/9Qx7r62e85jgwG/J9bzPCZyZUYmioSCFk3pCIlokHBw+VOJi2WHTL7MUK4MZYeG1LmviZ5PJl/m1GDurOyND+ztpVB0eOdtm2e8L/JFm2QsuKQRfyGEECJXKHN0MEswYC64A4TneRw8Ps5Pn+llYLTAptYEt17Zdc7Zgf1y2DiOR2dzgvpUeMaop2EYpBNhkvGQH8AOZSkUHaJha8bypjNt6Ujx8bsu4psPHeQbDx0C4MO3bT9rZlI2b1OfCJOWzl9RQ+Qb3yoIWCab21LkCmU8wPP8D0LX88ADFw/P9bO3OY6ffCgUNBed+be5LsptV3Zx0+5O9LHRqR7Bh184wdsu6eDaN7Uu2JtmGAbBwOxb+Liuh+O6lEv2Oa/XPZNlmoRDFkdPjdOzoW7eBmRo3N+79cRQjv6RPHdcvfG8laPadLenuG5XO3v2nWRLe4o3b22c93jDMKYCf9v2N0v3PAhYBnWVhi8athZM6lQqO0zkywyPF8iXHAwPWpuTnByYIF8q094YX5HEUMWyw9FTE8QiM7NOv9Y3xv5DQ1y3q53WaZ0UnudRtl262xc3u0AIIYSYNJopErQCeAFn3uNOj+T4yd4+Dp8YpyEZ5p4bt6I21p1zu+M4Ltm8TTIeoqMpPus+rJNMw2/HU7EQY9kip4Zy5IoOyYQ753Pqk2E+escOHtjbSzhkzUgaCf4MNsPwc2tIGypqiQSuqyQYMBfdq9WQikwlT1pKKvaAZXJxdwMXdzcwMlHkyV+c4rF9J3jp0CB3XbP5rA+uxTJNA9O0WGicMlso8/DzJzAMg62dKbrbkwuuywB/Km/OsTl2eoLu9tmTNeWL/tTeVDzECwcGCVgmb9rSADA12riYdSC15IZdHbx+coL7njhKZ3N80WttAwGTRKUDwHHdynrYPBgGyWiQukSIWCQ41ZlRqoysjkwUyBZsDAwiYZNUZeQzXMmuODJRolB02NiaPK/TihzX5djpCSyTGb3IxbLDfU8cpSkd4bpL2mc8J5u3aamLnvdtnYQQQqxvjusyPF6ksy1CqVia/RjH5eF9J3jsxVOEgxa3XdnFlTvOfW94z/PIFxw8YGNrgnRi8TOZTNOgPhkhHQ8zmilSKNtkcuU595gNBS3uumbzrGXIFcps7ayT/BCi5si3vioUDQfY2pGmfzQ3tQZ0qYFCfTLMO67exNWXdPK1H73CVx84wO7tTdxyxYYV+bKvj43w/cePUiw5GIbB3lf7sUyDTW1JejpT9HSmaUxH5vyAjkUClbUcuVmn34xli1imSans8OKRIS7aXD91HfmiTToeIl9yyGfLxKMLjyzWAtM0+OUbtvD57/2Cbz18mI/esWPJjYxlmsQib2TmLZb9NbGeYRAJWlgmU8FqOGTOuVbHMAySsSD5os1rfWNsakuel0y+nudxctCf/pSMzzzfQ88eZzxbOuu6bcfFMA2a6qLn/PpCCCEuLNmCv1/pXIMCA6N5vrvnCCeHclza08itV3ad85KUyfa3VHapS4Rpb4wtu6PdNA1/Z4GGOPteOcVIpkhyCYMcmbxNc11MsvGLmiSBa5UyTYO2hjipWJje/gwTuTKJ6NKT7mzpSPPJd13Mwy+c4IlfnOJg3xh3XrOZbRuWtg/sXAolmx8/3cu+g0O0NUR59+2KhmSYo/0THOwb59DxMR7Y28cDe/uoS4TY2pmmpzNNd/vZo3bJWJCB0TyRsEXDtGRNrucxPF4kEjZ58dAwpbI7IymT7Xg018WIhCyGJwqcHs6D5xKLruxWMY7jYpqL3+5mOdLxEHdfu5lvPHSIB589zu2zpLJfLMPwE3BNboJu2y6u5y0psUQ0HKBsuxw6MUZnU5zG1NydEYsxNF5geLx4VtB67PQEe1/t56qdLWftv5sr2GxoSUhPsRBCiCUbGisQDJ7dbnmexzOvDvCTZ3oJBizuuXErOzbVz3KGxXNcl3zBmWprNzRHz1vAGLBMuloSxCIBTgxkiYQXHuQolh1CAYvWBun4FbVJAtcqF4sE6NmQ8refGckv6oPpTMGAyS1XbGDn5nruffQI/++nr3HJ1kZuf8u59SIeOTnOvY++zniuxHWXtHP9rvapaTRbO9Js7UgDXYxOFDl4fIyDx8fZf2iIZ/UAAcvknhu30jMtgJ6erCkyLVlTrmBjOx4x0+T51wZpTIXZWAlmyrZDJBQgGvaD1KZ0lHQ8zMBonsGxPKGAuaz9V+fiuh75oo3rghUwcBwPPA/TMqYyMS+V47iUbZfAGRmcJ6mN9Vy5o4WnXj5Nd3uS7bNsHr4c8yV3mE8wYJI0g5wYyFIo2rQ3LW/dayZf5vhgluQZ2Rxt2+X7jx0lHQ9x4+7OGc8plhxi4YBkEhZCCLFkZdshky+dFTxO5Erc+9jrHDo+ztbOFO9622aSseVnCy6WHEplB8syaWnwv5fMt451uSa/98TCAV4/NYFt28Sis3/ncV2PYsmhp7NuXcxKExcmCVxrgGWatDfGScZC9PZPkMmViS9j9LWzKc4n3nkRe/af5LH9pzh8Ypw7rt645B7Fsu3y0HN9PPVyPw2pMB99xw42nDEqNl1dMswVO1q4YkcLjuNyrD/DA0/38u1HDvOxu3bSOG3tpmWaREIWr58anxoVHh4vEAwYDI7m6e3PcPPlnVPXni86dLUkZvwuggGTjqY49ckwJ4eyjGVLxBZII7+QYtmhVPKTGTSmI9QlwkTDAWzHpVDyG8KxTJlcoTRVhjOzQ09mbi7brh/w4uFhEAqaxCJBRjNF4pHArGtobr1iA8dOT/CvPzvEzs31XL69mY2tiTVLqmCaBsl4kNFMiXzJX/e6lEa5VHY4enqCWHhmMibX9fj2I4cZGi/woVu3zeik8bxKo7shLfvNCSGEWLKJXBl/B4c32pBXjo7wg8ePUrZd3nH1Rq5QzctqWx3XpVBwcTyXZNRPuhSPBlelvYpFgmzbUEffQIaxjL9t4JlThzN5m7bG2II7OAhRzeSvt4Ykov4H0+Rm2aGAH+Qt5QM2YJnceFknOzfWc+9jR/jmzw6hNtaxtSNFYzoytY3KXOc8MZjlu3uOMDhW4ModLdx8eeeSRoAty6S7PcX7b+rhCz94hW8+dJDfvHPnjKAnFLRwHJtjpzM0NiUZz5aIRwM8uv8kpmGwa6s/TdjzvMray9l7RaPhAN3tKSZyJU4M5chnS8QiAaxFTu91HJd80cEFEpEA7W1x4tHAjJ7KgGWSiJokokHaGvyALF9yGM+WmMiVcFwPM1isNJYQDVvUJ8LEIgFCQWvGPrnpeIijpyZIzNLgBAImH7x1G4/uP8X+Q0O8dHiYpnSE3dv9LZFWYkuYYtnhpcPD5MunuXxb41mv4Y+Q++teD/aNsbE1QTQcWHDU2U/GlME0mNGZ4Hke33v0CK8eG+X2t3SxtXPmdPZ8waEhHSYWkXU5Qgghlm5wrEAk5Lc7hZLNvY8e4YWDQ7Q3xnjPdd3Lyp1Qth3yBYeAZdBcHyWdWJnR1YUEAyab2pIMjuY5NZQjGnmjwz5ftIlHAjSlZYqwqG0SuNaYgGX6+30lwwyOFRjLFDEMg2hkaQmJ2hpjfOyunTz24imeeOk0+tjo1GOhoElTKkJjOjIVzDamI7x6dJRH9p0gEQ3yodu2VaYCL09dMsx7376Frz5wgO/uOcI9N26dEUxGK8maXj8xhut5uK7HvkNDbO9KT2XQyxdtGlPheQMlwzBIxcMkoiGGJ/z1lMWyg+v6I57g76frTT6hsn+tW9lKZqlTfEJBfyr35N64hZJDui7GRDJMMGjO2/OaToTpbPHo68/Muu1QMhbiHVdv5JYrOvnFkRGePTDAA3v7ePDZ41y0uZ7LVfNZo89L5XkeJwazPHdgkJeODFO2/XT7z+t+7rlpK631Z++bO7nu9cjJ8am9iUNBi1DAJBQ0KwG6RcAysEyDwbEC+ZK/B+v01/3hE8d48fAwN+7u5KqLZm6U7rr+9lGzvb4QQgixkELJplC2ScVC9PZnuPexlxgeL3DtJe3cMG2p02IVyw7FkkskZLGpLUkyFlp0gqSVYhoGLfUxYpEgR09NULZdwiET2/bY0p6Q2Uqi5kngWqNikSAbI0FKDTFGs0UGR/LYjr2kNbCWaXL9rg6uu6SdiVyZwbECQ2MFhsYLDI4VOHY6w4uHh2c8581bGnjHVRvPy7rR7vYUt17RxQN7e9mz/yTX7+qY8XgyFmQ8WyIaDnCgb4xcwebSbdOTMkFdYnFbxJimvw5ksrdxMhj296j1gyLH9fBcj7LjEgn5a2zP5UPeMAyi4QDpRJhSfvaU+2dqTEVwHJeTQ7k598wNBiwu3dbEpduaODWc47kDA7x4aJgXDw/TXBdh9/Zmtm1IU5cIL7oRLRRt9h8e5vkDA5weyRMM+Fsr7d7WRDIZ5Uv3vsQ/3fcq73rbZi7ubpilTCbBgD/y7VV+lyXbIV+yp36vlV8KAIlpa3A8z+Mnz/Tx7IEB3vbmtrO2vgF/PWxHY3xdbXckhBBi9YxlSliYjEwU+cqPNal4mI/8kmJja3JJ5ykUbUq2SzwSpKM9Pu8stbXiz9BL09ufYTxTYlN7cioxoxC1TALXGhcKWrTUxWhKRcnky/SP5BjPlghYxqKnj/qjkiFS8dBZe72Wyg5D40WGxvLEIsFl7wU7l6suauHUcI6fP3+C1voYamPdjHLVJyOMOA7PHxggGQvSU5k+6idlsoiGl5lO3jAwLQOq8HO8uS6K7XgMjhVIxeefFtvWEOOOqzdxy+Ub+MWRYZ49MMiPn+7lx0/3+nu+JULUpyI0JsPUpyI0pMI0JMPUJcIYBvT2Z3juwCAvvz6C7bi0N8a44+qNvHlL41QjV18X5xPv3Mm//vwQ33r4MCeHcty0u3POoNgwDAJL+N0+su8kT/7iNFfuaOGmM5IxgV/XoaC16H1shRBCiOlcz2NovEAkYvLDh49iGAa/875L8Rx7Uc/3PI980cF2/PWrXa1+QqRqC1inCwUtuttTZPLlGTOchKhlEriuE6b5RvCZL9oMjxcYmShihYq4rrfs6SuhoEV7Y4z2xsVP0Zxce7oYhmFw51s3MTCa5zt7DvPxO3eetcZkLFvi4PFxrrukfeo6ZkvKtF4YhkFbYwzbcRnLFEkuYruaUNDisu3NXLa9mdPDOU4M5RiubDUzPF6YmjI0yTQMomGLbMEmFDTZ1dPI7u1NtDeevYcu+NOUP3K74kdP9fL4S6c4PZzjl2/Ycs5ra5946RQPv3CCXT2N/NJVXbPWZ67gsKUjteZTsIQQQtSmfNHfnaC/P8srR0e44dIO6pJhRkbnD1w9zyNXtHEcj7pEmOa66IrklFgpk98NhVgvaufdJxYtGg7Q2ZygpT4GQYtXDvrbz0xuGbNSXNcjkytjmgau5wfL0ZC14LqRYMDknpt6+ML3X+YbDx3iY3ftIBJ6409z38FBgKlpwq47f1Km9cA0DDY0J3Bcl2zOJh5b/Fu1tSFGa8PMjgbP88jky34gO+EHs+PZEpvbU1y8uX5R08sty+TOazbR1hjj/qeO8cUfvML7b+qhpX55yR6e1QP85Jk+Ltpczzuv2Tzr32a+aJOKh2SjdCGEEMs2Ml7ENOCBp3tJxUNc86bWeY+fPsLamIrQlI7KVFshqoAErutYMGDS3JTALdn0D+cYmSgSCpkzgsLzpVhyKJYdOpriNKQiFEoOE7kSwxMF7IKDaRiEQ+ace4em4yHe9/atfOXHB/jOI0f4wM09GIYfAD9/YJDu9iT1SX/vzkLJpjEVWdaeqbXENA26WpIcOTlOvuAQjSy/0ZwM9JOxEJvalrae50yXq2Za6qP8688O8aX7XuHd13azc/PStlTaf2iI+544yrYNad5zXfeso6me528d1N0eW5cj60IIIVae47qMZooc6Bvl1HCOX75+y7z5Egolm2LZpS4Rpq0+JgGrEFVkfX/zFwCEgxZdrUl6NqQJmBbjmdKMaaPnwvM8xrMlTNNg24Y6muqimKZBLBKgtSHGjo319GxI09IQxXFhPFsikytTtp2zzrWpLcntV3XxWt8YP3/hBACvHRthLFvism3NU8fZDlNB7HoXsEw2tyUxTL8xrRZdLQk+8c6dfgD780M89FxfJVPzwl49OsL3Hj3C5rYk73371jlH5DM5m5a66Ip0tAghhLgwZPM2hZLDz547zoaWOBd3z97RWrYdxrIlgpbFts40m1oloZEQ1Ua+EV5AYpEgWztTjGdLnBjKki/aJKJn7xm6WKWyQ77o0FofpaU+Nut5JjPrRsMBmuuiFEsOE/kSIxNFxjMlUomZ032vUM2cHMqxZ99J2hpivNY3TiRksaOStKlsO0TDVk2tMTlXwYBFd1uKgyfGKJWdJe2bu5KSsRAf+SXF/U8e49H9p3jx0DDJWJBo2M/IHA0HiFXqfvJ2Jlfiu4++TmdTnA/c3DNjH9fpbMfFsoxl7aknhBBCTBoaK/DsgX6yBZsP3LLtrBk8tuOSLzhTyYySVZglWAjhu3C+/QvADyTTiTCJWJCR8SKnhnMY+PvALjaA9TyPbN7Gsgx6NqSJRxa//jAcsgiHojSmIvT2ZxjLlEhOy5xrGAZ3XLWRgZE8391zBNfzuHx789QU48mkTBeacMhiS3uKQ8fHsB2X6CpkM5xMSmFmioxnSyRjZzfmAcvknW/bzMbWBAf7xsgVbSZyZU6P5MkX7VlH9tsaonzwlm1zBuCO45LNl9nUllr308GFEEKsnLLt0DcwwTOvDnDJ1kY6m95IQOi4HhPZEpZl0tkS97eQk4BViKomgesFyjJNmuqipBMhBkYLjGaKOI4LGBgGWJZB0DKxLGNGsGLbLtmCTWM6TFtDfNmBhWEYdDbHKZVdcgWbWOSNP8VAwOR9N27liz94hUy+zGXTkjKZ6zwp03yi4QA9G9IMjOYZnShiGuaSOhwWy3U98gUHFz8pxbbuBg4d9Tg9kicWtmZdG7Srp4ldPU1n3V+2XfJFm1zRJl+0KZYcNrcn55z+Wyo7/jFtKdKJC2M6uBBCiJUxniuzZ/8pTNPg5svf2G6tULIx82VaG2M0JNd/zgwh1gsJXC9wwYBFR1OcjqY4ZdulbDsUSg75ok22YJPPlWEycPXAtAy625Ok4uceVFimyaY2f6TuzCmwqXiID9+2nZFseSpDbr5k03ABJGWaTyQUoKslSWt9jOHxAoNjBTwgFl44e/NCHNcll3cwDGhKR2hIRQgFLWKRIG0NcZKxEMdOTVAqLT7LcTBgEgyEFpWOP1+08TzY2lk3oyNDCCGEWI7nDgxw8PgYN17WMdXpnS/6+SIu7m5gYjy/lsUTQiyRfDsUU/wgwyQ2beqv63qUbIeS7VIqO6TjoXmz8S39NS02t6c4eHwM0zRmBKUt9VFUdxMjo1kAnAsoKdNCQkGLtsY4TXVRRjNF+odz2K6//neudaNzsR1/1NsyDVobo9QnIrOeIx4Jsq2rjhODWUYmCiRiQSzz/HQiZHJlwiGLTa3JqlnDK4QQonZl82V+sreXdDzE1Re3AZAvOJimSXd7kkg4wMQal1EIsTQSuIp5maZBJBQgsoKzc6PhAJtaE7x+coJEbPZkUaXyhZeUaTEClklTOkpDMsJ4tsjpEX9/1lDAxDAN8MDz/8f0nL+eB+DhOBCwDDqbE6TjoQVHswOWSVdLgkQsyPGBLEHLJXIOdeJ5HhPZMulkmA3N8fMWCAshhLiwPfRcH4NjBX7lhi0EAya5gk0w4GfqP58d8EKI1SNRgKgKqXiYjiaX44MZUvHQWUmAimWXrub4HM8WpmlQl4yQSoTJ5suMTBTx8DAxwPDXLRtQ+dfAMP1/wyGLVCy0pHWyhmHQkIwQCwfo7c/MmbhpIY7rksnZflbqhpgkxRBCCHFeZAplfvR0L10tcS7aXE82bxMOmmxqSy15VpIQonpI4CqqRmM6QrHsMDRemLEm0nU9DCBxgSZlWorJ5FWrkcAqEgqwtSNN/0jOT9wUCSz6C0HZ9rdS6mpN0JCMrHBJhRBCXEi+t+cI+aLN7W/ZRjZv+zO72pIXdI4MIdYDCVxF1TAMg/bGOCXbIZu3iUf9P09JylS9TNOgrTFOIhait3+CQsl540HPwzAMLMvAMv3/TNOgWHJwXNjaubStlIQQy6eU+iDwWSAI/JXW+m/PePxS4ItACngE+G2ttb3a5RTiXJ0eyfHz54/zpu4GkrEQ8aif1FC+QwhR++RdLKqKaRpTDUyhkvnPcTxJylTlEtEgOzbWs3NTPds31LGlI8WmthRtjbGphF6OC9m8jWma9EjQKsSqUUp1An8CXAtcCnxSKXXRGYd9Ffi01no7/sqCT6xqIYU4T77+4GuYpsHVF7WSjAbZ2CpBqxDrhYy4iqoTsPzkCQePj5HJlYiGA5KUqQYYhkHAMghYEGb2xBeenxVqyethhRDn5BbgIa31MIBS6t+A9wL/vXJ7ExDVWj9ZOf7LwOeAv1+tAnqex6Hj4xRKCw/ypofyjI3lVqFUK6fWr6Fayz8wmmffwSGuuqiVDS0JNjQnzvte50KItSPRgKhK4ZDF5vYkAxNlmtOyBnK9kIBViDXRAZycdvsk8JYFHt+wlBdobEwsu3AAB/tG+dOvPntO5xAC/HwZd9/Qw8VbGhcMWpubk6tUqpVR6+WH2r+GWi8/1NY1SOAqqlY8EqS5KUlGNggXQohzYTJzRywDcJfw+IKGhjK4rrfwgXNIhy0+86HdDIwu/HmfTEaYmCgs+7WqQa1fQzWXv60hSksqxNBQZt7jmpuTDAzU7k6utV5+qP1rqPXyQ3Veg2kac3aGSuAqqlo8GiSXqc7GUQghakQfcN20223AiTMeb5/n8VWxvauO7V11Cx5XjV+0lqrWr6HWyy+EqE2yWl0IIYRY334K3KyUalZKxYBfAX40+aDW+ihQUEq9rXLXrwH3r34xhRBCiLlJ4CqEEEKsY1rr48AfAj8DXgC+prV+Win1Q6XUFZXDPgT8pVLqVSAB/M2aFFYIIYSYg0wVFkIIIdY5rfXXgK+dcd8d037ex8yETUIIIURVkRFXIYQQQgghhBBVTQJXIYQQQgghhBBVbUWnCiulUsDjwF1a69eVUm8F/hJIAvuBj2itSytZBiGEEEIIIYQQtW3FRlyVUlcBjwLbK7dTwLeBT2qtL64c9rGVen0hhBBCCCGEEOvDSo64fgL4FPCVyu1bgSe01vsrt39nhV9fCCGEEEIIIcQ6sGKBo9b64wBKqcm7eoCMUurrwA7gMeD3Vur1hRBCCCGEEEKsD6s54hkAbgeuBo4BXwI+A/zRUk7S2Jg47wWbT3NzclVfbyXU+jXUevmh9q+h1ssPtX8NtV5+WB/XIIQQQoi1sZqB6yngSa31EQCl1DeBTy/1JENDGVzXO99lm1Vzc5KBgYlVea2VUuvXUOvlh9q/hlovP9T+NdR6+aE6r8E0jVXvDBVCCCHE8qxm4PoA8DmlVJfWuhe4C3h2Cc+3wP+isZpW+/VWQq1fQ62XH2r/Gmq9/FD711Dr5Yfqu4Zp5bHWshw1TtrmZar1a6j18kPtX0Otlx9q/xpqvfxQfdcwX9tseN7Kjl4qpV4H3l7ZDudO4E+ACPAC8Jta69wiT3UtsGdFCimEEOJCdh1+FnyxdNI2CyGEWAlntc0rHrieR2HgSuAk4KxxWYQQQtQ+C2gH9gLFNS5LrZK2WQghxPk0Z9tcS4GrEEIIIYQQQogLkLnWBRBCCCGEEEIIIeYjgasQQgghhBBCiKomgasQQgghhBBCiKomgasQQgghhBBCiKomgasQQgghhBBCiKomgasQQgghhBBCiKomgasQQgghhBBCiKoWWOsCrAWlVAp4HLhLa/26Uuo3gD/A3zz9IeD3tNa2Umo38HkgBPQCH9ZajyqlPgL8T+B05ZT3aa3/sEqv4R3An1We9iLwW1rrjFJqI/BVoAXQwIe01pkaKv+a1oFS6r8B90x77T9QSt0C/AUQBb6htf5s5dhLgS8CKeAR4Lcr17ZmdXCeyl8zdTDtOf8CPKS1/nLldk3UwTzlr5k6UErdDXwOMIAjwEe11iNr/Vkkqoe0zdI2n4fyS9tcQ3Uw7TnSNp9H67ltvuBGXJVSVwGPAtsrtxXwP4CbtdZvBoLAv68c/tfAf9Va78KvtN+v3H8F8Lta60sr/612w7ioa1BK1QH/DHxAa30JsA/408pp/g74O631DuAZ4L/UWPnXrA4qb/7bgMuAS4HLlVK/CvwTcDewE7iy0rCD/8b/tNZ6O/4Hwycq969JHZzH8tdMHSilOpRS3wfee8apaqIO5il/TdRB5cvw3wN3Vj5P9wN/VDnVmn0WieohbbO0zeeh/NI2+2qmDqRtXttrqMW2+YILXPHf2J8CTlRuXwI8obU+Wbn9A+DdlZ8t/J4sgBiQr/x8JfARpdSLSqmvKqXqV7zUMy32GrYBR7XWL0+/XykVBK4H/q1y/5eB9618saecU/krP69lHZzE73Uuaa3LwCv4Df1rWusjWmsbv0F5n1JqExDVWj9Zee6XK/evZR2cc/krP9dEHVSO/xDwPeCbkyeolTqoHH9W+StqpQ6CwKe01scrz90PbKyCzyJRPaRtXvv3g7TNNdIuSNu8YqRtrvK2+YILXLXWH9da75l21z7gaqVUl1LKwu81aas89rvAF5RSJ4FbgX+o3H8S+GP8D/Ve4P+sSuErlnANrwFdSqldlePuqdzfBIxX/njBv54Nq1P681L+yTKvSR1orX8x2VgopbZVyuVWyjRp8nfaMcf9a1YH56n8kz/XQh2gtf5zrfUXzzhNrdTBXOWfPKbq60BrPaS1/k7l2CjwGeC7rPFnkage0jZL23yupG2WtvlcSdtc/W3zBRe4nklrfQC/ou4F9uD3NpQqFfgl4BatdTv+kPm/VJ7zHq31Y1prD/hfwDtmPfkqmesatNajwK8D/6iU2ovfi1rCr3fvjNO4q1bgMyyj/FVRB0qpi4GfAP8JOMzM36mB/zs983c91/2wynVwjuWvpTqYS63UwZxqrQ6UUmngPmCf1vqfqYI6ENVJ2uYp0jYvkbTNNVMHc6mVOphTrdVBLbXNF3zgqpSKAE9rrS/TWl8DHAcOAW8C8lrrpyuHfh54u1IqrZT6j9NOYQA2a2iua6j0kPZpra/SWl8JPI9/bf1AuvI4QDtvTA1adUstfzXUgVLqbcCDwGcqb/I+/N/jpDb83+lc969pHZxr+WusDuZSK3Uw1/Nrqg6UUu288eX345XHq+qzSFQPaZsBaZuXU2Zpm2unDuZSK3Uw1/Nrqg5qrW2+4ANXIA48qJRKKqVCwO8A3wAO4k+FUZXj7gb2AhngD5SfxADg08B3VrnMZ5rrGjzgAaVUp1LKwJ9e9Y3KnPc9wPsrz/914P41KPekJZWfNa4DpVQX/lSKD2qtv165+yn/IdVTeaN/ELhfa30UKFQ+RAB+rXL/mtXB+Sg/NVQHc52jVupgntPUTB1Ufv4+8E2t9X+o9EKvaR2Iqidt89q/H6RtrtJ2QdrmlSFtc/W3zYG1LsBa01oPKaU+BzyJv0j5a1rrrwEoPw38NysfzP34KaIdpdQ9wN8rf8rSAfwKXTMLXMNvAT8CwsBPgT+vPO3fAf+slPoscAz41VUveMVSy18FdfD7QAT4ize+O/EPwG8A36o89kPeWNT+Ifz1WCngOeBvKvevVR2cc/lrsA7mUit1cJYaq4N3A7uBgFJqMvviM1rrj1NFn0WiekjbvPbvB2mbq75dkLb5/JO2ucrbZsPzzpzCLIQQQgghhBBCVA+ZKiyEEEIIIYQQoqpJ4CqEEEIIIYQQoqpJ4CqEEEIIIYQQoqpJ4CqEEEIIIYQQoqpJ4CqEEEIIIYQQoqpJ4CqEEEIIIYQQoqpJ4CqEEEIIIYQQoqoF1roAQoiFKaW+APRrrf+wcvvDwK8A/wR8FggBOeD3tdZPKKVagc8DrUAbcBS4R2vdr5R6HXgKuAT4z1rr76z29QghhBC1TtpmIVaXjLgKURv+FvioUmqys+mTwI+APwXu0FpfVrnv20qpOPAB4Amt9VuBLfgN569NO99LWuud0jAKIYQQyyZtsxCrSAJXIWqA1voF4Ahwp1JqJ9CBP2OiHXhQKfUC8H8BF+jRWv818LhS6neBvwPeBCSmnXLP6pVeCCGEWH+kbRZidclUYSFqx98CvwkcAP4Rv+PpQa31+ycPUEp1ASeUUn8GvAV/utLPgCBgTDtXZrUKLYQQQqxj0jYLsUpkxFWI2vFvwGXAe/EbvQeB25RSOwCUUncA+4EocDvwV1rrrwD9wK2AtRaFFkIIIdYxaZuFWCUSuApRI7TWJfwG8nGt9aDW+mX8tTNfV0rtA/4YeJfWOgP8d+B/K6X2A/cCjwI9a1R0IYQQYl2StlmI1WN4nrfWZRBCLEIlscMjwKe01k+udXmEEEKIC520zUKsHhlxFaIGKKVuB3qB+6VhFEIIIdaetM1CrC4ZcRVCCCGEEEIIUdVkxFUIIYQQQgghRFWTwFUIIYQQQgghRFWTwFUIIYQQQgghRFWTwFUIIYQQQgghRFWTwFUIIYQQQgghRFWTwFUIIYQQQgghRFX7/9X4BuL7lTj2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing Ucity changed over time for fueltype 1 and fueltype 2\n",
    "\n",
    "fig, ax =plt.subplots(1,2,figsize=(16,6))\n",
    "\n",
    "sns.lineplot(x='year', y='city08', data=vehicles,ax=ax[0]).set_title('City MPG trend for fule type 1')\n",
    "sns.lineplot(x='year', y='cityA08', data=vehicles,ax=ax[1]).set_title('City MPG trend for fule type 2')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Data preparation \n",
    "### Cleaning data\n",
    "\n",
    "Lets handle missing values, outliers and mixed types in the dataset. There are some features which areirrekevant for the analysis and model training. After evaluation of useful columns, the irelevant features wil be dropped from the training dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mfrCode', 'c240Dscr', 'c240bDscr'], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a copy for data cleaning and training set \n",
    "vehicles_clean = vehicles.copy()\n",
    "\n",
    "# Check columns which have mixed types.\n",
    "# From the warning we get when loading the data, we see that columns with mixed types are at index [73,74,76]\n",
    "column_names = vehicles.columns.values\n",
    "column_names[[73,74,76]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barrels08</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>cityUF</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2TailpipeGpm</th>\n",
       "      <th>comb08</th>\n",
       "      <th>...</th>\n",
       "      <th>range</th>\n",
       "      <th>rangeCity</th>\n",
       "      <th>rangeHwy</th>\n",
       "      <th>UCity</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>year</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.353421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.791995</td>\n",
       "      <td>5.843684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.637349</td>\n",
       "      <td>467.721552</td>\n",
       "      <td>20.121890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.372486</td>\n",
       "      <td>33.838537</td>\n",
       "      <td>2002.175225</td>\n",
       "      <td>-4576.530227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.319235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.391269</td>\n",
       "      <td>9.331172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184.554969</td>\n",
       "      <td>116.406190</td>\n",
       "      <td>4.817131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.957422</td>\n",
       "      <td>8.401597</td>\n",
       "      <td>11.618256</td>\n",
       "      <td>3582.237310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.866875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>185.145833</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>-32500.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.330870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>386.391304</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>-6500.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.480500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>444.350000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.439800</td>\n",
       "      <td>33.333300</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>-4250.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.388824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>522.764706</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>47.087143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>36.873600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>1269.571429</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.111100</td>\n",
       "      <td>76.923100</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>3750.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          barrels08  charge240        city08       city08U   cityCD    cityE  \\\n",
       "count  39749.000000    39749.0  39749.000000  39749.000000  39749.0  39749.0   \n",
       "mean      17.353421        0.0     17.791995      5.843684      0.0      0.0   \n",
       "std        4.319235        0.0      4.391269      9.331172      0.0      0.0   \n",
       "min        6.866875        0.0      6.000000      0.000000      0.0      0.0   \n",
       "25%       14.330870        0.0     15.000000      0.000000      0.0      0.0   \n",
       "50%       16.480500        0.0     17.000000      0.000000      0.0      0.0   \n",
       "75%       19.388824        0.0     20.000000     15.000900      0.0      0.0   \n",
       "max       47.087143        0.0     44.000000     36.873600      0.0      0.0   \n",
       "\n",
       "        cityUF           co2  co2TailpipeGpm        comb08  ...    range  \\\n",
       "count  39749.0  39749.000000    39749.000000  39749.000000  ...  39749.0   \n",
       "mean       0.0    101.637349      467.721552     20.121890  ...      0.0   \n",
       "std        0.0    184.554969      116.406190      4.817131  ...      0.0   \n",
       "min        0.0     -1.000000      185.145833      7.000000  ...      0.0   \n",
       "25%        0.0     -1.000000      386.391304     17.000000  ...      0.0   \n",
       "50%        0.0     -1.000000      444.350000     20.000000  ...      0.0   \n",
       "75%        0.0     -1.000000      522.764706     23.000000  ...      0.0   \n",
       "max        0.0    893.000000     1269.571429     48.000000  ...      0.0   \n",
       "\n",
       "       rangeCity  rangeHwy         UCity      UHighway          year  \\\n",
       "count    39749.0   39749.0  39749.000000  39749.000000  39749.000000   \n",
       "mean         0.0       0.0     22.372486     33.838537   2002.175225   \n",
       "std          0.0       0.0      5.957422      8.401597     11.618256   \n",
       "min          0.0       0.0      0.000000      0.000000   1984.000000   \n",
       "25%          0.0       0.0     18.400000     28.000000   1992.000000   \n",
       "50%          0.0       0.0     21.439800     33.333300   2003.000000   \n",
       "75%          0.0       0.0     25.700000     39.000000   2012.000000   \n",
       "max          0.0       0.0     61.111100     76.923100   2022.000000   \n",
       "\n",
       "       youSaveSpend  phevCity  phevHwy  phevComb  \n",
       "count  39749.000000   39749.0  39749.0   39749.0  \n",
       "mean   -4576.530227       0.0      0.0       0.0  \n",
       "std     3582.237310       0.0      0.0       0.0  \n",
       "min   -32500.000000       0.0      0.0       0.0  \n",
       "25%    -6500.000000       0.0      0.0       0.0  \n",
       "50%    -4250.000000       0.0      0.0       0.0  \n",
       "75%    -2000.000000       0.0      0.0       0.0  \n",
       "max     3750.000000       0.0      0.0       0.0  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39749, 83)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping only fueltype 1 for the analysis going forward and removing the hybrid atv type. \n",
    "\n",
    "criteria1 = vehicles_clean.fuelType1.isin([\"Regular Gasoline\",\"Premium Gasoline\",\"Midgrade Gasoline\"])\n",
    "criteria2 = vehicles_clean.fuelType2.isnull()\n",
    "criteria3 = vehicles_cleaning.atvType != \"Hybrid\"\n",
    "\n",
    "vehicles_clean = vehicles_clean[criteria1 & criteria2 & criteria3]\n",
    "vehicles_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['barrels08', 'barrelsA08', 'charge120', 'charge240', 'city08',\n",
       "       'city08U', 'cityA08', 'cityA08U', 'cityCD', 'cityE', 'cityUF',\n",
       "       'co2', 'co2A', 'co2TailpipeAGpm', 'co2TailpipeGpm', 'comb08',\n",
       "       'comb08U', 'combA08', 'combA08U', 'combE', 'combinedCD',\n",
       "       'combinedUF', 'cylinders', 'displ', 'drive', 'engId', 'eng_dscr',\n",
       "       'feScore', 'fuelCost08', 'fuelCostA08', 'fuelType', 'fuelType1',\n",
       "       'ghgScore', 'ghgScoreA', 'highway08', 'highway08U', 'highwayA08',\n",
       "       'highwayA08U', 'highwayCD', 'highwayE', 'highwayUF', 'hlv', 'hpv',\n",
       "       'id', 'lv2', 'lv4', 'make', 'model', 'mpgData', 'phevBlended',\n",
       "       'pv2', 'pv4', 'range', 'rangeCity', 'rangeCityA', 'rangeHwy',\n",
       "       'rangeHwyA', 'trany', 'UCity', 'UCityA', 'UHighway', 'UHighwayA',\n",
       "       'VClass', 'year', 'youSaveSpend', 'guzzler', 'trans_dscr',\n",
       "       'tCharger', 'sCharger', 'atvType', 'fuelType2', 'rangeA',\n",
       "       'evMotor', 'mfrCode', 'c240Dscr', 'charge240b', 'c240bDscr',\n",
       "       'createdOn', 'modifiedOn', 'startStop', 'phevCity', 'phevHwy',\n",
       "       'phevComb'], dtype=object)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_clean.columns.values\n",
    "#df = df.drop(df[<some boolean condition>].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_columns = ['barrelsA08', 'eng_dscr', 'engId', 'createdOn', 'modifiedOn',\n",
    "          'startStop', 'trans_dscr', 'rangeA', 'c240Dscr', 'c240bDscr', 'rangeHwyA', 'UCityA', 'UHighwayA', \n",
    "          'highwayA08U', 'rangeCityA', 'highwayA08', 'cityA08', 'cityA08U', 'co2A',\n",
    "          'co2TailpipeAGpm', 'fuelType', 'combA08','combA08U', 'fuelCostA08', 'ghgScoreA','id','guzzler','charge120','tCharger','sCharger', 'charge240b', 'createdOn',]\n",
    "\n",
    "# dropped irrelevant features\n",
    "vehicles_clean.drop(drop_columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39749, 52)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Outlier detection')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Outliers detection by boxplot\n",
    "\n",
    "fig, ax =plt.subplots(1,2,figsize=(16,6))\n",
    "sns.boxplot(y='co2TailpipeGpm', data=vehicles_clean,ax=ax[0]).set_title('Outlier detection')\n",
    "sns.boxplot(y='city08', data=vehicles_clean,ax=ax[1]).set_title('Outlier detection')\n",
    "#presence of significant outliers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing values and outliers detection\n",
    "In the dataset, missing data is represented with a **nan**.\n",
    "\n",
    "-Some columns have -1 as not available.\n",
    "-Need to convert every -1 to Nan.\n",
    "\n",
    "-Sometimes outliers cannot be removed as it can affect training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert all -1 to Nan for feature(feScore)\n",
    "features=['feScore']\n",
    "for i in features:\n",
    "    vehicles_clean[i].replace([-1], [np.nan], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# Missing values')"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mising values\n",
    "plt.clf()\n",
    "a = vehicles_clean.isnull().sum()/len(vehicles_clean)\n",
    "title = 'missing values count for all columns'\n",
    "a.plot(title=title,kind='bar', figsize=(20,10))\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('# Missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlier columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feScore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>atvType</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fuelType2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>evMotor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mfrCode</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Outlier columns\n",
       "0         feScore\n",
       "1         atvType\n",
       "2       fuelType2\n",
       "3         evMotor\n",
       "4         mfrCode"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets find the outliers which also has missing valuesmore than 25%\n",
    "Vehicles_null = vehicles_clean.isnull().sum() / len(vehicles_clean)\n",
    "missing_features = vehicles_null[pct_null > 0.25].index \n",
    "# Print all the outliers columns to be removed.  \n",
    "pd.DataFrame(missing_features, columns=(\"Outlier columns\",))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outlier columns from the dataset\n",
    "vehicles_clean.drop(missing_features, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barrels08</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>cityUF</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2TailpipeGpm</th>\n",
       "      <th>comb08</th>\n",
       "      <th>...</th>\n",
       "      <th>rangeHwy</th>\n",
       "      <th>trany</th>\n",
       "      <th>UCity</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>VClass</th>\n",
       "      <th>year</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.695714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>423.190476</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>23.3333</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>Two Seaters</td>\n",
       "      <td>1985</td>\n",
       "      <td>-2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.964545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>807.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>Two Seaters</td>\n",
       "      <td>1985</td>\n",
       "      <td>-12750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.207778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>329.148148</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>Subcompact Cars</td>\n",
       "      <td>1985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.964545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>807.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Automatic 3-spd</td>\n",
       "      <td>12.2222</td>\n",
       "      <td>16.6667</td>\n",
       "      <td>Vans</td>\n",
       "      <td>1985</td>\n",
       "      <td>-12750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.347895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>467.736842</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>1993</td>\n",
       "      <td>-6500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   barrels08  charge240  city08  city08U  cityCD  cityE  cityUF  co2  \\\n",
       "0  15.695714        0.0      19      0.0     0.0    0.0     0.0   -1   \n",
       "1  29.964545        0.0       9      0.0     0.0    0.0     0.0   -1   \n",
       "2  12.207778        0.0      23      0.0     0.0    0.0     0.0   -1   \n",
       "3  29.964545        0.0      10      0.0     0.0    0.0     0.0   -1   \n",
       "4  17.347895        0.0      17      0.0     0.0    0.0     0.0   -1   \n",
       "\n",
       "   co2TailpipeGpm  comb08  ...  rangeHwy            trany    UCity  UHighway  \\\n",
       "0      423.190476      21  ...       0.0     Manual 5-spd  23.3333   35.0000   \n",
       "1      807.909091      11  ...       0.0     Manual 5-spd  11.0000   19.0000   \n",
       "2      329.148148      27  ...       0.0     Manual 5-spd  29.0000   47.0000   \n",
       "3      807.909091      11  ...       0.0  Automatic 3-spd  12.2222   16.6667   \n",
       "4      467.736842      19  ...       0.0     Manual 5-spd  21.0000   32.0000   \n",
       "\n",
       "            VClass  year youSaveSpend  phevCity phevHwy  phevComb  \n",
       "0      Two Seaters  1985        -2500         0       0         0  \n",
       "1      Two Seaters  1985       -12750         0       0         0  \n",
       "2  Subcompact Cars  1985            0         0       0         0  \n",
       "3             Vans  1985       -12750         0       0         0  \n",
       "4     Compact Cars  1993        -6500         0       0         0  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Missing Values\n",
      "cylinders has 0.0% of data marked as NaNs\n",
      "displ has 0.0% of data marked as NaNs\n",
      "drive has 2.6% of data marked as NaNs\n",
      "mpgData has 0.2% of data marked as NaNs\n",
      "trany has 0.0% of data marked as NaNs\n"
     ]
    }
   ],
   "source": [
    "# Display the percentage of missing values in each feature\n",
    "def missing_values_per(df):\n",
    "\n",
    "    missing_vals = []\n",
    "    missing_values_proportion =(df.isnull().sum()/len(df))\n",
    "    print(\"Percentage of Missing Values\")\n",
    "    if np.max(missing_values_proportion.values) > 0.:\n",
    "        for x in range(len(missing_values_proportion)):\n",
    "            if(missing_values_proportion[x] > 0):\n",
    "                missing_vals = missing_values_proportion.index[x]\n",
    "                print(\"{} has {:.1f}% of data marked as NaNs\".format(missing_vals, (missing_values_proportion[x]*100)))\n",
    "    else:\n",
    "        print(\"No features with missing values\")\n",
    "            \n",
    "missing_values_per(vehicles_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cylinders: 3\n",
      "displ: 2\n",
      "trany: 2\n"
     ]
    }
   ],
   "source": [
    "#number of missing values\n",
    "print('Cylinders:',vehicles_clean.cylinders.isnull().sum())\n",
    "print('displ:',vehicles_clean.displ.isnull().sum())\n",
    "print('trany:',vehicles_clean.trany.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drive          object\n",
       "fuelType1      object\n",
       "make           object\n",
       "model          object\n",
       "mpgData        object\n",
       "phevBlended      bool\n",
       "trany          object\n",
       "VClass         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical columns datatype\n",
    "vehicles_clean.dtypes[vehicles_cleaning.dtypes != 'int64'][vehicles_clean.dtypes != 'float64']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cylinders: float64\n",
      "displ: float64\n",
      "drive: object\n",
      "trany: object\n"
     ]
    }
   ],
   "source": [
    "# Data type for each features with missing values.\n",
    "features = ['cylinders','displ','drive','trany']\n",
    "for f in features:\n",
    "    print(\"{0}: {1}\".format(f, vehicles_cleaning[f].dtype))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Imputation of numerical/Categorical  data \n",
    "Removing columns for less number of missing values or few outliers is not correct. So, we will impute all the missing values with mean, mode and median values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute numerical variables \n",
    "num_feat = ['cylinders','displ']\n",
    "\n",
    "for feat in num_feat:\n",
    "    vehicles_clean[feat] = vehicles_clean[feat].fillna(vehicles_clean[feat].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute categorical variables\n",
    "cat_feat = ['drive', 'trany']\n",
    "for feat in cat_feat:\n",
    "    vehicles_clean[feat] = vehicles_clean[feat].fillna(vehicles_clean[feat].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Missing Values\n",
      "mpgData has 0.2% of data marked as NaNs\n"
     ]
    }
   ],
   "source": [
    "# recheck the the data for outliers\n",
    "missing_values_per(vehicles_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39749, 47)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 39749 entries, 0 to 43920\n",
      "Data columns (total 47 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   barrels08       39749 non-null  float64\n",
      " 1   charge240       39749 non-null  float64\n",
      " 2   city08          39749 non-null  int64  \n",
      " 3   city08U         39749 non-null  float64\n",
      " 4   cityCD          39749 non-null  float64\n",
      " 5   cityE           39749 non-null  float64\n",
      " 6   cityUF          39749 non-null  float64\n",
      " 7   co2             39749 non-null  int64  \n",
      " 8   co2TailpipeGpm  39749 non-null  float64\n",
      " 9   comb08          39749 non-null  int64  \n",
      " 10  comb08U         39749 non-null  float64\n",
      " 11  combE           39749 non-null  float64\n",
      " 12  combinedCD      39749 non-null  float64\n",
      " 13  combinedUF      39749 non-null  float64\n",
      " 14  cylinders       39749 non-null  float64\n",
      " 15  displ           39749 non-null  float64\n",
      " 16  drive           39749 non-null  object \n",
      " 17  fuelCost08      39749 non-null  int64  \n",
      " 18  fuelType1       39749 non-null  object \n",
      " 19  ghgScore        39749 non-null  int64  \n",
      " 20  highway08       39749 non-null  int64  \n",
      " 21  highway08U      39749 non-null  float64\n",
      " 22  highwayCD       39749 non-null  float64\n",
      " 23  highwayE        39749 non-null  float64\n",
      " 24  highwayUF       39749 non-null  float64\n",
      " 25  hlv             39749 non-null  int64  \n",
      " 26  hpv             39749 non-null  int64  \n",
      " 27  lv2             39749 non-null  int64  \n",
      " 28  lv4             39749 non-null  int64  \n",
      " 29  make            39749 non-null  object \n",
      " 30  model           39749 non-null  object \n",
      " 31  mpgData         39654 non-null  object \n",
      " 32  phevBlended     39749 non-null  bool   \n",
      " 33  pv2             39749 non-null  int64  \n",
      " 34  pv4             39749 non-null  int64  \n",
      " 35  range           39749 non-null  int64  \n",
      " 36  rangeCity       39749 non-null  float64\n",
      " 37  rangeHwy        39749 non-null  float64\n",
      " 38  trany           39749 non-null  object \n",
      " 39  UCity           39749 non-null  float64\n",
      " 40  UHighway        39749 non-null  float64\n",
      " 41  VClass          39749 non-null  object \n",
      " 42  year            39749 non-null  int64  \n",
      " 43  youSaveSpend    39749 non-null  int64  \n",
      " 44  phevCity        39749 non-null  int64  \n",
      " 45  phevHwy         39749 non-null  int64  \n",
      " 46  phevComb        39749 non-null  int64  \n",
      "dtypes: bool(1), float64(21), int64(18), object(7)\n",
      "memory usage: 14.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vehicles_clean.shape)\n",
    "display(vehicles_clean.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create a copy of the clean dataset\n",
    "vehicles_new = vehicles_clean.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see some conlusions based on clean set of data for single fuel type and non hybrid make cars:\n",
    "\n",
    "###  Which engine size type is most popular over the years?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data by year to calculate engine disp\n",
    "group_year = vehicles_new.groupby(['year'])\n",
    "average_year = group_year['displ'].agg([np.mean])\n",
    "average_year['year'] = average_year.index\n",
    "\n",
    "\n",
    "def plot_graph(df, aes_x, aes_y, x_lab='', y_lab='', title='', color='red', geom_type = 'line'):\n",
    "         \n",
    "    if geom_type =='line':\n",
    "        print(ggplot(df, aes(aes_x, aes_y)) +\\\n",
    "        geom_line(color=color) + xlab(x_lab) + ylab(y_lab) + ggtitle(title))\n",
    "    elif geom_type == 'point':\n",
    "        print(ggplot(df, aes(aes_x, aes_y)) +\\\n",
    "        geom_point(color=color) + xlab(x_lab) + ylab(y_lab) + ggtitle(title)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (144500401782)>\n"
     ]
    }
   ],
   "source": [
    "# Plot displacement against year\n",
    "aes_x = 'year'\n",
    "aes_y = 'mean'\n",
    "x_lab = \"year\"\n",
    "y_lab = \"displ\"\n",
    "title = \"Which engine size type is most popular over the years?\"\n",
    "plot_graph(average_year, aes_x, aes_y, x_lab, y_lab, title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer\n",
    "Displacement has started decreasing its popularity in 2008 that means more small cars with small engine types were used after 2008"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How does the size of a car engine affect its fuel consumption?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2. , 4.9, 2.2, 5.2, 1.8, 1.6, 2.3, 2.8, 4. , 5. , 3.3, 3.1, 3.8,\n",
       "       4.6, 3.4, 3. , 5.9, 2.5, 4.5, 6.8, 2.4, 2.9, 5.7, 4.3, 3.5, 5.8,\n",
       "       3.2, 4.2, 1.9, 2.6, 7.4, 3.9, 1.5, 1.3, 4.1, 8. , 6. , 3.6, 5.4,\n",
       "       5.6, 1. , 2.1, 1.2, 6.5, 2.7, 4.7, 5.5, 1.1, 5.3, 4.4, 3.7, 6.7,\n",
       "       4.8, 1.7, 6.2, 8.3, 1.4, 6.1, 7. , 8.4, 6.3, 6.6, 6.4, 0.9])"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets take two important featues - comb08 and displ \n",
    "pd.unique(vehicles_new.comb08)\n",
    "pd.unique(vehicles_new.displ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dy+jPbt/n6jXegRKsLqS8L/+gjCImM0+g9Mt/tNrGByiXAR05kv1o2d58ymf76ZTX8xJKf+u2mflo9Zq9jZKJX0UJ+k0FzFMofaXnUwL0ti1NycdSjgl/bTvZIjP/QukSeDnlM3gSJSnZpaF6TVl9g4Nea6zeUGVtK7WOIK1Glp4MrJiZ3glFE150uLZTE4N3+lEvWRv4YZRrUX9P6X88DPi2wVJStwyY6hnV9WkHUprF1qVcs3kmS774XtIkF+V6+N8Br8/Mv7ct25jSLTWLconRfqM9gbZJVpI0aUW5s9HXKdcyP7NDwPwLsHdmXhQR36Dc1OGro9nWlP15L0lST9gHeA+wyP2dq8uPVsjH78F9Ml0MfrJJVpI0oUTEbB7/JZxWA9n24wP5+A3nO61qDcrI8yHzePxOZSO2VAPmfn2zbP/VpHf8gpuGLyRNBjNWGpM7+3R9rH/m6ofx+C/htDqMcqOVuvpZ+BrsPurdLakjM0xJ0kRzDKX5tN3ACNdzMwv/ostqdGi6rcuAKUlqVLeDY6pm14Fu65Hlp/L+HREvzczfUm4y8ZPRrs9BP5KkRvX39XX16FZEnBsRQ/dBfgvldolXU36x6UujXe9SvazEPkz1Avsw1TPGqA/zff0rdXWs/9Kj8yfkr6aYYUqSVIN9mJKkRvVPyPywewZMSVKjerXp0oApSWpUEwN3JqJePRGQJKlRZpiSpEb1aiZmwJQkNcpBP5Ik1WCGKUlSDX0O+pEkaeoyw5QkNapXMzEDpiSpUQ76kSSpBjNMSZJq8E4/kiRNYWaYkqRG9WomZsCUJDXKQT+SJNXQqxlmr+6XJEmNMsOUJDWqn95skzVgSpIaZR+mJEk19GpfnwFTktSoXs0we/VEQJKkRplhSpIa5aAfSZJq6NUmWQOmJKlRvdrXZ8CUJDXKDFOSpAkmInYHDgamA8dk5rFtyzcFTgCWBW4C3pqZA6PZVq9mzpKkcdJPX1ePuiJiTeAIYAtgY2DfiNigrdgXgY9n5kZAAgeOfr8kSWpQf193jxHYBjg/M+/MzAXAWcDObWWmAbOq/2cA9492v2ySlSQ1qtsuzIiYDczusGigrTl1DWBey/Q8YLO253wA+N+IOAZYAGw+2nqZYUqSJpq5wA0dHnPbyvUDgy3TfcCjQxMRsQLwDWCbzFwdOA745mgrZYYpSWpUA6NkjwFO7jB/oG36ZuBlLdOrAbe2TG8I3J+Zl1TTJwCfGm2lDJiSpEZ1e6efqtl1oEbR84BDI2JVSnPrHGDfluXXAWtHRGRmAjsAfxhtvWySlSQ1amkN+snMW4CDgAuAK4DTM/OSiDg3Il6QmXcBewJnRsSfgHcAe412v/oGBweHL9WQ/fpmLb2NSWPk+AU3jXcVpGbMWGlMbjFw+uwnd3Ws333gnxPy1gdmmJIk1WAfpiSpURMyPWyAAVOS1Kj+vt4MmQZMSVKjejNc2ocpSVItZpiSpEb1aoZpwJQkNcqAKUlSDX0O+pEkaXi9GS4d9CNJUi1mmJKkRvVqJmbAlCQ1qke7MA2YkqRm9fVoL6YBU5LUqN4Ml73b1CxJUqPMMCVJjerVDNOAKUlqVH+PRkwDpiSpUb066Mc+TEmSajDDlCQ1qjfzSwOmJKlh3rhAkqQaejReGjAlSc3q79GQ6aAfSZJqMMOUJDWqN/NLA6YkqWEO+pEkqYYejZcGTElSs5bmnX4iYnfgYGA6cExmHtu2PIATgJWB24A3ZeZdo9mWg34kSZNSRKwJHAFsAWwM7BsRG7Qs7wPOBj6TmRsBlwMfGe32zDAlSY1aijdf3wY4PzPvBIiIs4CdgU9WyzcFFmTmT6vp/wJmj3ZjBkxJUqO6jZcRMZvOgW0gMwdaptcA5rVMzwM2a5leH7gtIr4BbAL8FXjvaOtlk6wkqVF9XT6AucANHR5z2zbVDwy2bfrRlullgK2Ar2bmpsDfgKNGu19mmJKkieYY4OQO8wfapm8GXtYyvRpwa8v0bcC1mXlpNf0/wFmjrZQBU5LUqG5HyVbNrgM1ip4HHBoRqwILgDnAvi3LfwesGhEbZeYfge2B/xttvWySlSQ1qq+vu0ddmXkLcBBwAXAFcHpmXhIR50bECzLzfuCNwNcj4krgFcABo96vwcHB4Us1ZL++WUtvY1PQepu9gJ0+exhHbb3deFelpx2/4KbxrsKkd/4vf82xXzuRZaZNY86Ob2DXnXZcaPkRRx7F1XkNALffcQezZs7kzG/+N2ef+1NOOvVb9Pf3M2eH7dl9153HofY9ZMZKYzKe9aLV1u7qWP+i226akPc+sEm2R7z6g/uz+dvexAML7hvvqkhL9NBDD/PpLxzNWaedzAorrMCb99ybrbfcglVXWeWxMgd98AOPld39HfvwqUM+BsDnjv4i55x1BjNmzGC7Obux3WtfzUqzZo3LfmjxJmS0a4BNsj3i9utv4ISd3jre1ZCGdf0NN7DO2mux0qxZLDt9Os/fZCMuvfyKjmVPO+PbvPRFmxPPWB+AeMb63HPvvTz4wAMMDg7S16s3LdWEtMQMMyJuYOEhu0P6gMHMfNqY1Eojdvn3zuZJ664z3tWQhnXvggXMXHHFx6afMOMJ3HvPvYuUe/Chhzjju9/nrFNPfmzeM57+dObs/nZWWGF5XvWKrZk1c+bSqLJGqFdPZIZrkt1qaVRCUu87+tivctnlfySvvY7nbficx+YvuG8BMzsEvt9fdAkv3HQTZs4swfXqa67lwt/8ll+c8wNmzFiBDx70CX7y8/N43au2WWr7oHp6M1wOEzAz80aAiJgOvIcywuhh4FzgG2NeO0k94/3veRdQ+iW3m7MbA/PnM2PGDC697Areucei3Qm/u/gStnzpSx6bnrniiiy/3HIst/xyTJs2jSc+cWXuvvuepVZ/1TclA2aLE4EVgK9T+j33ADZk0bsuSNISTZ++DB85YC7vfPf7GBwcZM4O2/OUJz+ZgfnzOfiTR/CVL3wOgBtu/Ac7bv/4iO8111id3ea8kd332ofp06ezzlpr8sY3vH68dkNL0KtNsrUuK4mIqzPzWS3T/cBfMnODJTxtEV5Wol7gZSXqGWN0Wcn/rbFuV8f6599644SMuHVHyd4QEeu3TD8FuGUM6iNJmuT6+7p7TFR1m2SnA3+MiF8Bj1B+e+zWiDgfIDNfMUb1kyRNMn0TOep1oW7A/FTb9JFNV0SS1Bt6tAuzdpPs74G7MvOXwJrA64ErM/OX1TxJknpa3YB5GvCWiNgMOAy4m84/vSJJmuKW1s3Xl7a6AfOpmflhyk+nnJiZn6IM/JEkaSF9fX1dPSaqun2Yy0TEKpSfSdkpIlajXJcpSdJCJnDM60rdDPNI4GLgx5n5F+BXLDoQSJKkqZ1hZubpwOkts56dmY+MTZUkSZp4hvu1knMy8/WdfrUkIvDXSiRJ7SZwktiV4TLM30bEHsChS6EukqQe0N+jEXO4gPnM6vE0YH3Kr5Q8ArwWuBI4ZUxrJ0madHo0Xg778157AUTEBcBGmfmvanpl4AdjXjtJ0qQzkQfudKPuKNk1gDtbphcAqzdfHUmSJqa612H+GPh5RHyP8tuguwLfHrNaSZImrb66qdgkU/eykg9ExBxgK8po2c9n5tljWTFJ0uTUq02ydTNMMvO7wHfHsC6SpB7Qo/Gydh+mJElTWu0MU5KkOqZ8k6wkSXX0aLw0YEqSmtWrd/qxD1OS1Kil+QPSEbF7RFwVEddGxHuWUG676r7oo2bAlCRNShGxJnAEsAWwMbBvRGzQodxTgM9T7iMwagZMSVKjluLvYW4DnJ+Zd2bmAuAsYOcO5U4EDut2v+zDlCQ1qtsuzIiYDczusGggMwdaptcA5rVMzwM2a1vX+4DLgIu6q5UZpiSpYQ30Yc4FbujwmNu2qX4W/q3mPuDRoYmI2BCYA3yqif0yw5QkNaqvv9tRsoPHACd3WDDQNn0z8LKW6dWAW1umd6H8UMilwLLAGhHx68xsfU5tBkxJ0oRSNbsO1Ch6HnBoRKxK+RWtOcC+Lev5BPAJgIhYD7hwtMESbJKVJDVsaV1Wkpm3AAcBFwBXAKdn5iURcW5EvKDx/RocHBy+VEP265u19DYmjZHjF9w03lWQmjFjpTG5w8D/e+EGXR3rn/KHqybknQ9skpUkNapHb/Rjk6wkSXWYYUqSGuWvlUiSVEOPxksDpiSpWWaYkiTV0KPx0kE/kiTVYYYpSWqUTbKSJNXQ16NtlwZMSVKjzDAlSaqj618rmZh6NHGWJKlZZpiSpGbZJCtJ0vDsw5QkqQ77MCVJmrrMMCVJzbJJVpKk4fX1aJOsAVOS1CwzTEmShterGaaDfiRJqsEMU5LULJtkJUmqoUebZA2YkqRG9eqdfuzDlCSpBjNMSVKzbJKVJKmGHm2SNWBKkhrV16OdfQZMSVKzlmKGGRG7AwcD04FjMvPYtuU7AIcBfcANwF6ZeddottWj5wGSpF4XEWsCRwBbABsD+0bEBi3LZwFfBbbLzI2APwGHjnZ7BkxJUqP6+vu6eozANsD5mXlnZi4AzgJ2blk+HXhPZt5STf8JWGe0+2WTrCSpWV02yUbEbGB2h0UDmTnQMr0GMK9leh6w2dBEZt4BfL9a5wrAR4Avj7ZeZpiSpGb193X3gLmU/sb2x9z2LQGDLdN9wKPt1YmIlYAfA3/MzFNGu1tmmJKkRjVwp59jgJM7zB9om74ZeFnL9GrAra0FImJ14GfA+cD7u6mUAVOSNKFUza4DNYqeBxwaEasCC4A5wL5DCyNiGvAj4MzMPLzbehkwJUnNWkp3+snMWyLiIOACYFngxMy8JCLOBT4OrA1sCiwTEUODgS7NzL1Hsz0DpiSpWUvxOszMPB04vW3ettW/l9LgWB0DpiSpUf5aiSRJU5gZpiSpWf5aiSRJw+vVJlkDpiSpWWaYkiTV0KMZpoN+JEmqwQxTktSoEf7iyKRhwJQkNatHm2QNmJKkZplhSpI0PC8racDxC25ampuTxsR+T1h7vKsgNeL4wbvHuwqTihmmJKlZNslKklSDTbKSJNXQowHTGxdIklSDGaYkqVk9mmEaMCVJzervzcZLA6YkqVlmmJIk1dCjAbM382ZJkhpmhilJalaPZpgGTElSsxz0I0lSDWaYkiTV0KMBszfzZkmSGmaGKUlqVo9mmAZMSVKzluKgn4jYHTgYmA4ck5nHti3fGDgRmAX8CtgvMx8ezbZskpUkNauvr7tHTRGxJnAEsAWwMbBvRGzQVuw04D8z85lAH7DPaHfLgClJmqy2Ac7PzDszcwFwFrDz0MKIWBdYITMvqmadDOwy2o3ZJCtJalaXfZgRMRuY3WHRQGYOtEyvAcxrmZ4HbDbM8rVGWy8zTElSs7pvkp0L3NDhMbdtS/3AYOuWgUdHsHxEzDAlSY3q637QzzGU5tN2A23TNwMva5leDbi1bfnqS1g+IgZMSVKzumySrZpdB2oUPQ84NCJWBRYAc4B9W9ZzY0T8OyJempm/Bd4G/GS09bJJVpI0KWXmLcBBwAXAFcDpmXlJRJwbES+oir0FODoirgZWBL402u2ZYUqSmrUUb1yQmacDp7fN27bl/z+y8ECgUTNgSpKa5Z1+JEmqwZ/3kiSphh7NMHvzNECSpIaZYUqSmtWjGaYBU5LULAOmJEk19Oign97cK0mSGmaGKUlqlk2ykiTVYMCUJKmGHu3DNGBKkprVoxlmb54GSJLUMDNMSVKzejTDNGBKkpplwJQkqQYH/UiSVEOPZpi9eRogSVLDzDAlSc3q0QzTgClJalZfbzZeGjAlSc3q780MszdPAyRJapgZpiSpWTbJSpJUg4N+JEmqwRsXSJJUQ49mmL15GiBJUsPMMCVJzXLQjyRJNUyAJtmIWAc4DXgykMBbMvPetjKrAycBqwGPAgdm5vmLW2dvngZIksZPf393j2YcBxyXmc8CLgUO6VDmSOBHmbkx8Gbg9IiYttjdaqpmkiRNBBExHdgSOKuadTKwS4ei3wdOr/6/DlgeWHFx67VJVpLUrC6bZCNiNjC7w6KBzByosYpVgLsz8+Fqeh6wVnuhzPxuy+SBwOWZOX9xKzVgSpKa1f2gn7nAJzrMPww4tHVGROwCHN1W7lpgsG3eo4vbWETMBf4DePmSKmXAlCQ1q/ubrx9DaUZtN9A+IzO/A3yndV7VJHtHREzLzEeA1YFbO20oIj4HbAdsmZk3L6lSBkxJUrO6zDCrZteBLp7/UET8GtiN0ke5B/CT9nJVZrk18NI6Tb0GTElSL3o3cEpEHAz8gzIKlojYD1iD0uT7CeBu4MKIGHretpnZMRs1YEqSmjUBrsPMzBuBrTrMP75lcuWRrNOAKUlqlnf6kSSphu4H/UxIBkxJUrMmQJPsWOjNvFmSpIaZYUqSmmUfpiRJNdiHKUlSDT2aYfbmXkmS1DAzTElSs3p0lKwBU5LUrB5tkjVgSpKa5aAfSZJq6NEMszf3SpKkhplhSpKa5aAfSZJq6O/NxksD5iRz/i9/zbFfO5Flpk1jzo5vYNeddlxo+RFHHsXVeQ0At99xB7NmzuTMb/43Z5/7U0469Vv09/czZ4ft2X3Xnceh9lI96232Anb67GEctfV2410VjYYZpsbbQw89zKe/cDRnnXYyK6ywAm/ec2+23nILVl1llcfKHPTBDzxWdvd37MOnDvkYAJ87+oucc9YZzJgxg+3m7MZ2r301K82aNS77IS3Jqz+4P5u/7U08sOC+8a6KRstBPxpv199wA+usvRYrzZrFstOn8/xNNuLSy6/oWPa0M77NS1+0OfGM9QGIZ6zPPffey4MPPMDg4CB9PXoGqMnv9utv4ISd3jre1ZAWscQMMyJmAQcC9wPHA6cCLwf+AOyVmTeOeQ31mHsXLGDmiis+Nv2EGU/g3nvuXaTcgw89xBnf/T5nnXryY/Oe8fSnM2f3t7PCCsvzqldszayZM5dGlaURu/x7Z/OkddcZ72qoGz16Qj5chnkaMANYG7gY+AXwFOBE4OtjWzUNOfrYr/K2vffj3XMP5N57Fzw2f8F9C5jZIfD9/qJLeOGmmzBzZgmuV19zLRf+5rf84pwfcP6Pf8idd97FT35+3lKrv6Qppr+/u8cENVwf5tqZ+QaAiLglM4+u5p8eER8a26ppyPvf8y6g9EtuN2c3BubPZ8aMGVx62RW8c49Fm65+d/ElbPnSlzw2PXPFFVl+ueVYbvnlmDZtGk984srcffc9S63+kqaYHs0whwuYD0XEq4GVgBUj4vmZ+X8R8YylUDe1mT59GT5ywFze+e73MTg4yJwdtucpT34yA/Pnc/Anj+ArX/gcADfc+A923P7x0YVrrrE6u815I7vvtQ/Tp09nnbXW5I1veP147YYkTUp9g4ODi10YEZsAX6Q03R4AfAO4j9JEu2dm/mxEW7tv/uI3Jk0S+z1h7fGugtSI4wfvHpNU8JHzv9XVsX7aK94yIVPUJWaYmXk5sOXQdES8ANgQuC4zB8a2apKkSWmKNskSEa8BdgHWAh4FbgXOBb43tlWTJE1KPXod5nCXlXwS2IwyWnYe0AesBuwdES/JzAPHvoqSpElliv68127AszPz0daZEfE/wF8o12hKktTzhguY/6Y0xf6jbf66wANjUiNJ0uQ2AZpkI2IdSuvok4EE3pKZi97ppZSdCVwBvDMzL1zcOocLmAcAv46IayhNsoPAGsAzgT1HVn1J0pQwMQb9HAccl5lnRMQhwCHAhxdT9ivAysOtcLhRsudFRFD6MdegXF5yM3BxZpphSpIWNc4ZZkRMp1zhsWM162Tgl3QImBGxG3AP8Kfh1jvsKNnM/Dfwq4jYHtgKeB4wnXKbPEmSFtLtjztExGxgdodFAzUvaVwFuDszH66m51G6F9u3sw4wF3gF8JPhVlrr570i4r+AlwFnUrLMwyNis8z8dJ3nS5I0AnOBT3SYfxhwaOuMiNgFOLqt3LWULsRW7YNX+yk34/nPzLy/NKYuWd3fw3w98PzMfKja0AnApYABU5K0sO6bZI+hNKO2G2ifkZnfAb7TOq9qkr0jIqZl5iPA6pR7CLR6VvX4RhUs1wdOjIh9MvOCTpWqGzDvAmYCd1bTywLzaz5XkjSVdBkwq2bXgS6e/1BE/JpyaeTpwB60Nblm5lWU27wCEBEXAoeOepRsRJxESWv7gT9GxNnAw8C2wNWj2RFJUo+bGDcueDdwSkQcTLk08s0AEbEfsEZmfnykKxwuw7yw+vvLtvmXjXRDkiQtLZl5I2Wgavv84xdTfpGy7Ya7rOSUof+rCzuHvU5FkjTFTYAbF4yFuqNkjwT2Be6oZvVRmmqfNkb1kiRNVhPjxgWNqzvoZ0dgzcXdVkiSpMdM5QyTcgeE5QADpiRpyaZ4hnkqcF1E/JkyShaAzHzFmNRKkqQJpm7APALYH7hxDOsiSeoFU7xJdn5mfnNMayJJ6g0T4zrMxtUNmJdFxHcpd0p4cGimQVSStIgpnmE+AbgbeGnLvEHAgClJWthUHvSTmXtVN7ON6jl/afnZFEmSel6tvDkink/5uZRTgJOAf0TE5mNZMUnSJNXX391jgqrbJPslYLfMvBggIl4EfBnYbKwqJkmapHq0SbZuKF9xKFgCZOZFwPJjUyVJ0qTWoxlm3ZrdGRE7DE1ExI48fl9ZSZJ6Xt0m2X2BcyLiGzx+4/WXjFmtJEmTV//EzRK7UXevXgfcB6wLbA3cToffGZMkqa+vr6vHRDWSDHOzzLwP+FM1avZi4GtjVjNJ0uQ0gfshu1E3YE6n5Q4/1f+DzVdHkjTpTeAssRt1A+YPgPMj4kxKoJwD/HCsKiVJ0kRT904/H46InYGXAw8BX8rMH4xlxSRJk9QUb5IlM88CzhrDukiSesEUb5KVJKmeHr2sxIApSWpWj2aYvXkaIElSw8wwJUnNmuqDfiRJqqVHm2QNmJKkhvVmwOzNvFmSpIaZYUqSmjUBmmQjYh3gNODJQAJvycx728osC3weeBmwLPD+zPzfxa3TDFOS1Ky+vu4ezTgOOC4znwVcChzSocyHgFWATYFdgZMiYrEVMGBKkhrW1+WjOxExHdiSx+9OdzKwS4eiuwGfyczBzLwSeNWSKmCTrCSpWV1miRExG5jdYdFAZg7UWMUqwN2Z+XA1PQ9Yq0O59YGXR8SxlHj4scy8anErNWBKkiaaucAnOsw/DDi0dUZE7AIc3VbuWhb9CcpHO6xvGUog3RJ4LvCziHhWZs7vVCkDpiSpWd23qh5DaUZtN9A+IzO/A3yndV7VJHtHREzLzEeA1YFbO6zvNuCMzBwE/hQRNwEBXNKpUgZMSVLDuouYVbPrQBfPfygifk3pozwd2AP4SYeiP6rKXB4RTwPWoYyo7chBP5KkZk2MUbLvBvaNiKsol40cDBAR+0XEJ6syHwHWiIgrKcFz78U1xwL0DQ62N/OOofvmL8WNSWNjvyesPd5VkBpx/ODdY3LB5OBt13V1rO9bbf3xv5CzAzNMSZJqsA9TktSwCZkgds2AKUlq1gS4Nd5YMGBKkhrWmwHTPkxJkmoww5QkNcsmWUmSajBgSpJUhwFTkqRh9fVohumgH0mSajDDlCQ1q0czTAOmJKlhBkxJkobXoxmmfZiSJNVghilJalaPZpgGTElSwwyYkiQNzwxTkqQaejNeOuhHkqQ6zDAlSQ3rzRTTgClJapZ9mJIk1WDAlCSpjt4MmA76kSSpBjNMSVKzbJKVJKkGA6YkSXX0ZsC0D1OSpBr6BgcHx7sOkiRNeGaYkiTVYMCUJKkGA6YkSTUYMCVJqsGAKUlSDQZMSZJqMGBKklSDAVOSpBoMmJIk1eC9ZCehiDgZuAbYIjO3HeFz1wMuzMz1mq+Z1JyI2BPYKjP37LBsG+AoYAXg25l5cDV/U+AEYFngJuCtmTmwlKqsHmeGOXndOtJgKfWCiFgB+G9gB+DZwAsj4nXV4i8CH8/MjYAEDhyfWqoXmWFOAhHRB3wBeD1wKzANuDAi/p6Z60XE7sCHgEeAG4C3Ai8CDgEeAp4KXALsPQ7V1xRRfU4/A7wReJiS6f0E+BrwRGAB8L7M/EPVSrIA2BSYDXwMeBuwEfCDzDygWu36EfGr6vnnAB8FNgOuzcwbqu2eBuxSbWsaMKt67gzgzrHbY001ZpiTwxxgE+A5lAPD+m3LDwdenZnPpwTMZ1XzXwLsX00vD7xnqdRWU9XOwEuB51KC2l6UIPelzHwe8H7grIhYriq/Rma+mBJkTwL2AzYG9omIlaoyT6V8/jcFtgDeAKwBzGvZ7jxgrer/DwBfj4h5wKuA45vfTU1VBszJYSvge5n5UGbeDpzbtvxHwG8j4nPAdzPzimr+r7IYBE4FXrG0Kqwp6eXAmZn5QGbeSwlwq2Tm9wAy8yJKxhdV+Z9Uf28E/pKZ/8zMe6oyK1fLzs7M2zPzQeBMynehH2j9maU+4NGqqfYbwDaZuTpwHPDNsdlVTUUGzMlhkIV/kfXh1oWZuT/lLPwu4LSIeGuHcv3tz5Ma9hALB7KnsegvCffxeFfQgy3zF/fZbP8MPwTcDKzeMn81SlfFhsD9mXlJNf8ESoCVGmHAnBzOA3aNiOUiYmXgtUMLImKZiLgW+FdmfppyRr1JtXiLiFgzIvqBPXj8jF4aC78C5kTE9IiYQckIByNiJ4CIeBEluP1lBOvcNiJmR8TywJso34WLy+pi/YiYBuxO+WxfB6wdEUMZ7A7AH5rYMQkMmJNCZv4QuJByoDkbuKpl2cPAx4GfR8SllME+n60W30oJoFcBtwAnLr1aa6rJzO8DvwUuowSqL1L60d8XEX8GvgLsVDWv1nU1pQviMuCczPzfzPw3sCfwXcpn+2rgrMy8q5p/ZkT8CXgHpR9VakTf4ODg8KU06UTEVsChmbnVOFdFknqCGaYkSTWYYUqSVIMZpiRJNRgwJUmqwYApSVINBkxJkmowYEqSVIMBU5KkGv4/5EU8exzFzBoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check corrrelation between displ and comb08 for single fuel vehicles\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "enginesize_fuelrelation = vehicles_new[['displ','comb08']]\n",
    "sns.heatmap(enginesize_fuelrelation.corr(),cmap=plt.cm.Reds,annot=True)\n",
    "plt.title('corrrelation between engine size and fuel comsumption',fontsize=15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (144502306835)>\n"
     ]
    }
   ],
   "source": [
    "# Average MPG(Miles Per Gallon) for Engine Displacement \n",
    "aes_x = 'displ'\n",
    "aes_y = 'comb08'\n",
    "x_lab = \"displ\"\n",
    "y_lab = \"Average MPG\"\n",
    "title = \"How does the size of a car engine affect it's fuel consumption?\"\n",
    "plot_graph(vehicles_new, aes_x, aes_y, x_lab, y_lab, title, geom_type='point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Answer\n",
    "\n",
    "There is a negative correlation between engine displ and fuel efficiency that means Small engine size cars are fuel effiecient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model building\n",
    "\n",
    "Target variable= Ucity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barrels08</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>cityUF</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2TailpipeGpm</th>\n",
       "      <th>comb08</th>\n",
       "      <th>...</th>\n",
       "      <th>range</th>\n",
       "      <th>rangeCity</th>\n",
       "      <th>rangeHwy</th>\n",
       "      <th>UCity</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>year</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.000000</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "      <td>39749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.353421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.791995</td>\n",
       "      <td>5.843684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.637349</td>\n",
       "      <td>467.721552</td>\n",
       "      <td>20.121890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.372486</td>\n",
       "      <td>33.838537</td>\n",
       "      <td>2002.175225</td>\n",
       "      <td>-4576.530227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.319235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.391269</td>\n",
       "      <td>9.331172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184.554969</td>\n",
       "      <td>116.406190</td>\n",
       "      <td>4.817131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.957422</td>\n",
       "      <td>8.401597</td>\n",
       "      <td>11.618256</td>\n",
       "      <td>3582.237310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.866875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>185.145833</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>-32500.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.330870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>386.391304</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>-6500.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.480500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>444.350000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.439800</td>\n",
       "      <td>33.333300</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>-4250.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.388824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>522.764706</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>47.087143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>36.873600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>1269.571429</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.111100</td>\n",
       "      <td>76.923100</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>3750.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          barrels08  charge240        city08       city08U   cityCD    cityE  \\\n",
       "count  39749.000000    39749.0  39749.000000  39749.000000  39749.0  39749.0   \n",
       "mean      17.353421        0.0     17.791995      5.843684      0.0      0.0   \n",
       "std        4.319235        0.0      4.391269      9.331172      0.0      0.0   \n",
       "min        6.866875        0.0      6.000000      0.000000      0.0      0.0   \n",
       "25%       14.330870        0.0     15.000000      0.000000      0.0      0.0   \n",
       "50%       16.480500        0.0     17.000000      0.000000      0.0      0.0   \n",
       "75%       19.388824        0.0     20.000000     15.000900      0.0      0.0   \n",
       "max       47.087143        0.0     44.000000     36.873600      0.0      0.0   \n",
       "\n",
       "        cityUF           co2  co2TailpipeGpm        comb08  ...    range  \\\n",
       "count  39749.0  39749.000000    39749.000000  39749.000000  ...  39749.0   \n",
       "mean       0.0    101.637349      467.721552     20.121890  ...      0.0   \n",
       "std        0.0    184.554969      116.406190      4.817131  ...      0.0   \n",
       "min        0.0     -1.000000      185.145833      7.000000  ...      0.0   \n",
       "25%        0.0     -1.000000      386.391304     17.000000  ...      0.0   \n",
       "50%        0.0     -1.000000      444.350000     20.000000  ...      0.0   \n",
       "75%        0.0     -1.000000      522.764706     23.000000  ...      0.0   \n",
       "max        0.0    893.000000     1269.571429     48.000000  ...      0.0   \n",
       "\n",
       "       rangeCity  rangeHwy         UCity      UHighway          year  \\\n",
       "count    39749.0   39749.0  39749.000000  39749.000000  39749.000000   \n",
       "mean         0.0       0.0     22.372486     33.838537   2002.175225   \n",
       "std          0.0       0.0      5.957422      8.401597     11.618256   \n",
       "min          0.0       0.0      0.000000      0.000000   1984.000000   \n",
       "25%          0.0       0.0     18.400000     28.000000   1992.000000   \n",
       "50%          0.0       0.0     21.439800     33.333300   2003.000000   \n",
       "75%          0.0       0.0     25.700000     39.000000   2012.000000   \n",
       "max          0.0       0.0     61.111100     76.923100   2022.000000   \n",
       "\n",
       "       youSaveSpend  phevCity  phevHwy  phevComb  \n",
       "count  39749.000000   39749.0  39749.0   39749.0  \n",
       "mean   -4576.530227       0.0      0.0       0.0  \n",
       "std     3582.237310       0.0      0.0       0.0  \n",
       "min   -32500.000000       0.0      0.0       0.0  \n",
       "25%    -6500.000000       0.0      0.0       0.0  \n",
       "50%    -4250.000000       0.0      0.0       0.0  \n",
       "75%    -2000.000000       0.0      0.0       0.0  \n",
       "max     3750.000000       0.0      0.0       0.0  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_final=vehicles_clean.copy()\n",
    "vehicles_final.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles_cont = vehicles_final.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barrels08</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>cityUF</th>\n",
       "      <th>co2</th>\n",
       "      <th>co2TailpipeGpm</th>\n",
       "      <th>comb08</th>\n",
       "      <th>...</th>\n",
       "      <th>rangeHwy</th>\n",
       "      <th>trany</th>\n",
       "      <th>UCity</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>VClass</th>\n",
       "      <th>year</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.341463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>23.3333</td>\n",
       "      <td>0.455000</td>\n",
       "      <td>Two Seaters</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.574279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.574279</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>0.247000</td>\n",
       "      <td>Two Seaters</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.544828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.132791</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132791</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>Subcompact Cars</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.574279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.574279</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Automatic 3-spd</td>\n",
       "      <td>12.2222</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>Vans</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.544828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.260591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260591</td>\n",
       "      <td>0.292683</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manual 5-spd</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>0.416000</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.717241</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   barrels08  charge240    city08  city08U  cityCD  cityE  cityUF  co2  \\\n",
       "0   0.219512        0.0  0.342105      0.0     0.0    0.0     0.0  0.0   \n",
       "1   0.574279        0.0  0.078947      0.0     0.0    0.0     0.0  0.0   \n",
       "2   0.132791        0.0  0.447368      0.0     0.0    0.0     0.0  0.0   \n",
       "3   0.574279        0.0  0.105263      0.0     0.0    0.0     0.0  0.0   \n",
       "4   0.260591        0.0  0.289474      0.0     0.0    0.0     0.0  0.0   \n",
       "\n",
       "   co2TailpipeGpm    comb08  ...  rangeHwy            trany    UCity  \\\n",
       "0        0.219512  0.341463  ...       0.0     Manual 5-spd  23.3333   \n",
       "1        0.574279  0.097561  ...       0.0     Manual 5-spd  11.0000   \n",
       "2        0.132791  0.487805  ...       0.0     Manual 5-spd  29.0000   \n",
       "3        0.574279  0.097561  ...       0.0  Automatic 3-spd  12.2222   \n",
       "4        0.260591  0.292683  ...       0.0     Manual 5-spd  21.0000   \n",
       "\n",
       "   UHighway           VClass  year youSaveSpend  phevCity phevHwy  phevComb  \n",
       "0  0.455000      Two Seaters  1985     0.827586         0       0         0  \n",
       "1  0.247000      Two Seaters  1985     0.544828         0       0         0  \n",
       "2  0.611000  Subcompact Cars  1985     0.896552         0       0         0  \n",
       "3  0.216667             Vans  1985     0.544828         0       0         0  \n",
       "4  0.416000     Compact Cars  1993     0.717241         0       0         0  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cont_variables = ['barrels08', 'city08', 'city08U','co2', 'co2TailpipeGpm', 'comb08U', \n",
    "                  'comb08', 'highway08' , 'highway08U', 'UHighway','youSaveSpend'\n",
    "                  ]\n",
    "minVec = vehicles_cont[cont_variables].min().copy()\n",
    "maxVec = vehicles_cont[cont_variables].max().copy()\n",
    "vehicles_cont[cont_variables] = (vehicles_cont[cont_variables]-minVec)/(maxVec-minVec)\n",
    "vehicles_cont.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fuelType1, 'make', 'VClass'\n",
    "vehicles_cont=vehicles_cont.drop(['model'], axis=1)\n",
    "vehicles_cont['make']=vehicles_final['make'].astype(str)\n",
    "vehicles_cont = pd.get_dummies(vehicles_cont,prefix=['make'], columns = ['make'])\n",
    "vehicles_cont = pd.get_dummies(vehicles_cont,prefix=['fuelType1'], columns = ['fuelType1'])\n",
    "vehicles_cont = pd.get_dummies(vehicles_cont,prefix=['VClass'], columns = ['VClass'])\n",
    "vehicles_cont = pd.get_dummies(vehicles_cont,prefix=['drive'], columns = ['drive'])\n",
    "vehicles_cont = pd.get_dummies(vehicles_cont,prefix=['trany'], columns = ['trany'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barrels08                                     0\n",
       "charge240                                     0\n",
       "city08                                        0\n",
       "city08U                                       0\n",
       "cityCD                                        0\n",
       "cityE                                         0\n",
       "cityUF                                        0\n",
       "co2                                           0\n",
       "co2TailpipeGpm                                0\n",
       "comb08                                        0\n",
       "comb08U                                       0\n",
       "combE                                         0\n",
       "combinedCD                                    0\n",
       "combinedUF                                    0\n",
       "cylinders                                     0\n",
       "displ                                         0\n",
       "fuelCost08                                    0\n",
       "ghgScore                                      0\n",
       "highway08                                     0\n",
       "highway08U                                    0\n",
       "highwayCD                                     0\n",
       "highwayE                                      0\n",
       "highwayUF                                     0\n",
       "hlv                                           0\n",
       "hpv                                           0\n",
       "lv2                                           0\n",
       "lv4                                           0\n",
       "mpgData                                      95\n",
       "phevBlended                                   0\n",
       "pv2                                           0\n",
       "pv4                                           0\n",
       "range                                         0\n",
       "rangeCity                                     0\n",
       "rangeHwy                                      0\n",
       "UCity                                         0\n",
       "UHighway                                      0\n",
       "year                                          0\n",
       "youSaveSpend                                  0\n",
       "phevCity                                      0\n",
       "phevHwy                                       0\n",
       "phevComb                                      0\n",
       "make_AM General                               0\n",
       "make_ASC Incorporated                         0\n",
       "make_Acura                                    0\n",
       "make_Alfa Romeo                               0\n",
       "make_American Motors Corporation              0\n",
       "make_Aston Martin                             0\n",
       "make_Audi                                     0\n",
       "make_Aurora Cars Ltd                          0\n",
       "make_Autokraft Limited                        0\n",
       "make_Avanti Motor Corporation                 0\n",
       "make_BMW                                      0\n",
       "make_BMW Alpina                               0\n",
       "make_Bentley                                  0\n",
       "make_Bertone                                  0\n",
       "make_Bill Dovell Motor Car Company            0\n",
       "make_Bitter Gmbh and Co. Kg                   0\n",
       "make_Bugatti                                  0\n",
       "make_Buick                                    0\n",
       "make_CCC Engineering                          0\n",
       "make_CX Automotive                            0\n",
       "make_Cadillac                                 0\n",
       "make_Chevrolet                                0\n",
       "make_Chrysler                                 0\n",
       "make_Consulier Industries Inc                 0\n",
       "make_Dabryan Coach Builders Inc               0\n",
       "make_Dacia                                    0\n",
       "make_Daewoo                                   0\n",
       "make_Daihatsu                                 0\n",
       "make_Dodge                                    0\n",
       "make_E. P. Dutton, Inc.                       0\n",
       "make_Eagle                                    0\n",
       "make_Environmental Rsch and Devp Corp         0\n",
       "make_Evans Automobiles                        0\n",
       "make_Excalibur Autos                          0\n",
       "make_Federal Coach                            0\n",
       "make_Ferrari                                  0\n",
       "make_Fiat                                     0\n",
       "make_Ford                                     0\n",
       "make_GMC                                      0\n",
       "make_General Motors                           0\n",
       "make_Genesis                                  0\n",
       "make_Geo                                      0\n",
       "make_Goldacre                                 0\n",
       "make_Grumman Allied Industries                0\n",
       "make_Honda                                    0\n",
       "make_Hummer                                   0\n",
       "make_Hyundai                                  0\n",
       "make_Import Foreign Auto Sales Inc            0\n",
       "make_Import Trade Services                    0\n",
       "make_Infiniti                                 0\n",
       "make_Isis Imports Ltd                         0\n",
       "make_Isuzu                                    0\n",
       "make_J.K. Motors                              0\n",
       "make_JBA Motorcars, Inc.                      0\n",
       "make_Jaguar                                   0\n",
       "make_Jeep                                     0\n",
       "make_Kenyon Corporation Of America            0\n",
       "make_Kia                                      0\n",
       "make_Koenigsegg                               0\n",
       "make_Laforza Automobile Inc                   0\n",
       "make_Lambda Control Systems                   0\n",
       "make_Lamborghini                              0\n",
       "make_Land Rover                               0\n",
       "make_Lexus                                    0\n",
       "make_Lincoln                                  0\n",
       "make_London Coach Co Inc                      0\n",
       "make_Lotus                                    0\n",
       "make_MINI                                     0\n",
       "make_Maserati                                 0\n",
       "make_Maybach                                  0\n",
       "make_Mazda                                    0\n",
       "make_McLaren Automotive                       0\n",
       "make_Mcevoy Motors                            0\n",
       "make_Mercedes-Benz                            0\n",
       "make_Mercury                                  0\n",
       "make_Merkur                                   0\n",
       "make_Mitsubishi                               0\n",
       "make_Mobility Ventures LLC                    0\n",
       "make_Morgan                                   0\n",
       "make_Nissan                                   0\n",
       "make_Oldsmobile                               0\n",
       "make_PAS Inc - GMC                            0\n",
       "make_PAS, Inc                                 0\n",
       "make_Pagani                                   0\n",
       "make_Panos                                    0\n",
       "make_Panoz Auto-Development                   0\n",
       "make_Panther Car Company Limited              0\n",
       "make_Peugeot                                  0\n",
       "make_Pininfarina                              0\n",
       "make_Plymouth                                 0\n",
       "make_Pontiac                                  0\n",
       "make_Porsche                                  0\n",
       "make_Quantum Technologies                     0\n",
       "make_Qvale                                    0\n",
       "make_RUF Automobile                           0\n",
       "make_Ram                                      0\n",
       "make_Red Shift Ltd.                           0\n",
       "make_Renault                                  0\n",
       "make_Rolls-Royce                              0\n",
       "make_Roush Performance                        0\n",
       "make_Ruf Automobile Gmbh                      0\n",
       "make_S and S Coach Company  E.p. Dutton       0\n",
       "make_SRT                                      0\n",
       "make_STI                                      0\n",
       "make_Saab                                     0\n",
       "make_Saleen                                   0\n",
       "make_Saleen Performance                       0\n",
       "make_Saturn                                   0\n",
       "make_Scion                                    0\n",
       "make_Shelby                                   0\n",
       "make_Spyker                                   0\n",
       "make_Sterling                                 0\n",
       "make_Subaru                                   0\n",
       "make_Superior Coaches Div E.p. Dutton         0\n",
       "make_Suzuki                                   0\n",
       "make_TVR Engineering Ltd                      0\n",
       "make_Tecstar, LP                              0\n",
       "make_Texas Coach Company                      0\n",
       "make_Toyota                                   0\n",
       "make_VPG                                      0\n",
       "make_Vector                                   0\n",
       "make_Volga Associated Automobile              0\n",
       "make_Volkswagen                               0\n",
       "make_Volvo                                    0\n",
       "make_Wallace Environmental                    0\n",
       "make_Yugo                                     0\n",
       "make_smart                                    0\n",
       "fuelType1_Midgrade Gasoline                   0\n",
       "fuelType1_Premium Gasoline                    0\n",
       "fuelType1_Regular Gasoline                    0\n",
       "VClass_Compact Cars                           0\n",
       "VClass_Large Cars                             0\n",
       "VClass_Midsize Cars                           0\n",
       "VClass_Midsize Station Wagons                 0\n",
       "VClass_Midsize-Large Station Wagons           0\n",
       "VClass_Minicompact Cars                       0\n",
       "VClass_Minivan - 2WD                          0\n",
       "VClass_Minivan - 4WD                          0\n",
       "VClass_Small Pickup Trucks                    0\n",
       "VClass_Small Pickup Trucks 2WD                0\n",
       "VClass_Small Pickup Trucks 4WD                0\n",
       "VClass_Small Sport Utility Vehicle 2WD        0\n",
       "VClass_Small Sport Utility Vehicle 4WD        0\n",
       "VClass_Small Station Wagons                   0\n",
       "VClass_Special Purpose Vehicle 2WD            0\n",
       "VClass_Special Purpose Vehicle 4WD            0\n",
       "VClass_Special Purpose Vehicles               0\n",
       "VClass_Special Purpose Vehicles/2wd           0\n",
       "VClass_Special Purpose Vehicles/4wd           0\n",
       "VClass_Sport Utility Vehicle - 2WD            0\n",
       "VClass_Sport Utility Vehicle - 4WD            0\n",
       "VClass_Standard Pickup Trucks                 0\n",
       "VClass_Standard Pickup Trucks 2WD             0\n",
       "VClass_Standard Pickup Trucks 4WD             0\n",
       "VClass_Standard Pickup Trucks/2wd             0\n",
       "VClass_Standard Sport Utility Vehicle 2WD     0\n",
       "VClass_Standard Sport Utility Vehicle 4WD     0\n",
       "VClass_Subcompact Cars                        0\n",
       "VClass_Two Seaters                            0\n",
       "VClass_Vans                                   0\n",
       "VClass_Vans Passenger                         0\n",
       "VClass_Vans, Cargo Type                       0\n",
       "VClass_Vans, Passenger Type                   0\n",
       "drive_2-Wheel Drive                           0\n",
       "drive_4-Wheel Drive                           0\n",
       "drive_4-Wheel or All-Wheel Drive              0\n",
       "drive_All-Wheel Drive                         0\n",
       "drive_Front-Wheel Drive                       0\n",
       "drive_Part-time 4-Wheel Drive                 0\n",
       "drive_Rear-Wheel Drive                        0\n",
       "trany_Automatic (AM-S10)                      0\n",
       "trany_Automatic (AM-S6)                       0\n",
       "trany_Automatic (AM-S7)                       0\n",
       "trany_Automatic (AM-S8)                       0\n",
       "trany_Automatic (AM5)                         0\n",
       "trany_Automatic (AM6)                         0\n",
       "trany_Automatic (AM7)                         0\n",
       "trany_Automatic (AM8)                         0\n",
       "trany_Automatic (AV-S1)                       0\n",
       "trany_Automatic (AV-S10)                      0\n",
       "trany_Automatic (AV-S6)                       0\n",
       "trany_Automatic (AV-S7)                       0\n",
       "trany_Automatic (AV-S8)                       0\n",
       "trany_Automatic (L3)                          0\n",
       "trany_Automatic (L4)                          0\n",
       "trany_Automatic (S10)                         0\n",
       "trany_Automatic (S4)                          0\n",
       "trany_Automatic (S5)                          0\n",
       "trany_Automatic (S6)                          0\n",
       "trany_Automatic (S7)                          0\n",
       "trany_Automatic (S8)                          0\n",
       "trany_Automatic (S9)                          0\n",
       "trany_Automatic (variable gear ratios)        0\n",
       "trany_Automatic 10-spd                        0\n",
       "trany_Automatic 3-spd                         0\n",
       "trany_Automatic 4-spd                         0\n",
       "trany_Automatic 5-spd                         0\n",
       "trany_Automatic 6-spd                         0\n",
       "trany_Automatic 7-spd                         0\n",
       "trany_Automatic 8-spd                         0\n",
       "trany_Automatic 9-spd                         0\n",
       "trany_Manual 3-spd                            0\n",
       "trany_Manual 4-spd                            0\n",
       "trany_Manual 4-spd Doubled                    0\n",
       "trany_Manual 5-spd                            0\n",
       "trany_Manual 6-spd                            0\n",
       "trany_Manual 7-spd                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_cont.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles_final1 =vehicles_cont.drop(['mpgData','charge240'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "flot_type = vehicles_final1.select_dtypes(include=['float64'])\n",
    "for i in vehicles_final1.columns.values:\n",
    "    vehicles_final1[i] = vehicles_final1[i].astype('int64')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barrels08                                    int64\n",
       "city08                                       int64\n",
       "city08U                                      int64\n",
       "cityCD                                       int64\n",
       "cityE                                        int64\n",
       "cityUF                                       int64\n",
       "co2                                          int64\n",
       "co2TailpipeGpm                               int64\n",
       "comb08                                       int64\n",
       "comb08U                                      int64\n",
       "combE                                        int64\n",
       "combinedCD                                   int64\n",
       "combinedUF                                   int64\n",
       "cylinders                                    int64\n",
       "displ                                        int64\n",
       "fuelCost08                                   int64\n",
       "ghgScore                                     int64\n",
       "highway08                                    int64\n",
       "highway08U                                   int64\n",
       "highwayCD                                    int64\n",
       "highwayE                                     int64\n",
       "highwayUF                                    int64\n",
       "hlv                                          int64\n",
       "hpv                                          int64\n",
       "lv2                                          int64\n",
       "lv4                                          int64\n",
       "phevBlended                                  int64\n",
       "pv2                                          int64\n",
       "pv4                                          int64\n",
       "range                                        int64\n",
       "rangeCity                                    int64\n",
       "rangeHwy                                     int64\n",
       "UCity                                        int64\n",
       "UHighway                                     int64\n",
       "year                                         int64\n",
       "youSaveSpend                                 int64\n",
       "phevCity                                     int64\n",
       "phevHwy                                      int64\n",
       "phevComb                                     int64\n",
       "make_AM General                              int64\n",
       "make_ASC Incorporated                        int64\n",
       "make_Acura                                   int64\n",
       "make_Alfa Romeo                              int64\n",
       "make_American Motors Corporation             int64\n",
       "make_Aston Martin                            int64\n",
       "make_Audi                                    int64\n",
       "make_Aurora Cars Ltd                         int64\n",
       "make_Autokraft Limited                       int64\n",
       "make_Avanti Motor Corporation                int64\n",
       "make_BMW                                     int64\n",
       "make_BMW Alpina                              int64\n",
       "make_Bentley                                 int64\n",
       "make_Bertone                                 int64\n",
       "make_Bill Dovell Motor Car Company           int64\n",
       "make_Bitter Gmbh and Co. Kg                  int64\n",
       "make_Bugatti                                 int64\n",
       "make_Buick                                   int64\n",
       "make_CCC Engineering                         int64\n",
       "make_CX Automotive                           int64\n",
       "make_Cadillac                                int64\n",
       "make_Chevrolet                               int64\n",
       "make_Chrysler                                int64\n",
       "make_Consulier Industries Inc                int64\n",
       "make_Dabryan Coach Builders Inc              int64\n",
       "make_Dacia                                   int64\n",
       "make_Daewoo                                  int64\n",
       "make_Daihatsu                                int64\n",
       "make_Dodge                                   int64\n",
       "make_E. P. Dutton, Inc.                      int64\n",
       "make_Eagle                                   int64\n",
       "make_Environmental Rsch and Devp Corp        int64\n",
       "make_Evans Automobiles                       int64\n",
       "make_Excalibur Autos                         int64\n",
       "make_Federal Coach                           int64\n",
       "make_Ferrari                                 int64\n",
       "make_Fiat                                    int64\n",
       "make_Ford                                    int64\n",
       "make_GMC                                     int64\n",
       "make_General Motors                          int64\n",
       "make_Genesis                                 int64\n",
       "make_Geo                                     int64\n",
       "make_Goldacre                                int64\n",
       "make_Grumman Allied Industries               int64\n",
       "make_Honda                                   int64\n",
       "make_Hummer                                  int64\n",
       "make_Hyundai                                 int64\n",
       "make_Import Foreign Auto Sales Inc           int64\n",
       "make_Import Trade Services                   int64\n",
       "make_Infiniti                                int64\n",
       "make_Isis Imports Ltd                        int64\n",
       "make_Isuzu                                   int64\n",
       "make_J.K. Motors                             int64\n",
       "make_JBA Motorcars, Inc.                     int64\n",
       "make_Jaguar                                  int64\n",
       "make_Jeep                                    int64\n",
       "make_Kenyon Corporation Of America           int64\n",
       "make_Kia                                     int64\n",
       "make_Koenigsegg                              int64\n",
       "make_Laforza Automobile Inc                  int64\n",
       "make_Lambda Control Systems                  int64\n",
       "make_Lamborghini                             int64\n",
       "make_Land Rover                              int64\n",
       "make_Lexus                                   int64\n",
       "make_Lincoln                                 int64\n",
       "make_London Coach Co Inc                     int64\n",
       "make_Lotus                                   int64\n",
       "make_MINI                                    int64\n",
       "make_Maserati                                int64\n",
       "make_Maybach                                 int64\n",
       "make_Mazda                                   int64\n",
       "make_McLaren Automotive                      int64\n",
       "make_Mcevoy Motors                           int64\n",
       "make_Mercedes-Benz                           int64\n",
       "make_Mercury                                 int64\n",
       "make_Merkur                                  int64\n",
       "make_Mitsubishi                              int64\n",
       "make_Mobility Ventures LLC                   int64\n",
       "make_Morgan                                  int64\n",
       "make_Nissan                                  int64\n",
       "make_Oldsmobile                              int64\n",
       "make_PAS Inc - GMC                           int64\n",
       "make_PAS, Inc                                int64\n",
       "make_Pagani                                  int64\n",
       "make_Panos                                   int64\n",
       "make_Panoz Auto-Development                  int64\n",
       "make_Panther Car Company Limited             int64\n",
       "make_Peugeot                                 int64\n",
       "make_Pininfarina                             int64\n",
       "make_Plymouth                                int64\n",
       "make_Pontiac                                 int64\n",
       "make_Porsche                                 int64\n",
       "make_Quantum Technologies                    int64\n",
       "make_Qvale                                   int64\n",
       "make_RUF Automobile                          int64\n",
       "make_Ram                                     int64\n",
       "make_Red Shift Ltd.                          int64\n",
       "make_Renault                                 int64\n",
       "make_Rolls-Royce                             int64\n",
       "make_Roush Performance                       int64\n",
       "make_Ruf Automobile Gmbh                     int64\n",
       "make_S and S Coach Company  E.p. Dutton      int64\n",
       "make_SRT                                     int64\n",
       "make_STI                                     int64\n",
       "make_Saab                                    int64\n",
       "make_Saleen                                  int64\n",
       "make_Saleen Performance                      int64\n",
       "make_Saturn                                  int64\n",
       "make_Scion                                   int64\n",
       "make_Shelby                                  int64\n",
       "make_Spyker                                  int64\n",
       "make_Sterling                                int64\n",
       "make_Subaru                                  int64\n",
       "make_Superior Coaches Div E.p. Dutton        int64\n",
       "make_Suzuki                                  int64\n",
       "make_TVR Engineering Ltd                     int64\n",
       "make_Tecstar, LP                             int64\n",
       "make_Texas Coach Company                     int64\n",
       "make_Toyota                                  int64\n",
       "make_VPG                                     int64\n",
       "make_Vector                                  int64\n",
       "make_Volga Associated Automobile             int64\n",
       "make_Volkswagen                              int64\n",
       "make_Volvo                                   int64\n",
       "make_Wallace Environmental                   int64\n",
       "make_Yugo                                    int64\n",
       "make_smart                                   int64\n",
       "fuelType1_Midgrade Gasoline                  int64\n",
       "fuelType1_Premium Gasoline                   int64\n",
       "fuelType1_Regular Gasoline                   int64\n",
       "VClass_Compact Cars                          int64\n",
       "VClass_Large Cars                            int64\n",
       "VClass_Midsize Cars                          int64\n",
       "VClass_Midsize Station Wagons                int64\n",
       "VClass_Midsize-Large Station Wagons          int64\n",
       "VClass_Minicompact Cars                      int64\n",
       "VClass_Minivan - 2WD                         int64\n",
       "VClass_Minivan - 4WD                         int64\n",
       "VClass_Small Pickup Trucks                   int64\n",
       "VClass_Small Pickup Trucks 2WD               int64\n",
       "VClass_Small Pickup Trucks 4WD               int64\n",
       "VClass_Small Sport Utility Vehicle 2WD       int64\n",
       "VClass_Small Sport Utility Vehicle 4WD       int64\n",
       "VClass_Small Station Wagons                  int64\n",
       "VClass_Special Purpose Vehicle 2WD           int64\n",
       "VClass_Special Purpose Vehicle 4WD           int64\n",
       "VClass_Special Purpose Vehicles              int64\n",
       "VClass_Special Purpose Vehicles/2wd          int64\n",
       "VClass_Special Purpose Vehicles/4wd          int64\n",
       "VClass_Sport Utility Vehicle - 2WD           int64\n",
       "VClass_Sport Utility Vehicle - 4WD           int64\n",
       "VClass_Standard Pickup Trucks                int64\n",
       "VClass_Standard Pickup Trucks 2WD            int64\n",
       "VClass_Standard Pickup Trucks 4WD            int64\n",
       "VClass_Standard Pickup Trucks/2wd            int64\n",
       "VClass_Standard Sport Utility Vehicle 2WD    int64\n",
       "VClass_Standard Sport Utility Vehicle 4WD    int64\n",
       "VClass_Subcompact Cars                       int64\n",
       "VClass_Two Seaters                           int64\n",
       "VClass_Vans                                  int64\n",
       "VClass_Vans Passenger                        int64\n",
       "VClass_Vans, Cargo Type                      int64\n",
       "VClass_Vans, Passenger Type                  int64\n",
       "drive_2-Wheel Drive                          int64\n",
       "drive_4-Wheel Drive                          int64\n",
       "drive_4-Wheel or All-Wheel Drive             int64\n",
       "drive_All-Wheel Drive                        int64\n",
       "drive_Front-Wheel Drive                      int64\n",
       "drive_Part-time 4-Wheel Drive                int64\n",
       "drive_Rear-Wheel Drive                       int64\n",
       "trany_Automatic (AM-S10)                     int64\n",
       "trany_Automatic (AM-S6)                      int64\n",
       "trany_Automatic (AM-S7)                      int64\n",
       "trany_Automatic (AM-S8)                      int64\n",
       "trany_Automatic (AM5)                        int64\n",
       "trany_Automatic (AM6)                        int64\n",
       "trany_Automatic (AM7)                        int64\n",
       "trany_Automatic (AM8)                        int64\n",
       "trany_Automatic (AV-S1)                      int64\n",
       "trany_Automatic (AV-S10)                     int64\n",
       "trany_Automatic (AV-S6)                      int64\n",
       "trany_Automatic (AV-S7)                      int64\n",
       "trany_Automatic (AV-S8)                      int64\n",
       "trany_Automatic (L3)                         int64\n",
       "trany_Automatic (L4)                         int64\n",
       "trany_Automatic (S10)                        int64\n",
       "trany_Automatic (S4)                         int64\n",
       "trany_Automatic (S5)                         int64\n",
       "trany_Automatic (S6)                         int64\n",
       "trany_Automatic (S7)                         int64\n",
       "trany_Automatic (S8)                         int64\n",
       "trany_Automatic (S9)                         int64\n",
       "trany_Automatic (variable gear ratios)       int64\n",
       "trany_Automatic 10-spd                       int64\n",
       "trany_Automatic 3-spd                        int64\n",
       "trany_Automatic 4-spd                        int64\n",
       "trany_Automatic 5-spd                        int64\n",
       "trany_Automatic 6-spd                        int64\n",
       "trany_Automatic 7-spd                        int64\n",
       "trany_Automatic 8-spd                        int64\n",
       "trany_Automatic 9-spd                        int64\n",
       "trany_Manual 3-spd                           int64\n",
       "trany_Manual 4-spd                           int64\n",
       "trany_Manual 4-spd Doubled                   int64\n",
       "trany_Manual 5-spd                           int64\n",
       "trany_Manual 6-spd                           int64\n",
       "trany_Manual 7-spd                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_final1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_vehicles=vehicles_final1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = model_vehicles.loc[:, model_vehicles.columns != 'UCity']\n",
    "y = model_vehicles.loc[:, model_vehicles.columns == 'UCity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only features which are relevant for the prediction\n",
    "\n",
    "vehicle_model =model_vehicles.drop(['year', 'cityCD',\n",
    "                                                 'cityE', 'cityUF', 'combE', 'combinedCD',\n",
    "                                                 'highwayCD', 'highwayE', 'highwayUF', 'range',\n",
    "                                                 'rangeCity', 'rangeHwy', 'phevCity', \n",
    "                                                 'hpv', 'lv2', 'lv4', 'pv2'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39749, 229)"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_model.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['barrels08', 'city08', 'city08U', 'co2', 'co2TailpipeGpm', 'comb08',\n",
       "       'comb08U', 'combinedUF', 'cylinders', 'displ',\n",
       "       ...\n",
       "       'trany_Automatic 6-spd', 'trany_Automatic 7-spd',\n",
       "       'trany_Automatic 8-spd', 'trany_Automatic 9-spd', 'trany_Manual 3-spd',\n",
       "       'trany_Manual 4-spd', 'trany_Manual 4-spd Doubled',\n",
       "       'trany_Manual 5-spd', 'trany_Manual 6-spd', 'trany_Manual 7-spd'],\n",
       "      dtype='object', length=229)"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_model.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now seperate the dataset as response variable and feature variabes\n",
    "target='UCity'\n",
    "\n",
    "X = vehicle_model.drop(target, axis = 1)\n",
    "Y = vehicle_model[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Here we would also split the data into a training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size =0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying Standard scaling to get optimized result\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31799, 245)"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestClassifier(n_estimators=20, random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random forest on test set: 0.71\n"
     ]
    }
   ],
   "source": [
    "# Print accuracy\n",
    "print('Accuracy of Random forest on test set: {:.2f}'.format(regressor.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Metrics\n",
    "r2 = round(metrics.r2_score(y_test, y_pred),2)\n",
    "rmse = round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2)\n",
    "accuracy_df = accuracy_df.append(pd.DataFrame({'r2':[r2],'rmse':[rmse]},index = ['Random Forest Baseline']))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree Baseline</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest Baseline</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest Baseline</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          r2  rmse\n",
       "Decision Tree Baseline  0.99  0.62\n",
       "Random Forest Baseline  0.99  0.62\n",
       "Random Forest Baseline  0.98  0.79"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9890199155813383"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "tree = DecisionTreeRegressor(criterion='mse',  max_depth=10)         \n",
    "tree.fit(X_train, y_train)\n",
    "tree.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit\n",
    "model = DecisionTreeRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_pred = model.predict(X_test)\n",
    "# Metrics\n",
    "r2 = round(metrics.r2_score(y_test, y_pred),2)\n",
    "rmse = round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2)\n",
    "accuracy_df = pd.DataFrame(columns=('r2', 'rmse'))\n",
    "accuracy_df = accuracy_df.append(pd.DataFrame({'r2':[r2],'rmse':[rmse]}, index = ['Decision Tree Baseline']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree Baseline</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          r2  rmse\n",
       "Decision Tree Baseline  0.99  0.62"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted MPG')"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "grid = gridspec.GridSpec(ncols=2, nrows=1, figure=fig)\n",
    "\n",
    "ax1 = fig.add_subplot(grid[0, 0])\n",
    "\n",
    "\n",
    "sns.scatterplot(x = y_test['UCity'], y = y_pred, ax=ax1)\n",
    "sns.regplot(x = y_test['UCity'], y=y_pred, ax=ax1)\n",
    "\n",
    "ax1.set_title(\"Log of Predictions vs. actuals\")\n",
    "ax1.set_xlabel('Actual MPG')\n",
    "ax1.set_ylabel('predicted MPG')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation\n",
    "Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample.\n",
    "- Used 10-fold Cross-Validation to train these models.\n",
    "Models to evaluate are \n",
    "- Random Forest Model\n",
    "- Decision tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model: mean of score across 10 folds (std dev of score)\n",
      "RF: 0.879116 (0.005041)\n",
      "CART: 0.995419 (0.001432)\n"
     ]
    }
   ],
   "source": [
    "#setting kfold value \n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "models=[]\n",
    "models.append(('RF', RandomForestClassifier(n_estimators=100)))\n",
    "models.append(('CART', DecisionTreeRegressor()))\n",
    "\n",
    "# now evaluate each model \n",
    "results = []\n",
    "names = []\n",
    "print(\"model: mean of score across 10 folds (std dev of score)\")\n",
    "\n",
    "for name, model in models:\n",
    "    # --> split training dataset into 10 parts; train on 9 and test on 1; repeat for all combinations.\n",
    "    kfold = KFold (n_splits=10);\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold);\n",
    "    results.append(cv_results);\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Through this analysis we learn a lot about our dataset, about various research questions based on our analysis taking a set of data from this large dataset.\n",
    "- We also learn that the decision tree model is the best algorithm for this dataset throught the process of cross validation.\n",
    "- I learn many things about vehicles fuel efficiency and how gasoline cars changed over the years.\n",
    "\n"
   ]
  }
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