craiglist_vehicles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7d0db80-8f03-4357-86fd-8e341aefdf33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opendatasets in c:\\users\\admin\\anaconda3\\lib\\site-packages (0.1.22)\n",
      "Requirement already satisfied: tqdm in c:\\users\\admin\\anaconda3\\lib\\site-packages (from opendatasets) (4.65.0)\n",
      "Requirement already satisfied: kaggle in c:\\users\\admin\\anaconda3\\lib\\site-packages (from opendatasets) (1.5.16)\n",
      "Requirement already satisfied: click in c:\\users\\admin\\anaconda3\\lib\\site-packages (from opendatasets) (8.0.4)\n",
      "Requirement already satisfied: colorama in c:\\users\\admin\\appdata\\roaming\\python\\python311\\site-packages (from click->opendatasets) (0.4.6)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\admin\\appdata\\roaming\\python\\python311\\site-packages (from kaggle->opendatasets) (1.16.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\admin\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (2023.7.22)\n",
      "Requirement already satisfied: python-dateutil in c:\\users\\admin\\appdata\\roaming\\python\\python311\\site-packages (from kaggle->opendatasets) (2.8.2)\n",
      "Requirement already satisfied: requests in c:\\users\\admin\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (2.31.0)\n",
      "Requirement already satisfied: python-slugify in c:\\users\\admin\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (5.0.2)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\admin\\appdata\\roaming\\python\\python311\\site-packages (from kaggle->opendatasets) (2.0.7)\n",
      "Requirement already satisfied: bleach in c:\\users\\admin\\anaconda3\\lib\\site-packages (from kaggle->opendatasets) (4.1.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\admin\\appdata\\roaming\\python\\python311\\site-packages (from bleach->kaggle->opendatasets) (23.2)\n",
      "Requirement already satisfied: webencodings in c:\\users\\admin\\appdata\\roaming\\python\\python311\\site-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests->kaggle->opendatasets) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests->kaggle->opendatasets) (3.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install opendatasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec5c4d23-c230-4ac4-8413-f333128aea19",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import opendatasets as od"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aea56b64-3a4e-41cf-bf02-0b33488fee06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "download_url = 'https://www.kaggle.com/datasets/mbaabuharun/craigslist-vehicles'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea956136-efa5-4278-b081-ed1166705fe2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping, found downloaded files in \".\\craigslist-vehicles\" (use force=True to force download)\n"
     ]
    }
   ],
   "source": [
    "od.download(download_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d670ed38-c509-4e6c-a993-5aa2168bbd9f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_filename = '.\\craigslist-vehicles/craigslist_vehicles.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b9777532-49df-47d5-9a49-0a9e6d503851",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1cf4566a-371d-44e5-9b91-a50a48bfa528",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_filename, nrows=10000)  # Read the first 10,000 rows as an example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e005ea3c-7e74-4846-933b-db58ab605eac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 28 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Unnamed: 0    10000 non-null  int64  \n",
      " 1   id            10000 non-null  int64  \n",
      " 2   url           10000 non-null  object \n",
      " 3   region        10000 non-null  object \n",
      " 4   region_url    10000 non-null  object \n",
      " 5   price         10000 non-null  int64  \n",
      " 6   year          9992 non-null   float64\n",
      " 7   manufacturer  9665 non-null   object \n",
      " 8   model         9843 non-null   object \n",
      " 9   condition     6275 non-null   object \n",
      " 10  cylinders     5553 non-null   object \n",
      " 11  fuel          9950 non-null   object \n",
      " 12  odometer      9946 non-null   float64\n",
      " 13  title_status  9760 non-null   object \n",
      " 14  transmission  9917 non-null   object \n",
      " 15  VIN           5952 non-null   object \n",
      " 16  drive         6862 non-null   object \n",
      " 17  size          2756 non-null   object \n",
      " 18  type          7842 non-null   object \n",
      " 19  paint_color   7045 non-null   object \n",
      " 20  image_url     10000 non-null  object \n",
      " 21  description   10000 non-null  object \n",
      " 22  county        0 non-null      float64\n",
      " 23  state         10000 non-null  object \n",
      " 24  lat           9881 non-null   float64\n",
      " 25  long          9881 non-null   float64\n",
      " 26  posting_date  10000 non-null  object \n",
      " 27  removal_date  10000 non-null  object \n",
      "dtypes: float64(5), int64(3), object(20)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "42f648b8-8146-463e-9e87-ba160353e97b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>county</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>9992.000000</td>\n",
       "      <td>9.946000e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9881.000000</td>\n",
       "      <td>9881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>282358.035100</td>\n",
       "      <td>7.310791e+09</td>\n",
       "      <td>3.588872e+04</td>\n",
       "      <td>2011.003203</td>\n",
       "      <td>9.554652e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.870140</td>\n",
       "      <td>-87.447119</td>\n",
       "    </tr>\n",
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       "      <th>std</th>\n",
       "      <td>40435.097849</td>\n",
       "      <td>4.341035e+06</td>\n",
       "      <td>1.259394e+06</td>\n",
       "      <td>9.952283</td>\n",
       "      <td>2.299699e+05</td>\n",
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       "      <td>3.678029</td>\n",
       "      <td>14.038848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>120490.000000</td>\n",
       "      <td>7.301593e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-25.262575</td>\n",
       "      <td>-128.686743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>261341.750000</td>\n",
       "      <td>7.307354e+09</td>\n",
       "      <td>5.995000e+03</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>3.666550e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.110400</td>\n",
       "      <td>-106.522515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>265311.500000</td>\n",
       "      <td>7.311440e+09</td>\n",
       "      <td>1.399000e+04</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.562450e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.680000</td>\n",
       "      <td>-81.494766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>288273.250000</td>\n",
       "      <td>7.314563e+09</td>\n",
       "      <td>2.750000e+04</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.300690e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.144900</td>\n",
       "      <td>-75.171800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>363434.000000</td>\n",
       "      <td>7.317059e+09</td>\n",
       "      <td>1.234568e+08</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.432675</td>\n",
       "      <td>-70.885698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Unnamed: 0            id         price         year      odometer  \\\n",
       "count   10000.000000  1.000000e+04  1.000000e+04  9992.000000  9.946000e+03   \n",
       "mean   282358.035100  7.310791e+09  3.588872e+04  2011.003203  9.554652e+04   \n",
       "std     40435.097849  4.341035e+06  1.259394e+06     9.952283  2.299699e+05   \n",
       "min    120490.000000  7.301593e+09  0.000000e+00  1923.000000  0.000000e+00   \n",
       "25%    261341.750000  7.307354e+09  5.995000e+03  2008.000000  3.666550e+04   \n",
       "50%    265311.500000  7.311440e+09  1.399000e+04  2013.000000  8.562450e+04   \n",
       "75%    288273.250000  7.314563e+09  2.750000e+04  2017.000000  1.300690e+05   \n",
       "max    363434.000000  7.317059e+09  1.234568e+08  2021.000000  1.000000e+07   \n",
       "\n",
       "       county          lat         long  \n",
       "count     0.0  9881.000000  9881.000000  \n",
       "mean      NaN    38.870140   -87.447119  \n",
       "std       NaN     3.678029    14.038848  \n",
       "min       NaN   -25.262575  -128.686743  \n",
       "25%       NaN    35.110400  -106.522515  \n",
       "50%       NaN    40.680000   -81.494766  \n",
       "75%       NaN    41.144900   -75.171800  \n",
       "max       NaN    48.432675   -70.885698  "
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "274d5c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify numerical and categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9eebda6b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "numerical_columns = df.select_dtypes(include=['number'])\n",
    "categorical_columns = df.select_dtypes(exclude=['number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "faf2053d-386d-4811-b513-f6ad4560f25d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[numerical_columns.columns] = df[numerical_columns.columns].fillna(df[numerical_columns.columns].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0dc4d647-5832-40f2-80f7-3178f986659e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 28 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Unnamed: 0    10000 non-null  int64  \n",
      " 1   id            10000 non-null  int64  \n",
      " 2   url           10000 non-null  object \n",
      " 3   region        10000 non-null  object \n",
      " 4   region_url    10000 non-null  object \n",
      " 5   price         10000 non-null  int64  \n",
      " 6   year          10000 non-null  float64\n",
      " 7   manufacturer  9665 non-null   object \n",
      " 8   model         9843 non-null   object \n",
      " 9   condition     6275 non-null   object \n",
      " 10  cylinders     5553 non-null   object \n",
      " 11  fuel          9950 non-null   object \n",
      " 12  odometer      10000 non-null  float64\n",
      " 13  title_status  9760 non-null   object \n",
      " 14  transmission  9917 non-null   object \n",
      " 15  VIN           5952 non-null   object \n",
      " 16  drive         6862 non-null   object \n",
      " 17  size          2756 non-null   object \n",
      " 18  type          7842 non-null   object \n",
      " 19  paint_color   7045 non-null   object \n",
      " 20  image_url     10000 non-null  object \n",
      " 21  description   10000 non-null  object \n",
      " 22  county        0 non-null      float64\n",
      " 23  state         10000 non-null  object \n",
      " 24  lat           10000 non-null  float64\n",
      " 25  long          10000 non-null  float64\n",
      " 26  posting_date  10000 non-null  object \n",
      " 27  removal_date  10000 non-null  object \n",
      "dtypes: float64(5), int64(3), object(20)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d5bed8f-1850-41c5-ac31-e9933dd49f84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>county</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>282358.035100</td>\n",
       "      <td>7.310791e+09</td>\n",
       "      <td>3.588872e+04</td>\n",
       "      <td>2011.004800</td>\n",
       "      <td>9.549294e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.891678</td>\n",
       "      <td>-87.376286</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40435.097849</td>\n",
       "      <td>4.341035e+06</td>\n",
       "      <td>1.259394e+06</td>\n",
       "      <td>9.948461</td>\n",
       "      <td>2.293493e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.661341</td>\n",
       "      <td>13.969978</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>120490.000000</td>\n",
       "      <td>7.301593e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-25.262575</td>\n",
       "      <td>-128.686743</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>261341.750000</td>\n",
       "      <td>7.307354e+09</td>\n",
       "      <td>5.995000e+03</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>3.700825e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.134700</td>\n",
       "      <td>-106.518300</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>265311.500000</td>\n",
       "      <td>7.311440e+09</td>\n",
       "      <td>1.399000e+04</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>8.562450e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.680000</td>\n",
       "      <td>-81.494766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>288273.250000</td>\n",
       "      <td>7.314563e+09</td>\n",
       "      <td>2.750000e+04</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.300000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.125860</td>\n",
       "      <td>-75.181123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>363434.000000</td>\n",
       "      <td>7.317059e+09</td>\n",
       "      <td>1.234568e+08</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.432675</td>\n",
       "      <td>-70.885698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0            id         price          year      odometer  \\\n",
       "count   10000.000000  1.000000e+04  1.000000e+04  10000.000000  1.000000e+04   \n",
       "mean   282358.035100  7.310791e+09  3.588872e+04   2011.004800  9.549294e+04   \n",
       "std     40435.097849  4.341035e+06  1.259394e+06      9.948461  2.293493e+05   \n",
       "min    120490.000000  7.301593e+09  0.000000e+00   1923.000000  0.000000e+00   \n",
       "25%    261341.750000  7.307354e+09  5.995000e+03   2008.000000  3.700825e+04   \n",
       "50%    265311.500000  7.311440e+09  1.399000e+04   2013.000000  8.562450e+04   \n",
       "75%    288273.250000  7.314563e+09  2.750000e+04   2017.000000  1.300000e+05   \n",
       "max    363434.000000  7.317059e+09  1.234568e+08   2021.000000  1.000000e+07   \n",
       "\n",
       "       county           lat          long  \n",
       "count     0.0  10000.000000  10000.000000  \n",
       "mean      NaN     38.891678    -87.376286  \n",
       "std       NaN      3.661341     13.969978  \n",
       "min       NaN    -25.262575   -128.686743  \n",
       "25%       NaN     35.134700   -106.518300  \n",
       "50%       NaN     40.680000    -81.494766  \n",
       "75%       NaN     41.125860    -75.181123  \n",
       "max       NaN     48.432675    -70.885698  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6828388a-bfe9-4f17-9423-945c288aa7f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in categorical columns with the mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "425bc68a-02d2-477f-b241-b7ce85b46851",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[categorical_columns.columns] = df[categorical_columns.columns].fillna(df[categorical_columns.columns].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2e6a4a35-f0db-4791-a787-1268d5721da4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Set 'posting_date' as the Index:\n",
    "# Set the time zone to Kenyan time (UTC+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c6cf0354-3fa9-4e46-82e5-15d9667ecdaf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['posting_date'] = pd.to_datetime(df['posting_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e04b04df-2aa3-42ba-8ba8-e88aa0753474",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "kenya_timezone = pytz.timezone('Africa/Nairobi')\n",
    "df['posting_date'] = df['posting_date'].dt.tz_convert(kenya_timezone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8bfb7c96-6fc0-4af0-958d-520c1c62460e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.set_index('posting_date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c08faa0b-4025-4097-b6a3-c6e70cd7a2b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"clean_craigslist_vehicles.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "faf97be1-4a10-403e-92ea-4d2309f19983",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   posting_date  Unnamed: 0          id  \\\n",
      "0     2021-04-16 03:00:00+03:00      362773  7307679724   \n",
      "1     2021-04-24 03:00:00+03:00      362712  7311833696   \n",
      "2     2021-04-23 03:00:00+03:00      362722  7311441996   \n",
      "3     2021-04-16 03:00:00+03:00      362771  7307680715   \n",
      "4     2021-04-24 03:00:00+03:00      362710  7311834578   \n",
      "...                         ...         ...         ...   \n",
      "9995  2021-04-30 03:00:00+03:00      362951  7314654152   \n",
      "9996  2021-05-02 03:00:00+03:00      362894  7315636694   \n",
      "9997  2021-04-19 03:00:00+03:00      363176  7309561174   \n",
      "9998  2021-04-26 03:00:00+03:00      363031  7313091974   \n",
      "9999  2021-04-24 03:00:00+03:00      363059  7312103278   \n",
      "\n",
      "                                                    url    region  \\\n",
      "0     https://abilene.craigslist.org/ctd/d/abilene-2...   abilene   \n",
      "1     https://abilene.craigslist.org/ctd/d/abilene-2...   abilene   \n",
      "2     https://abilene.craigslist.org/ctd/d/abilene-2...   abilene   \n",
      "3     https://abilene.craigslist.org/ctd/d/abilene-2...   abilene   \n",
      "4     https://abilene.craigslist.org/ctd/d/abilene-2...   abilene   \n",
      "...                                                 ...       ...   \n",
      "9995  https://amarillo.craigslist.org/ctd/d/lubbock-...  amarillo   \n",
      "9996  https://amarillo.craigslist.org/ctd/d/lubbock-...  amarillo   \n",
      "9997  https://amarillo.craigslist.org/ctd/d/lubbock-...  amarillo   \n",
      "9998  https://amarillo.craigslist.org/ctd/d/lubbock-...  amarillo   \n",
      "9999  https://amarillo.craigslist.org/ctd/d/lubbock-...  amarillo   \n",
      "\n",
      "                           region_url  price    year manufacturer  \\\n",
      "0      https://abilene.craigslist.org   4500  2002.0          bmw   \n",
      "1      https://abilene.craigslist.org   4500  2002.0          bmw   \n",
      "2      https://abilene.craigslist.org   4900  2006.0       toyota   \n",
      "3      https://abilene.craigslist.org   6500  2008.0         ford   \n",
      "4      https://abilene.craigslist.org   6500  2008.0         ford   \n",
      "...                               ...    ...     ...          ...   \n",
      "9995  https://amarillo.craigslist.org   3500  2008.0    chevrolet   \n",
      "9996  https://amarillo.craigslist.org   2500  2013.0        dodge   \n",
      "9997  https://amarillo.craigslist.org   3500  2014.0        dodge   \n",
      "9998  https://amarillo.craigslist.org   2500  2009.0        dodge   \n",
      "9999  https://amarillo.craigslist.org   4000  2012.0         ford   \n",
      "\n",
      "                      model  ...       size   type paint_color  \\\n",
      "0                        x5  ...        NaN    NaN         NaN   \n",
      "1                        x5  ...        NaN    NaN         NaN   \n",
      "2                     camry  ...        NaN  sedan      silver   \n",
      "3                expedition  ...        NaN    NaN         NaN   \n",
      "4                expedition  ...        NaN    NaN         NaN   \n",
      "...                     ...  ...        ...    ...         ...   \n",
      "9995               tahoe lt  ...   mid-size    SUV        blue   \n",
      "9996         challenger sxt  ...  full-size  coupe       black   \n",
      "9997                charger  ...  full-size  sedan       black   \n",
      "9998            charger sxt  ...  full-size  sedan         NaN   \n",
      "9999  expedition el limited  ...  full-size    SUV       white   \n",
      "\n",
      "                                              image_url  \\\n",
      "0     https://images.craigslist.org/00m0m_iba78h8ty9...   \n",
      "1     https://images.craigslist.org/00m0m_iba78h8ty9...   \n",
      "2     https://images.craigslist.org/00808_5FkOw2aGjA...   \n",
      "3     https://images.craigslist.org/00M0M_i9CoFvVq8o...   \n",
      "4     https://images.craigslist.org/00M0M_i9CoFvVq8o...   \n",
      "...                                                 ...   \n",
      "9995  https://images.craigslist.org/00h0h_24iH2qHAdt...   \n",
      "9996  https://images.craigslist.org/00v0v_fWHxiGWauh...   \n",
      "9997  https://images.craigslist.org/01414_3HoG03Cwie...   \n",
      "9998  https://images.craigslist.org/00B0B_qiYOf92pI0...   \n",
      "9999  https://images.craigslist.org/00a0a_7T1hDeFNme...   \n",
      "\n",
      "                                            description county state  \\\n",
      "0     $4,500 Cash  2002 BMW X5  8 cylinder 4.4L moto...    NaN    tx   \n",
      "1     $4,500 Cash  2002 BMW X5  8 cylinder 4.4L moto...    NaN    tx   \n",
      "2     2006 TOYOTA CAMRY LE Sedan   Ready To Upgrade ...    NaN    tx   \n",
      "3     $6500.00 2008 Ford Expedition  8 cylinder 5.4L...    NaN    tx   \n",
      "4     $6500.00 2008 Ford Expedition  8 cylinder 5.4L...    NaN    tx   \n",
      "...                                                 ...    ...   ...   \n",
      "9995  www.DEPOTAUTOSALES.com NO CREDIT CHECK! FREE O...    NaN    tx   \n",
      "9996  www.DEPOTAUTOSALES.com NO CREDIT CHECK! FREE O...    NaN    tx   \n",
      "9997  www.DEPOTAUTOSALES.com NO CREDIT CHECK! FREE O...    NaN    tx   \n",
      "9998  www.DEPOTAUTOSALES.com NO CREDIT CHECK! FREE O...    NaN    tx   \n",
      "9999  www.DEPOTAUTOSALES.com NO CREDIT CHECK! FREE O...    NaN    tx   \n",
      "\n",
      "            lat        long               removal_date  \n",
      "0     32.401556  -99.884713  2021-05-02 00:00:00+00:00  \n",
      "1     32.401556  -99.884713  2021-04-28 00:00:00+00:00  \n",
      "2     32.453848  -99.787900  2021-05-25 00:00:00+00:00  \n",
      "3     32.401556  -99.884713  2021-04-26 00:00:00+00:00  \n",
      "4     32.401556  -99.884713  2021-05-12 00:00:00+00:00  \n",
      "...         ...         ...                        ...  \n",
      "9995  33.561775 -101.856766  2021-05-06 00:00:00+00:00  \n",
      "9996  33.562204 -101.856594  2021-05-26 00:00:00+00:00  \n",
      "9997  33.561775 -101.856937  2021-05-07 00:00:00+00:00  \n",
      "9998  33.561775 -101.856766  2021-05-03 00:00:00+00:00  \n",
      "9999  33.561824 -101.856411  2021-05-11 00:00:00+00:00  \n",
      "\n",
      "[10000 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "clean_data = pd.read_csv(\"clean_craigslist_vehicles.csv\")\n",
    "print(clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3df59912-d212-42fd-ab80-4defcdbcd1a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c06dbc2f-b6d3-485f-be3d-28c5fa11b2f7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "299f40a2-7e3c-4a41-9396-c8dc8eff93de",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "522f99cf-5f2b-49de-a702-f0bd0c40035e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "summary_stats = clean_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4c5fc18c-814f-467a-8f18-2a1147a102cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2b6406b2-11d4-4ac5-b46f-2eb69ed31b69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(clean_data['price'], kde=True)\n",
    "plt.title('Price Distribution')\n",
    "plt.xlabel('Price')\n",
    "\n",
    "# Set custom x-axis limits\n",
    "plt.xlim(0, 50000)\n",
    "# Set custom y-axis limits\n",
    "plt.ylim(0, 1000)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4d09fe32-22dd-404c-b550-0fa9c6d6b167",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Temporal Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "af1ea7e8-2d97-4491-a052-65f940d295a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "eb02fd12-42aa-4477-949e-49e1fa113b05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Group by region and calculate the mean price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eb5210b2-1a54-4b6c-8b83-7b922b876dc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Price')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkchkdAA+u9KDlRkfIVMdHtzY6AgAAAACYGmf+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJMztPzfunVLH374ocqUKaP8+fPLz89Pw4YNU1pamm2M1WrV0KFD5ePjo/z58yswMFD79u2z+z7Jycl6++235enpqQIFCigoKEh//vmn3ZiEhASFhobK3d1d7u7uCg0NVWJiot2Y2NhYtW3bVgUKFJCnp6fCwsJ08+bNLHv9AAAAAABkB0PL/yeffKIpU6Zo8uTJiomJ0ZgxYzR27Fh99tlntjFjxozRhAkTNHnyZG3btk3e3t569tlndfnyZduYfv36afHixVqwYIGioqJ05coVtWnTRqmpqbYxISEh2rlzp1asWKEVK1Zo586dCg0Nte1PTU1V69atdfXqVUVFRWnBggVatGiRIiIisueHAQAAAABAFrFYrVarUU/epk0bFStWTNOnT7dte+GFF+Ti4qI5c+bIarXKx8dH/fr108CBAyX9dZa/WLFi+uSTT9SnTx8lJSWpaNGimjNnjl5++WVJ0unTp+Xr66uffvpJLVu2VExMjCpXrqzNmzerbt26kqTNmzerXr16OnDggPz9/fXzzz+rTZs2OnnypHx8fCRJCxYsULdu3XTu3Dm5ubn94+u5dOmS3N3dlZSUlKHxD6v0oOVZ9r2NcHx0a6MjAAAAAMBjKaM91NAz/w0bNtTq1at16NAhSdKuXbsUFRWlVq1aSZKOHTumuLg4tWjRwvaYvHnzqkmTJtq4caMkafv27UpJSbEb4+Pjo4CAANuYTZs2yd3d3Vb8Jenpp5+Wu7u73ZiAgABb8Zekli1bKjk5Wdu3b8+inwAAAAAAAFkvl5FPPnDgQCUlJalixYpydnZWamqqRowYoVdffVWSFBcXJ0kqVqyY3eOKFSumEydO2MbkyZNHHh4e6cbcfnxcXJy8vLzSPb+Xl5fdmDufx8PDQ3ny5LGNuVNycrKSk5NtX1+6dCnDrx0AAAAAgOxi6Jn/b7/9Vt98843mzZun6OhozZo1S+PGjdOsWbPsxlksFruvrVZrum13unPM3cY/zJi/GzVqlG0BQXd3d/n6+t43EwAAAAAARjC0/L/77rsaNGiQXnnlFVWtWlWhoaHq37+/Ro0aJUny9vaWpHRn3s+dO2c7S+/t7a2bN28qISHhvmPOnj2b7vnPnz9vN+bO50lISFBKSkq6GQG3vf/++0pKSrJ9nDx58kF/BAAAAAAAZDlDy/+1a9fk5GQfwdnZ2XarvzJlysjb21urVq2y7b9586YiIyNVv359SVLt2rWVO3duuzFnzpzR3r17bWPq1aunpKQkbd261TZmy5YtSkpKshuzd+9enTlzxjZm5cqVyps3r2rXrn3X/Hnz5pWbm5vdBwAAAAAAOY2h1/y3bdtWI0aMUMmSJVWlShXt2LFDEyZMUI8ePST9NQ2/X79+GjlypMqXL6/y5ctr5MiRcnFxUUhIiCTJ3d1dPXv2VEREhIoUKaLChQtrwIABqlq1qpo3by5JqlSpkp577jn16tVLX331lSSpd+/eatOmjfz9/SVJLVq0UOXKlRUaGqqxY8cqPj5eAwYMUK9evSj1AAAAAIDHmqHl/7PPPtPgwYPVt29fnTt3Tj4+PurTp4+GDBliG/Pee+/p+vXr6tu3rxISElS3bl2tXLlSrq6utjETJ05Urly51LFjR12/fl3PPPOMZs6cKWdnZ9uYuXPnKiwszHZXgKCgIE2ePNm239nZWcuXL1ffvn3VoEED5c+fXyEhIRo3blw2/CQAAAAAAMg6FqvVajU6hFlk9P6Kj6r0oOVZ9r2NcHx0a6MjAAAAAMBjKaM91NBr/gEAAAAAQNaj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOco/AAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AAAAAAJOj/AMAAAAAYHKUfwAAAAAATI7yDwAAAACAyVH+AQAAAAAwOcPL/6lTp9S5c2cVKVJELi4uqlGjhrZv327bb7VaNXToUPn4+Ch//vwKDAzUvn377L5HcnKy3n77bXl6eqpAgQIKCgrSn3/+aTcmISFBoaGhcnd3l7u7u0JDQ5WYmGg3JjY2Vm3btlWBAgXk6empsLAw3bx5M8teOwAAAAAA2cHQ8p+QkKAGDRood+7c+vnnn7V//36NHz9ehQoVso0ZM2aMJkyYoMmTJ2vbtm3y9vbWs88+q8uXL9vG9OvXT4sXL9aCBQsUFRWlK1euqE2bNkpNTbWNCQkJ0c6dO7VixQqtWLFCO3fuVGhoqG1/amqqWrduratXryoqKkoLFizQokWLFBERkS0/CwAAAAAAsorFarVajXryQYMGacOGDVq/fv1d91utVvn4+Khfv34aOHCgpL/O8hcrVkyffPKJ+vTpo6SkJBUtWlRz5szRyy+/LEk6ffq0fH199dNPP6lly5aKiYlR5cqVtXnzZtWtW1eStHnzZtWrV08HDhyQv7+/fv75Z7Vp00YnT56Uj4+PJGnBggXq1q2bzp07Jzc3t398PZcuXZK7u7uSkpIyNP5hlR60PMu+txGOj25tdAQAAAAAeCxltIcaeuZ/6dKlqlOnjl566SV5eXmpZs2amjZtmm3/sWPHFBcXpxYtWti25c2bV02aNNHGjRslSdu3b1dKSordGB8fHwUEBNjGbNq0Se7u7rbiL0lPP/203N3d7cYEBATYir8ktWzZUsnJyXaXIfxdcnKyLl26ZPcBAAAAAEBOY2j5P3r0qL788kuVL19ev/zyi15//XWFhYVp9uzZkqS4uDhJUrFixeweV6xYMdu+uLg45cmTRx4eHvcd4+Xlle75vby87Mbc+TweHh7KkyePbcydRo0aZVtDwN3dXb6+vg/6IwAAAAAAIMsZWv7T0tJUq1YtjRw5UjVr1lSfPn3Uq1cvffnll3bjLBaL3ddWqzXdtjvdOeZu4x9mzN+9//77SkpKsn2cPHnyvpkAAAAAADCCoeW/ePHiqly5st22SpUqKTY2VpLk7e0tSenOvJ87d852lt7b21s3b95UQkLCfcecPXs23fOfP3/ebsydz5OQkKCUlJR0MwJuy5s3r9zc3Ow+AAAAAADIaQwt/w0aNNDBgwftth06dEilSpWSJJUpU0be3t5atWqVbf/NmzcVGRmp+vXrS5Jq166t3Llz2405c+aM9u7daxtTr149JSUlaevWrbYxW7ZsUVJSkt2YvXv36syZM7YxK1euVN68eVW7du1MfuUAAAAAAGSfXEY+ef/+/VW/fn2NHDlSHTt21NatWzV16lRNnTpV0l/T8Pv166eRI0eqfPnyKl++vEaOHCkXFxeFhIRIktzd3dWzZ09FRESoSJEiKly4sAYMGKCqVauqefPmkv6aTfDcc8+pV69e+uqrryRJvXv3Vps2beTv7y9JatGihSpXrqzQ0FCNHTtW8fHxGjBggHr16sUZfQAAAADAY83Q8v/kk09q8eLFev/99zVs2DCVKVNGkyZNUqdOnWxj3nvvPV2/fl19+/ZVQkKC6tatq5UrV8rV1dU2ZuLEicqVK5c6duyo69ev65lnntHMmTPl7OxsGzN37lyFhYXZ7goQFBSkyZMn2/Y7Oztr+fLl6tu3rxo0aKD8+fMrJCRE48aNy4afBAAAAAAAWcditVqtRocwi4zeX/FRlR60PMu+txGOj25tdAQAAAAAeCxltIcaes0/AAAAAADIepR/AAAAAABMjvIPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJPVT5T0xM1H//+1+9//77io+PlyRFR0fr1KlTmRoOAAAAAAA8ulwP+oDdu3erefPmcnd31/Hjx9WrVy8VLlxYixcv1okTJzR79uysyAkAAAAAAB7SA5/5f+edd9StWzcdPnxY+fLls21//vnn9dtvv2VqOAAAAAAA8OgeuPxv27ZNffr0Sbe9RIkSiouLy5RQAAAAAAAg8zxw+c+XL58uXbqUbvvBgwdVtGjRTAkFAAAAAAAyzwOX/+DgYA0bNkwpKSmSJIvFotjYWA0aNEgvvPBCpgcEAAAAAACP5oHL/7hx43T+/Hl5eXnp+vXratKkicqVKydXV1eNGDEiKzICAAAAAIBH8MCr/bu5uSkqKkpr1qxRdHS00tLSVKtWLTVv3jwr8gEAAAAAgEf0wOX/tmbNmqlZs2aZmQUAAAAAAGSBB572HxYWpk8//TTd9smTJ6tfv36ZkQkAAAAAAGSiBy7/ixYtUoMGDdJtr1+/vr7//vtMCQUAAAAAADLPA5f/ixcvyt3dPd12Nzc3XbhwIVNCAQAAAACAzPPA5b9cuXJasWJFuu0///yz/Pz8MiUUAAAAAADIPA+84N8777yjt956S+fPn7ct+Ld69WqNHz9ekyZNyux8AAAAAADgET1w+e/Ro4eSk5M1YsQI/fvf/5YklS5dWl9++aW6dOmS6QEBAAAAAMCjeahb/b3xxht64403dP78eeXPn18FCxbM7FwAAAAAACCTPFT5v61o0aKZlQMAAAAAAGSRDJX/WrVqafXq1fLw8FDNmjVlsVjuOTY6OjrTwgEAAAAAgEeXofIfHBysvHnzSpLatWuXlXkAAAAAAEAmy1D5/+ijjyRJqampCgwMVLVq1eTh4ZGlwQAAAAAAQOZwepDBzs7OatmypRITE7MoDgAAAAAAyGwPVP4lqWrVqjp69GhWZAEAAAAAAFnggcv/iBEjNGDAAC1btkxnzpzRpUuX7D4AAAAAAEDO8sC3+nvuueckSUFBQXar/lutVlksFqWmpmZeOgAAAAAA8MgeuPyvXbs2K3IAAAAAAIAs8kDl32q1ysfHRykpKapQoYJy5Xrg9w4AAAAAAEA2y/A1/8ePH1eNGjVUsWJFVa1aVeXKlVN0dHRWZgMAAAAAAJkgw+V/4MCBunHjhubMmaPvvvtOxYsX1+uvv56V2QAAAAAAQCbI8Lz99evXa/78+WrSpIkk6amnnlKpUqV0/fp15c+fP8sCAgAAAACAR5PhM/9xcXGqWLGi7esnnnhC+fPn19mzZ7MkGAAAAAAAyBwZLv8Wi0VOTvbDnZycZLVaMz0UAAAAAADIPBme9m+1WlWhQgVZLBbbtitXrqhmzZp2bwrEx8dnbkIAAAAAAPBIMlz+Z8yYkZU5AAAAAABAFslw+e/atWtW5gAAAAAAAFkkw9f8AwAAAACAxxPlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJZXi1/9tSU1M1c+ZMrV69WufOnVNaWprd/jVr1mRaOAAAAAAA8OgeuPyHh4dr5syZat26tQICAmSxWLIiFwAAAAAAyCQPXP4XLFighQsXqlWrVlmRBwAAAAAAZLIHvuY/T548KleuXFZkAQAAAAAAWeCBy39ERIT+85//yGq1ZkUeAAAAAACQyR542n9UVJTWrl2rn3/+WVWqVFHu3Lnt9v/www+ZFg4AAAAAADy6By7/hQoVUvv27bMiCwAAAAAAyAIPXP5nzJiRFTkAAAAAAEAWeeBr/gEAAAAAwOPlgc/8S9L333+vhQsXKjY2Vjdv3rTbFx0dnSnBAAAAAABA5njgM/+ffvqpunfvLi8vL+3YsUNPPfWUihQpoqNHj+r555/PiowAAAAAAOARPHD5/+KLLzR16lRNnjxZefLk0XvvvadVq1YpLCxMSUlJWZERAAAAAAA8ggcu/7Gxsapfv74kKX/+/Lp8+bIkKTQ0VPPnz8/cdAAAAAAA4JE9cPn39vbWxYsXJUmlSpXS5s2bJUnHjh2T1WrN3HQAAAAAAOCRPXD5b9asmX788UdJUs+ePdW/f389++yzevnll9W+fftMDwgAAAAAAB7NA6/2P3XqVKWlpUmSXn/9dRUuXFhRUVFq27atXn/99UwPCAAAAAAAHs0Dl38nJyc5Of3/CQMdO3ZUx44dMzUUAAAAAADIPA887V+S1q9fr86dO6tevXo6deqUJGnOnDmKiorK1HAAAAAAAODRPXD5X7RokVq2bKn8+fNrx44dSk5OliRdvnxZI0eOzPSAAAAAAADg0Txw+R8+fLimTJmiadOmKXfu3Lbt9evXV3R0dKaGAwAAAAAAj+6By//BgwfVuHHjdNvd3NyUmJiYGZkAAAAAAEAmeuDyX7x4cf3xxx/ptkdFRcnPzy9TQgEAAAAAgMzzwOW/T58+Cg8P15YtW2SxWHT69GnNnTtXAwYMUN++fbMiIwAAAAAAeAQPfKu/9957T0lJSWratKlu3Lihxo0bK2/evBowYIDeeuutrMgIAAAAAAAewQOXf0kaMWKEPvjgA+3fv19paWmqXLmyChYsmNnZAAAAAABAJnio8i9JLi4uqlOnTmZmAQAAAAAAWSDD5b9Hjx4ZGvf1118/dBgAAAAAAJD5Mlz+Z86cqVKlSqlmzZqyWq1ZmQkAAAAAAGSiDJf/119/XQsWLNDRo0fVo0cPde7cWYULF87KbMBjqfSg5UZHyDTHR7c2OgIAAACATJDhW/198cUXOnPmjAYOHKgff/xRvr6+6tixo3755RdmAgAAAAAAkINluPxLUt68efXqq69q1apV2r9/v6pUqaK+ffuqVKlSunLlSlZlBAAAAAAAj+CByv/fWSwWWSwWWa1WpaWlZWYmAAAAAACQiR6o/CcnJ2v+/Pl69tln5e/vrz179mjy5MmKjY1VwYIFsyojAAAAAAB4BBle8K9v375asGCBSpYsqe7du2vBggUqUqRIVmYDAAAAAACZIMNn/qdMmSI3NzeVKVNGkZGR6tWrlzp06JDu42GNGjVKFotF/fr1s22zWq0aOnSofHx8lD9/fgUGBmrfvn12j0tOTtbbb78tT09PFShQQEFBQfrzzz/txiQkJCg0NFTu7u5yd3dXaGioEhMT7cbExsaqbdu2KlCggDw9PRUWFqabN28+9OsBAAAAACCnyHD579Kli5o2bapChQrZSvTdPh7Gtm3bNHXqVFWrVs1u+5gxYzRhwgRNnjxZ27Ztk7e3t5599lldvnzZNqZfv35avHixFixYoKioKF25ckVt2rRRamqqbUxISIh27typFStWaMWKFdq5c6dCQ0Nt+1NTU9W6dWtdvXpVUVFRWrBggRYtWqSIiIiHej0AAAAAAOQkGZ72P3PmzCwJcOXKFXXq1EnTpk3T8OHDbdutVqsmTZqkDz74wDajYNasWSpWrJjmzZunPn36KCkpSdOnT9ecOXPUvHlzSdI333wjX19f/frrr2rZsqViYmK0YsUKbd68WXXr1pUkTZs2TfXq1dPBgwfl7++vlStXav/+/Tp58qR8fHwkSePHj1e3bt00YsQIubm5ZclrBwAAAAAgOzz0av+Z5c0331Tr1q1t5f22Y8eOKS4uTi1atLBty5s3r5o0aaKNGzdKkrZv366UlBS7MT4+PgoICLCN2bRpk9zd3W3FX5Kefvppubu7240JCAiwFX9JatmypZKTk7V9+/bMf9EAAAAAAGSjDJ/5zwoLFixQdHS0tm3blm5fXFycJKlYsWJ224sVK6YTJ07YxuTJk0ceHh7pxtx+fFxcnLy8vNJ9fy8vL7sxdz6Ph4eH8uTJYxtzN8nJyUpOTrZ9fenSpXuOBQAAAADAKIad+T958qTCw8P1zTffKF++fPccZ7FY7L62Wq3ptt3pzjF3G/8wY+40atQou/UOfH1975sLAAAAAAAjGFb+t2/frnPnzql27drKlSuXcuXKpcjISH366afKlSuX7Uz8nWfez507Z9vn7e2tmzdvKiEh4b5jzp49m+75z58/bzfmzudJSEhQSkpKuhkBf/f+++8rKSnJ9nHy5MkH/CkAAAAAAJD1DCv/zzzzjPbs2aOdO3faPurUqaNOnTpp586d8vPzk7e3t1atWmV7zM2bNxUZGan69etLkmrXrq3cuXPbjTlz5oz27t1rG1OvXj0lJSVp69attjFbtmxRUlKS3Zi9e/fqzJkztjErV65U3rx5Vbt27Xu+hrx588rNzc3uAwAAAACAnMawa/5dXV0VEBBgt61AgQIqUqSIbXu/fv00cuRIlS9fXuXLl9fIkSPl4uKikJAQSZK7u7t69uypiIgIFSlSRIULF9aAAQNUtWpV2wKClSpV0nPPPadevXrpq6++kiT17t1bbdq0kb+/vySpRYsWqly5skJDQzV27FjFx8drwIAB6tWrF4UeAAAAAPDYM3TBv3/y3nvv6fr16+rbt68SEhJUt25drVy5Uq6urrYxEydOVK5cudSxY0ddv35dzzzzjGbOnClnZ2fbmLlz5yosLMx2V4CgoCBNnjzZtt/Z2VnLly9X37591aBBA+XPn18hISEaN25c9r1YAAAAAACyiMVqtVqNDmEWly5dkru7u5KSkrJ0xkDpQcuz7Hsb4fjo1kZHyFRmOj5mOzYAAACA2WS0hxp2zT8AAAAAAMgelH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJpfL6AAAkF1KD1pudIRMdXx0a6MjAAAA4DHBmX8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5HIZHQAAgNKDlhsdIVMdH93a6AgAAAB2OPMPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMztDyP2rUKD355JNydXWVl5eX2rVrp4MHD9qNsVqtGjp0qHx8fJQ/f34FBgZq3759dmOSk5P19ttvy9PTUwUKFFBQUJD+/PNPuzEJCQkKDQ2Vu7u73N3dFRoaqsTERLsxsbGxatu2rQoUKCBPT0+FhYXp5s2bWfLaAQAAAADILoaW/8jISL355pvavHmzVq1apVu3bqlFixa6evWqbcyYMWM0YcIETZ48Wdu2bZO3t7eeffZZXb582TamX79+Wrx4sRYsWKCoqChduXJFbdq0UWpqqm1MSEiIdu7cqRUrVmjFihXauXOnQkNDbftTU1PVunVrXb16VVFRUVqwYIEWLVqkiIiI7PlhAAAAAACQRXIZ+eQrVqyw+3rGjBny8vLS9u3b1bhxY1mtVk2aNEkffPCBOnToIEmaNWuWihUrpnnz5qlPnz5KSkrS9OnTNWfOHDVv3lyS9M0338jX11e//vqrWrZsqZiYGK1YsUKbN29W3bp1JUnTpk1TvXr1dPDgQfn7+2vlypXav3+/Tp48KR8fH0nS+PHj1a1bN40YMUJubm7Z+JMBAAAAACDzGFr+75SUlCRJKly4sCTp2LFjiouLU4sWLWxj8ubNqyZNmmjjxo3q06ePtm/frpSUFLsxPj4+CggI0MaNG9WyZUtt2rRJ7u7utuIvSU8//bTc3d21ceNG+fv7a9OmTQoICLAVf0lq2bKlkpOTtX37djVt2jSrXz4AADlS6UHLjY6QaY6Pbm10hExlpmMjme/4AEBOkmPKv9Vq1TvvvKOGDRsqICBAkhQXFydJKlasmN3YYsWK6cSJE7YxefLkkYeHR7oxtx8fFxcnLy+vdM/p5eVlN+bO5/Hw8FCePHlsY+6UnJys5ORk29eXLl3K8OsFAAAAACC75JjV/t966y3t3r1b8+fPT7fPYrHYfW21WtNtu9OdY+42/mHG/N2oUaNsCwi6u7vL19f3vpkAAAAAADBCjij/b7/9tpYuXaq1a9fqiSeesG339vaWpHRn3s+dO2c7S+/t7a2bN28qISHhvmPOnj2b7nnPnz9vN+bO50lISFBKSkq6GQG3vf/++0pKSrJ9nDx58kFeNgAAAAAA2cLQ8m+1WvXWW2/phx9+0Jo1a1SmTBm7/WXKlJG3t7dWrVpl23bz5k1FRkaqfv36kqTatWsrd+7cdmPOnDmjvXv32sbUq1dPSUlJ2rp1q23Mli1blJSUZDdm7969OnPmjG3MypUrlTdvXtWuXfuu+fPmzSs3Nze7DwAAAAAAchpDr/l/8803NW/ePC1ZskSurq62M+/u7u7Knz+/LBaL+vXrp5EjR6p8+fIqX768Ro4cKRcXF4WEhNjG9uzZUxERESpSpIgKFy6sAQMGqGrVqrbV/ytVqqTnnntOvXr10ldffSVJ6t27t9q0aSN/f39JUosWLVS5cmWFhoZq7Nixio+P14ABA9SrVy9KPQAAAADgsWZo+f/yyy8lSYGBgXbbZ8yYoW7dukmS3nvvPV2/fl19+/ZVQkKC6tatq5UrV8rV1dU2fuLEicqVK5c6duyo69ev65lnntHMmTPl7OxsGzN37lyFhYXZ7goQFBSkyZMn2/Y7Oztr+fLl6tu3rxo0aKD8+fMrJCRE48aNy6JXDwAAAABA9jC0/Fut1n8cY7FYNHToUA0dOvSeY/Lly6fPPvtMn3322T3HFC5cWN988819n6tkyZJatmzZP2YCAAAAAOBxkiMW/AMAAAAAAFmH8g8AAAAAgMkZOu0fAAAAMKPSg5YbHSFTHR/d2ugIAB4RZ/4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk8tldAAAAAAAyE6lBy03OkKmOT66tdER8JjgzD8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk6P8AwAAAABgcpR/AAAAAABMjvIPAAAAAIDJUf4BAAAAADA5yj8AAAAAACZH+QcAAAAAwOQo/wAAAAAAmBzlHwAAAAAAk8tldAAAAAAAACSp9KDlRkfIVMdHtzY6gg1n/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8g8AAAAAgMlR/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAAAAAACTo/wDAAAAAGBylH8AAAAAAEyO8n+HL774QmXKlFG+fPlUu3ZtrV+/3uhIAAAAAAA8Esr/33z77bfq16+fPvjgA+3YsUONGjXS888/r9jYWKOjAQAAAADw0Cj/fzNhwgT17NlTr732mipVqqRJkybJ19dXX375pdHRAAAAAAB4aLmMDpBT3Lx5U9u3b9egQYPstrdo0UIbN26862OSk5OVnJxs+zopKUmSdOnSpawLKikt+VqWfv/sltU/r+xmpuPDscnZzHR8ODY5m5mOD8cmZzPT8eHY5GxmOj4cm5wtO47P7eewWq33HWex/tMIB3H69GmVKFFCGzZsUP369W3bR44cqVmzZungwYPpHjN06FB9/PHH2RkTAAAAAIB0Tp48qSeeeOKe+znzfweLxWL3tdVqTbfttvfff1/vvPOO7eu0tDTFx8erSJEi93zM4+LSpUvy9fXVyZMn5ebmZnQc3IHjk3NxbHIujk3OxvHJuTg2ORfHJmfj+ORcZjs2VqtVly9flo+Pz33HUf7/j6enp5ydnRUXF2e3/dy5cypWrNhdH5M3b17lzZvXbluhQoWyKqIh3NzcTPF/CLPi+ORcHJuci2OTs3F8ci6OTc7FscnZOD45l5mOjbu7+z+OYcG//5MnTx7Vrl1bq1atstu+atUqu8sAAAAAAAB43HDm/2/eeecdhYaGqk6dOqpXr56mTp2q2NhYvf7660ZHAwAAAADgoVH+/+bll1/WxYsXNWzYMJ05c0YBAQH66aefVKpUKaOjZbu8efPqo48+SndZA3IGjk/OxbHJuTg2ORvHJ+fi2ORcHJucjeOTcznqsWG1fwAAAAAATI5r/gEAAAAAMDnKPwAAAAAAJkf5BwAAAADA5Cj/AAAAAACYHOUfAGAqaWlpunXrlt22s2fP6uOPP9Z7772nqKgog5IBAMwsMDBQs2fP1vXr142OgjtER0drz549tq+XLFmidu3a6V//+pdu3rxpYLLsxWr/sHPo0CGtW7dO586dU1pamt2+IUOGGJQKf/fHH3/oyJEjaty4sfLnzy+r1SqLxWJ0LCDH6N69u3Lnzq2pU6dKki5fvqwqVaroxo0bKl68uPbv368lS5aoVatWBifFzJkz1bFjR7m4uBgdBXjs3Lx5U8eOHVPZsmWVKxd3784JIiIiNHfuXF2/fl0dO3ZUz5499fTTTxsdC5KefPJJDRo0SC+88IKOHj2qKlWqqH379tq2bZtat26tSZMmGR0xW1D+YTNt2jS98cYb8vT0lLe3t12htFgsio6ONjAdLl68qJdffllr1qyRxWLR4cOH5efnp549e6pQoUIaP3680REdWmJiorZu3XrXN866dOliUCrHVKFCBU2ePFktWrSQJH3++ecaMWKEYmJi5O7uroEDB2rr1q1au3atwUlRvHhxXb16VS+99JJ69uyp+vXrGx0Jf3PkyBFNmjRJMTExslgsqlSpksLDw1W2bFmjozm0a9eu6e2339asWbMk/XXixs/PT2FhYfLx8dGgQYMMTujYUlNTtWzZMs2YMUM//fSTypUrpx49eig0NFTFihUzOp7Dcnd3V3R0tMqWLatPPvlEa9as0S+//KINGzbolVde0cmTJ42OmC2Y9g+b4cOHa8SIEYqLi9POnTu1Y8cO2wfF33j9+/dXrly5FBsba3eW7OWXX9aKFSsMTIYff/xRJUuW1PPPP6+33npL4eHhto9+/foZHc/hnDp1SuXLl7d9vXr1ar3wwgtyd3eXJHXt2lX79u0zKh7+5s8//9Q333yjhIQENW3aVBUrVtQnn3yiuLg4o6M5vF9++UWVK1fW1q1bVa1aNQUEBGjLli2qUqWKVq1aZXQ8h/b+++9r165dWrdunfLly2fb3rx5c3377bcGJoMkOTs7Kzg4WP/73/906tQphYSEaPDgwfL19VW7du20Zs0aoyM6JKvVajs58+uvv9pm//n6+urChQtGRstWlH/YJCQk6KWXXjI6Bu5h5cqV+uSTT/TEE0/YbS9fvrxOnDhhUCpIf03z69Gjhy5fvqzExEQlJCTYPuLj442O53Dy5ctnd73l5s2b7aZd5suXT1euXDEiGu7g7OysoKAg/fDDDzp58qR69+6tuXPnqmTJkgoKCtKSJUvSzaRB9hg0aJD69++vLVu2aMKECZo4caK2bNmifv36aeDAgUbHc2j/+9//NHnyZDVs2NBulmblypV15MgRA5Ph77Zu3aohQ4Zo3Lhx8vLy0vvvvy8vLy+1bdtWAwYMMDqew6lTp46GDx+uOXPmKDIyUq1bt5YkHTt2zKFmZFD+YfPSSy9p5cqVRsfAPVy9evWu18VeuHBBefPmNSARbjt16pTCwsK4bjmHqF69uubMmSNJWr9+vc6ePatmzZrZ9h85ckQ+Pj5GxcM9eHl5qUGDBqpXr56cnJy0Z88edevWTWXLltW6deuMjudwYmJi1LNnz3Tbe/Toof379xuQCLedP39eXl5e6bZfvXqVNYAMdu7cOY0fP14BAQFq1KiRzp8/rwULFuj48eP6+OOPNXXqVC1ZskRTpkwxOqrDmTRpkqKjo/XWW2/pgw8+ULly5SRJ33//vUNdcsbqILApV66cBg8erM2bN6tq1arKnTu33f6wsDCDkkGSGjdurNmzZ+vf//63pL/WYUhLS9PYsWPVtGlTg9M5tpYtW+r333+Xn5+f0VEgafDgwWrVqpUWLlyoM2fOqFu3bipevLht/+LFi9WgQQMDE+Lvzp49qzlz5mjGjBk6evSo2rVrp2XLlql58+a6fv26PvzwQ3Xt2pUZTtmsaNGi2rlzp90lNJK0c+fOuxZPZJ8nn3xSy5cv19tvvy1JtsI/bdo01atXz8hoDu+JJ55Q2bJl1aNHD3Xr1k1FixZNN+app57Sk08+aUA6x1atWjW71f5vGzt2rJydnQ1IZAwW/INNmTJl7rnPYrHo6NGj2ZgGd9q/f78CAwNVu3ZtrVmzRkFBQdq3b5/i4+O1YcMGFmAy0PTp0zVs2DB17979rm+cBQUFGZTMce3fv1+rVq2St7e3XnrpJTk5/f+JblOnTlXdunVVvXp1AxNCktq2batffvlFFSpU0GuvvaYuXbqocOHCdmNOnz6tJ554gun/2WzYsGGaOHGiBg0apPr168tisSgqKkqffPKJIiIi9OGHHxod0WFt3LhRzz33nDp16qSZM2eqT58+2rdvnzZt2qTIyEjVrl3b6IgOa/369WrUqJHRMXAPiYmJ+v7773XkyBG9++67Kly4sKKjo1WsWDGVKFHC6HjZgvIPPEbi4uL05Zdfavv27UpLS1OtWrX05ptv2p3VRPb7e7G8k8ViUWpqajamwalTp/7xH/G5c+eqU6dO2ZQI99KzZ0+99tpr9z1babVaFRsbq1KlSmVjMlitVk2aNEnjx4/X6dOnJUk+Pj569913FRYWxvRyg+3Zs0fjxo2z+3tg4MCBqlq1qtHRHN6tW7e0bt06HTlyRCEhIXJ1ddXp06fl5uamggULGh3PYe3evVvPPPOMChUqpOPHj+vgwYPy8/PT4MGDdeLECc2ePdvoiNmC8o+7uv2fBf+4A3jcVK5cWRs2bJCHh8dd98+bN0/dunXTzZs3szkZ8Hi6fPmyJMnV1dXgJEDOduLECT333HOKjY1VcnKy7TaM/fr1040bN7jW30DNmzdXrVq1NGbMGLm6umrXrl3y8/PTxo0bFRISouPHjxsdMVtwzT/szJ49W2PHjtXhw4cl/XW/7HfffVehoaEGJ4PEveRzqmvXrrHYXw7i5eWl5557TmvWrFGBAgXs9i1YsEBdu3bVmDFjDEqHO61evVqrV6++6++1r7/+2qBU+DtKf84SGxt73/0lS5bMpiS4U3h4uOrUqaNdu3apSJEitu3t27fXa6+9ZmAybNu2TV999VW67SVKlHCo28tS/mEzYcIEDR48WG+99ZYaNGggq9WqDRs26PXXX9eFCxfUv39/oyM6tB9//FGdOnXS1atX5erqajcrw2KxUP4NVKhQIdWpU0eBgYFq0qSJGjZsmK50IvssW7ZMgYGBCg4O1s8//2xbg2HhwoXq0qWLRo0axe+zHOLjjz/WsGHDVKdOHRUvXpzZZgarWbNmho9BdHR0FqfBvZQuXfq+x4lLzYwTFRWlDRs2KE+ePHbbS5UqpVOnThmUCtJft/m9dOlSuu0HDx6868KMZkX5h81nn32mL7/80q5EBgcHq0qVKho6dCh/LBvs9r3kR44cyVnmHCYyMlKRkZFat26dJk+erBs3bqhWrVq2NwOef/55oyM6lIIFC+rnn39W48aN9corr+j777/X999/r86dO+vf//4391fOQaZMmaKZM2cyuyyHaNeundERkAE7duyw+zolJUU7duzQhAkTNGLECINSQZLS0tLu+ubLn3/+yQwagwUHB2vYsGFauHChpL9OnMXGxmrQoEF64YUXDE6XfbjmHzb58uXT3r17bfe9vO3w4cOqWrWqbty4YVAySFKBAgW0Z88ebieXw6Wmpmrbtm2aMmWK5s6de88/BJD1Tp48qYYNG6pcuXKKiorSkCFD9MEHHxgdC39TpEgRbd26lbuVAJlg+fLlGjt2rNatW2d0FIf18ssvy93dXVOnTpWrq6t2796tokWLKjg4WCVLltSMGTOMjuiwLl26pFatWmnfvn26fPmyfHx8FBcXp3r16umnn35ymBmblH/YBAQEKCQkRP/617/stg8fPlzffvvtXe+NiezToUMHvfLKK+rYsaPRUXAXBw4c0Lp162wzAFJSUtS4cWM1adJE4eHhRsdzKLt377Z9fuDAAXXp0kXt2rVL97utWrVq2R0Ndxg4cKAKFiyowYMHGx0FeOwdPnxYNWrU0NWrV42O4rBOnz6tpk2bytnZWYcPH1adOnV0+PBheXp66rfffpOXl5fRER3emjVrFB0dbbtLRvPmzY2OlK0o/7BZtGiRXn75ZTVv3lwNGjSw3dN39erVWrhwodq3b290RIfGveRzLm9vb6WkpKhZs2YKDAxU48aNud2SgZycnGSxWGS1Wm3/Kynd58zIMF54eLhmz56tatWqqVq1aul+r02YMMGgZI7Jw8Mjw9f8x8fHZ3Ea3Mud1y1brVadOXNGQ4cO1YEDB7Rz505jgkGSdP36dc2fP9+uYHbq1En58+c3OhpA+Ye97du3a+LEiYqJiZHValXlypUVERGhmjVrGh3N4XEv+ZyrRo0aiomJUY0aNRQYGKjAwEA1atSI+/ka5MSJExkax33jjde0adN77rNYLFqzZk02psGsWbMyPLZr165ZmAT3c/sNzr+zWq3y9fXVggULVK9ePYOSATnLp59+muGxYWFhWZgk56D8A0AmSExM1G+//WZb/G/fvn2qVq2amjZtqtGjRxsdDwBgEpGRkXZfOzk5qWjRoipXrpxy5WIt7+y2dOnSDI9llmb2KlOmTIbGWSwWHT16NIvT5AyUf9g4OzvrzJkz6a5Hunjxory8vDiznIPcuHFD+fLlMzoG7iI+Pl7r1q3TkiVLNG/ePBb8A/DYuHTpktzc3Gyf38/tcYCju9/MzL9jliZyAt4ehM293gdKTk5Od79SZL/U1FSNHDlSU6ZM0dmzZ3Xo0CH5+flp8ODBKl26tHr27Gl0RIe1ePFirVu3TuvWrdO+fftUpEgRNWrUSBMnTrzvtGYA0rZt2/Tdd98pNjZWN2/etNv3ww8/GJTKMXl4eNhOAhQqVOiu1//fXkuDEmMczjTnLGlpaUZHADKM8g/b9TAWi0X//e9/7a5TTk1N1W+//aaKFSsaFQ//Z8SIEZo1a5bGjBmjXr162bZXrVpVEydOpPwbqE+fPmrcuLF69eqlwMBABQQEGB0JeCwsWLBAXbp0UYsWLbRq1Sq1aNFChw8fVlxcHIvMGmDNmjUqXLiwJGnt2rUGp8G9tGvXzm4B09vu3MabNIC9P//8U0uXLr3rm82OssAs0/5hux7mxIkTeuKJJ+Ts7GzblydPHpUuXVrDhg1T3bp1jYoISeXKldNXX32lZ555Rq6urtq1a5f8/Px04MAB1atXTwkJCUZHBIAHUq1aNfXp00dvvvmm7fdamTJl1KdPHxUvXlwff/yx0REd0q1btzRixAj16NFDvr6+RsfBHX799VcNHDhQI0eOVL169WSxWLRx40Z9+OGHGjlypJ599lmjIzoUFpV7PKxevVpBQUEqU6aMDh48qICAAB0/flxWq1W1atVymAVmKf+wadq0qX744Qd5eHgYHQV3kT9/fh04cEClSpWyK//79+/XU089pStXrhgd0aGlpqbqf//7n2JiYmSxWFSpUiUFBwfbvZmG7NWsWTP98MMPKlSokN32S5cuqV27dg7zD31OVqBAAe3bt0+lS5eWp6en1q5dq6pVqyomJkbNmjXTmTNnjI7osFxdXbVnzx6VLl3a6Ci4Q0BAgKZMmaKGDRvabV+/fr169+6tmJgYg5I5JhaVezw89dRTeu655zRs2DDb39FeXl7q1KmTnnvuOb3xxhtGR8wWTPuHDVP8crYqVapo/fr16W5P9t1333ErRoP98ccfatWqlU6dOiV/f39ZrVYdOnRIvr6+Wr58ucqWLWt0RIe0bt26dNP6pL8WzFy/fr0BiXCnwoUL6/Lly5KkEiVKaO/evapataoSExN17do1g9M5tmeeeUbr1q1Tt27djI6COxw5ckTu7u7ptru7u+v48ePZH8jBHTt2zOgIyICYmBjNnz9fkpQrVy5dv35dBQsW1LBhwxQcHEz5h+NJTU3VzJkztXr1ap07dy7dAiacJTPWRx99pNDQUJ06dUppaWn64YcfdPDgQc2ePVvLli0zOp5DCwsLU9myZbV582bb9bIXL15U586dFRYWpuXLlxuc0LHs3r3b9vn+/fsVFxdn+zo1NVUrVqxQiRIljIiGOzRq1EirVq1S1apV1bFjR4WHh2vNmjVatWqVnnnmGaPjObTnn39e77//vvbu3avatWurQIECdvtZSM44Tz75pPr166dvvvlGxYsXlyTFxcUpIiJCTz31lMHpgJypQIECSk5OliT5+PjoyJEjqlKliiTpwoULRkbLVkz7h81bb72lmTNnqnXr1ipevHi6VX4nTpxoUDLc9ssvv2jkyJHavn270tLSVKtWLQ0ZMkQtWrQwOppDK1CggDZv3qyqVavabd+1a5caNGjAJRnZzMnJyfb7627/xOXPn1+fffaZevTokd3RcIf4+HjduHFDPj4+SktL07hx4xQVFaVy5cpp8ODBXIZmoPvdvoyF5Ix1+PBhdejQQQcPHlTJkiUlSbGxsapQoYL+97//qVy5cgYndGwsKpcztWvXTq1bt1avXr303nvvafHixerWrZvtkudff/3V6IjZgvIPG09PT82ePVutWrUyOgrwWClcuLCWLVum+vXr223fsGGD2rZtq/j4eIOSOaYTJ07IarXKz89PW7duVdGiRW378uTJIy8vL9ZiAPBYS0tL06+//qoDBw7IarWqcuXKat68+V1vz4jsw6JyOdfRo0d15coVVatWTdeuXdOAAQNsbzZPnDgx3WW1ZkX5h42Pj4/WrVunChUqGB0FeKx06dJF0dHRmj59um3K5ZYtW9SrVy/Vrl1bM2fONDYgkIOlpqZq8eLF6RbLzJWLKxOBO926dUv58uXTzp07ua1sDsSicsjpKP+wGT9+vI4eParJkyfzznEO4eHhkeFjwdll4yQmJqpr16768ccflTt3bkl//YEWFBSkGTNmpFttHtlj1qxZ8vT0VOvWrSVJ7733nqZOnarKlStr/vz5DvMuf062d+9eBQcHKy4uTv7+/pKkQ4cOqWjRolq6dGm6S2mQva5evarIyMi7Tl/mlmXGKVu2rH744QdVr17d6Ci4g6urq3bu3KmyZcvKw8NDUVFRqlKlinbt2qXg4GAWZDSQn5+ftm3bpiJFithtT0xMVK1atRzmTgyUf9i0b99ea9euVeHChVWlShVbibnthx9+MCiZ45o1a1aGx3bt2jULkyAj/vjjD8XExNimYHLdpbH8/f315ZdfqlmzZtq0aZOeeeYZTZo0ScuWLVOuXLn4nZYDPP300/Ly8tKsWbNs1/cnJCSoW7duOnfunDZt2mRwQse1Y8cOtWrVSteuXdPVq1dVuHBhXbhwQS4uLvLy8nKYP5RzohkzZui7777TN998Y1tkFjmDt7e31qxZo8qVK6tKlSoaNWqUgoKCWAMoB3ByclJcXJy8vLzstp89e1YlS5a0LQZodsypg02hQoXUvn17o2Pgbyj0j4dhw4ZpwIABKleunF3hv379usaOHashQ4YYmM5xnTx50nY8/ve//+nFF19U79691aBBAwUGBhobDpL+WhTz999/t1vYz8PDQyNGjNCTTz5pYDL0799fbdu21ZdffqlChQpp8+bNyp07tzp37qzw8HCj4zm0Tz/9VH/88Yd8fHxUqlSpdHdiiI6ONigZnn76aW3YsEGVK1dW69atFRERoT179uiHH37Q008/bXQ8h7R06VLb57/88ovdbTJTU1O1evVqlS5d2oBkxuDMP5CDXbp0SW5ubrbP7+f2OGQ/Z2dnnTlzJt27yRcvXpSXlxerYhvEy8tLv/zyi2rWrKmaNWuqf//+6tKli44cOaLq1atzBiYHqFGjhiZMmKBmzZrZbV+zZo3Cw8O1Z88eg5KhUKFC2rJli/z9/VWoUCFt2rRJlSpV0pYtW9S1a1cdOHDA6IgO6+OPP77v/o8++iibkuBOLCqX89zvziW5c+dW6dKlNX78eLVp0yYbUxmHM/9ADubh4WErlYUKFbrr9f9Wq5XbLhns9jG4065du5iSaaBnn31Wr732mmrWrKlDhw7Zrv3ft2+fQ73Ln9P8/Y3MkSNHKiwsTEOHDrWdFdu8ebOGDRumTz75xKiI0F9/FN/+vVasWDHFxsaqUqVKcnd3V2xsrMHpHBvlPufy8/Ozfe7i4qIvvvjCwDSQ/rozhiSVKVNGv//+e7pr/h0N5R92vv/+ey1cuPCui/swjSz7rVmzxlYe165da3Aa3On2gowWi0UVKlSwewMgNTVVV65c0euvv25gQsf2+eef68MPP9TJkye1aNEi2z/427dv16uvvmpwOsd15xuZVqtVHTt2tG27PSGxbdu2vKlpoJo1a+r3339XhQoV1LRpUw0ZMkQXLlzQnDlzWIgxB0hMTNT333+vI0eO6N1331XhwoUVHR2tYsWKqUSJEkbHc1jdu3dX586d1axZMxbPzkFSUlJUunRpXbx40eHLP9P+YfPpp5/qgw8+UNeuXTVt2jR1795dR44c0bZt2/Tmm29qxIgRRkcEcpRZs2bJarWqR48emjRpkt11ZHny5FHp0qVVr149AxMCOU9kZGSGxzZp0iQLk+B+fv/9d12+fFlNmzbV+fPn1bVrV9v05RkzZrDSvIF2796t5s2by93dXcePH9fBgwfl5+enwYMH68SJE5o9e7bRER1WUFCQVq5cqSJFiuiVV15RaGioatSoYXQsSCpatKg2btyo8uXLGx3FUJR/2FSsWFEfffSRXn31Vdu9Sf38/DRkyBDFx8dr8uTJRkd0eAkJCZo+fbrd/bC7d+/O1HKDRUZGqkGDBtyXPAdav369vvrqKx09elTfffedSpQooTlz5qhMmTJq2LCh0fEA4IE1b95ctWrV0pgxY+z+Xtu4caNCQkK4nZzBEhMTtXDhQs2bN0/r16+Xv7+/OnfurJCQEC45M1BERIRy586t0aNHGx3FUJR/2Li4uCgmJkalSpWSl5eXVq1aperVq+vw4cN6+umndfHiRaMjOrTIyEgFBQXJ3d1dderUkfTX9OXExEQtXbqUM2QGio6OVu7cuW1TYZcsWaIZM2aocuXKGjp0qPLkyWNwQse0aNEihYaGqlOnTpozZ472798vPz8/ffHFF1q2bJl++uknoyM6pN27d2d4bLVq1bIwCfB4cnd3V3R0tMqWLWtX/k+cOCF/f3/duHHD6Ij4P3/++afmz5+vr7/+WocPH9atW7eMjuSw3n77bc2ePVvlypVTnTp10t0lY8KECQYly16cpoKNt7e3Ll68qFKlSqlUqVLavHmzqlevrmPHjon3iIz35ptv6uWXX9aXX34pZ2dnSX9dV963b1+9+eab2rt3r8EJHVefPn00aNAgVa1aVUePHtXLL7+sDh066LvvvtO1a9c0adIkoyM6pOHDh2vKlCnq0qWLFixYYNtev359DRs2zMBkjq1GjRqyWCz/+O8KC5ka6+LFixoyZIjWrl2rc+fO2RbNui0+Pt6gZMiXL99d7wB08OBBFS1a1IBEuJuUlBT9/vvv2rJli44fP65ixYoZHcmh7d27V7Vq1ZIkHTp0yG6fI63PQPmHTbNmzfTjjz+qVq1a6tmzp/r376/vv/9ev//+uzp06GB0PId35MgRLVq0yFb8pb9uMffOO+9wfZ/BDh06ZLum77vvvlOTJk00b948bdiwQa+88grl3yAHDx5U48aN0213c3NTYmJi9geCJOnYsWNGR0AGdO7cWUeOHFHPnj1VrFgxh/rjOKcLDg7WsGHDtHDhQkl/FZfY2FgNGjRIL7zwgsHpsHbtWs2bN0+LFi1SamqqOnTooB9//DHdLU2RvVg4+y+Uf9hMnTrV9s7+66+/rsKFCysqKkpt27ZlxfIcoFatWoqJiZG/v7/d9piYGBaTMZjVarX9f+fXX3+13SvW19dXFy5cMDKaQytevLj++OOPdNdYRkVF2d2OCdnrbve53r9/f7q7zFgsFu6JbaCoqChFRUWxsF8ONG7cOLVq1UpeXl66fv26mjRpori4ONWrV4/FmQ32xBNP6OLFi2rZsqW++uortW3bVvny5TM6Fu7w559/ymKxOOSdMSj/sHFycpKTk5Pt644dO6pjx44GJsLfr40NCwtTeHi4/vjjD7v7YX/++ecOv3iJ0erUqaPhw4erefPmioyM1JdffinprzOcTPMzTp8+fRQeHq6vv/5aFotFp0+f1qZNmzRgwAANGTLE6HiQdPToUbVv31579uyxuxTg9llmpv0bp2LFirp+/brRMXAXbm5uioqK0po1axQdHa20tDTVqlVLzZs3NzqawxsyZIheeukleXh4GB0Fd0hLS9Pw4cM1fvx4XblyRZLk6uqqiIgIffDBB3YdyMxY8A82M2bMUMGCBfXSSy/Zbb993XLXrl0NSua4nJycuDb2MbB792516tRJsbGxeuedd/TRRx9J+mtxmYsXL2revHkGJ3RcH3zwgSZOnGhbACtv3rwaMGCA/v3vfxucDJLUtm1bOTs7a9q0afLz89OWLVsUHx+viIgIjRs3To0aNTI6osPatm2bBg0apCFDhiggIEC5c+e22+/m5mZQMgB4cO+//76mT5+ujz/+WA0aNJDVatWGDRs0dOhQ9erVy2FmzVD+YePv768pU6aoadOmdtsjIyPVu3dvHTx40KBkjuvEiRMZHsv02Jznxo0bcnZ2TvdHM7LXtWvXtH//fqWlpaly5coqWLCg0ZHwfzw9PbVmzRpVq1ZN7u7u2rp1q/z9/bVmzRpFRERox44dRkd0WIcPH9arr76a7hhYrVbecDbYPy1YyswmID0fHx9NmTJFQUFBdtuXLFmivn376tSpUwYly15M+4fNiRMnVKZMmXTbS5UqpdjYWAMSgWtjHy83b96866rYJUuWNCiRY1u1apUaNGggFxcX2+0xkbOkpqba3ozx9PTU6dOn5e/vr1KlSvGGs8E6deqkPHnyaN68eSz4l8MsXrzY7uuUlBQdO3ZMuXLlUtmyZSn/wF3Ex8erYsWK6bZXrFjRoe5eQvmHjZeXl3bv3p1ucaxdu3apSJEixoSCDdfG5lyHDh1Sz549tXHjRrvtnCEz1gsvvKDk5GTVrl1bTZo0UWBgoBo0aMCZ/xwkICBAu3fvlp+fn+rWrasxY8YoT548mjp1KosyGmzv3r3asWNHukVmYby7zYi5dOmSunXrpvbt2xuQCMj5qlevrsmTJ+vTTz+12z558mSHWtiU8g+bV155RWFhYXJ1dbXdHisyMlLh4eF65ZVXDE6H8PBwlSlTRr/++utdr42Fcbp3765cuXJp2bJlKl68OGfIcoiEhARt3bpVkZGRWrdunT7//HPduHFDtWrVUmBgIAtl5gAffvihrl69KkkaPny42rRpo0aNGqlIkSL69ttvDU7n2OrUqaOTJ09S/h8Tbm5uGjZsmNq0aaPQ0FCj4wA5zpgxY9S6dWv9+uuvqlevniwWizZu3KiTJ0/qp59+MjpetuGaf9jcvHlToaGh+u6775Qr11/vC6WlpalLly6aMmWK8uTJY3BCx8a1sTlXgQIFtH379rtOJ0POsXfvXo0bN05z585VWloaMzJyqPj4eHl4ePAmmsG+++47DR06VO+++66qVq2abu2SatWqGZQM93L79swJCQlGR3Fohw4d0rp16+56GSCXZBjr9OnT+vzzz3XgwAFZrVZVrlxZffv2lY+Pj9HRsg3lH+kcPnxYO3fuVP78+VW1alWuJc8hPDw8tH37dvn5+als2bL673//q6ZNm+rIkSOqWrWqrl27ZnREh/Xkk09q4sSJatiwodFR8DcxMTG2s/6RkZFKTU1Vw4YNFRgYqCZNmjjUND/gQd3vtldczmSsO6ctW61WnTlzRnPmzFHjxo01f/58g5Jh2rRpeuONN+Tp6Slvb2+7NzEtFouio6MNTAdQ/oHHRqNGjRQREaF27dopJCRECQkJ+vDDDzV16lRt375de/fuNTqiw1qzZo0+/PBDjRw58q5nyLglljGcnJxUtGhR9evXT0FBQapSpYrRkYDHxj/dbYYTA8a5c3Hm27/rmjVrpvfff1+urq4GJUOpUqXUt29fDRw40OgouIuEhARNnz5dMTExslgsqlSpkrp3767ChQsbHS3bUP6Bx8Qvv/yiq1evqkOHDjp69KjatGmjAwcO2K6NbdasmdERHdbtM2R3TlNmwT9j9evXT7/99pv27dunGjVqKDAwUIGBgWrUqBGL/gEZdK87zLRt29bAVEDO5Obmpp07d7JgaQ4UGRmp4OBgubm52e4AtH37diUmJmrp0qVq0qSJwQmzB+UfeIxxbWzOEBkZed/9jvIPSk6VmJio9evXKzIyUpGRkdqzZ49q1KihzZs3Gx0NyLG4wwzw4Hr27Kknn3xSr7/+utFRcIeAgADVr19fX375pZydnSX99Xusb9++2rBhg8PMoGW1f+Ax5kjTlHIyyn3OlpaWplu3bunmzZtKTk5WSkqKjh8/bnQsIEfjDjM5V/v27TP8pv8PP/yQxWnw9zUYypUrp8GDB2vz5s13vQwwLCwsu+Ph/xw5ckSLFi2yFX9JcnZ21jvvvKPZs2cbmCx7Uf6hqVOnKigoSN7e3kZHAR5biYmJ2rp1611X9+3SpYtBqRxbeHi41q1bp3379qlw4cJq3LixevfurcDAQAUEBBgdD8jRNm3apDVr1qho0aJycnKSs7OzGjZsqFGjRiksLIw7zBjI3d1dixcvlru7u9305aSkJLVr147ZgNls4sSJdl8XLFjQNtPs7ywWC+XfQLVq1VJMTEy625fGxMSoRo0axoQyAOUfmj9/vsLCwlS9enUFBwcrODiYhbGAB/Djjz+qU6dOunr1qlxdXdOt7kv5N8apU6fUq1cvyj7wEFJTU21rY3h6eur06dPy9/dXqVKldPDgQYPTObZixYqpY8eOmjJlSrrpy25ubho7dqzBCR3LsWPHjI6ADAgLC1N4eLj++OMPPf3005KkzZs36/PPP9fo0aO1e/du21gz38qUa/4h6a/VL5cvX66lS5fql19+kaenp4KDgxUUFKTGjRvf95Y/gKOrUKGCWrVqpZEjR8rFxcXoOADwyLjDTM5VtGhRRUVFpTuDefDgQdWvX18XL140KBmQc/1Tl7m9tonZF2rmzD8k/XUP+c6dO6tz5866efOm1qxZo6VLlyo0NFTXrl1T69atFRQUpOeff14FChQwOi6Qo5w6dUphYWEU/xzo0KFDWrdu3V0vxxgyZIhBqYCc78MPP9TVq1clScOHD1ebNm3UqFEj2x1mYJxbt27dc/rynb/nkL3eeeedu263WCzKly+fypUrp+DgYNZsMgAzNP7CmX/8o99//11Lly7VkiVL9OKLL2rw4MFGRwJylA4dOuiVV15Rx44djY6Cv5k2bZreeOMNeXp6ytvbO93lGNHR0QamAx4/3GEmZ3jnnXc0c+ZM/etf/7Kbvjx69Gh16dJFEyZMMDih42ratKmio6OVmpoqf39/Wa1WHT58WM7OzqpYsaIOHjwoi8WiqKgoVa5c2ei4cECUfzyQlJSUdCuXAo5o6dKlts/Pnz+vYcOGqXv37ndd3TcoKCi740FSqVKl1LdvXw0cONDoKACQadLS0jRu3Dj95z//0ZkzZyRJxYsXV3h4uCIiIuxWM0f2mjRpktavX68ZM2bIzc1NknTp0iX17NlTDRs2VK9evRQSEqLr16/rl19+MTgtHBHlHwAeQkbXwTD7tWM5mZubm3bu3Ck/Pz+jowBAlrh06ZIk2YomjFWiRAmtWrUq3Vn9ffv2qUWLFjp16pSio6PVokULXbhwwaCUcGSs4gYADyEtLS1DHxR/47z00ktauXKl0TEAIMu4ublR/HOQpKQknTt3Lt328+fP296oKVSokG7evJnd0QBJLPgHADCRTz/91PZ5uXLlNHjwYG3evPmul2Nwv2UAj6MyZcrcd92Fo0ePZmMa/F1wcLB69Oih8ePH68knn5TFYtHWrVs1YMAAtWvXTpK0detWVahQwdigDuTQoUP8vP+Gaf8AkAkiIyM1btw4xcTEyGKxqFKlSnr33XfVqFEjo6M5lDJlymRonMVi4Q9kAI+l//znP3Zfp6SkaMeOHVqxYoXeffddDRo0yKBkuHLlivr376/Zs2fr1q1bkqRcuXKpa9eumjhxogoUKKCdO3dKkmrUqGFcUAdSoEABlSxZUkFBQQoODlb9+vWNjmQoyj/sJCYmauvWrXe9LVaXLl0MSgXkbN988426d++uDh06qEGDBrJardq4caMWL16smTNnKiQkxOiIAACT+/zzz/X7779rxowZRkdxeFeuXNHRo0dltVpVtmxZFSxY0OhIDuvGjRtatWqVlixZomXLlslqtapNmzYKDg5WixYtlC9fPqMjZivKP2x+/PFHderUSVevXpWrq2u622LFx8cbmA7IuSpVqqTevXurf//+dtsnTJigadOmKSYmxqBkjm3YsGEaMGCAXFxc7LZfv35dY8eO1ZAhQwxKBgCZ7+jRo6pRo4bt2nIA9qxWqzZt2qSlS5dq6dKlOnHihJo3b67g4GC1adNGXl5eRkfMcpR/2FSoUEGtWrXSyJEj0/2xDODe8ubNq3379qlcuXJ22//44w8FBAToxo0bBiVzbM7Ozjpz5ky6f8wvXrwoLy8vFmMEYCpjxozRF198oePHjxsdxaF06NBBM2fOlJubmzp06HDfsT/88EM2pUJGHD58WEuXLtWSJUu0ZcsWTZgwQW+++abRsbIUC/7B5tSpUwoLC6P4Aw/I19dXq1evTlf+V69eLV9fX4NSwWq13nVRrF27dqlw4cIGJAKAR1ezZk27321Wq1VxcXE6f/68vvjiCwOTOSZ3d3fb8XB3dzc4DR5E+fLlFRERoYiICF28eNEhZjlT/mHTsmVL/f7779wTG3hAERERCgsL086dO1W/fn1ZLBZFRUVp5syZ6RZmQtbz8PCQxWKRxWJRhQoV7P5ITk1N1ZUrV/T6668bmBAAHt7tVeNvc3JyUtGiRRUYGKiKFSsaE8qB/X2NBdZbeHwVKVJERYoUMTpGlmPaP2ymT5+uYcOGqXv37ne9LVZQUJBByYCcb/HixRo/frzt+v7bq/0HBwcbnMzxzJo1S1arVT169NCkSZPszsTkyZNHpUuXVr169QxMCAAAkP0o/7BxcnK65z6LxcL1sQAeK5GRkapfv366NzIB4HGXmpqqxYsX291eNjg4WLlyManXSGfPntWAAQO0evVqnTt3TnfWLP6WhtH4DQGbO2/tBwCPszJlyujMmTP33F+yZMlsTAMAmWPv3r0KDg5WXFyc/P39JUmHDh1S0aJFtXTpUlWtWtXghI6rW7duio2N1eDBg1W8ePG7rjsDGIkz/wDwEAoXLqxDhw7J09PTdo35vRQsWFBVqlTRJ598omrVqmVjSsfm5OR03+PCGRgAj6Onn35aXl5emjVrljw8PCRJCQkJ6tatm86dO6dNmzYZnNBxubq6av369apRo4bRUXCHkydPymKx6IknnpAkbd26VfPmzVPlypXVu3dvg9NlH878w05kZKTGjRtnN43s3XffVaNGjYyOBuQoEydOlKurqyRp0qRJ9x2bnJysn376Sd27d9f27duzIR0kaceOHXZfp6SkaMeOHZowYYJGjBhhUCoAeDS7du3S77//biv+0l8LnY4YMUJPPvmkgcng6+ubbqo/coaQkBD17t1boaGhiouL07PPPqsqVarom2++UVxcnIYMGWJ0xGzBmX/YfPPNN+revbs6dOigBg0ayGq1auPGjVq8eLFmzpypkJAQoyMCj62TJ0+qdu3aOnfunNFRHN7y5cs1duxYrVu3zugoAPDAatSooQkTJqhZs2Z229esWaPw8HDt2bPHoGRYuXKlxo8fr6+++kqlS5c2Og7+xsPDQ5s3b5a/v78+/fRTffvtt9qwYYNWrlyp119/XUePHjU6Yrag/MOmUqVK6t27t/r372+3fcKECZo2bZptFXMA99a3b18NGzZMnp6eRkfBPRw+fFg1atTQ1atXjY4CABly6dIl2+dRUVF67733NHToUD399NOSpM2bN2vYsGEaPXq0WrVqZVRMh3TnpX9Xr17VrVu35OLikm7BWUe4j3xOVbBgQe3du1elS5dWUFCQGjRooIEDByo2Nlb+/v66fv260RGzBeUfNnnz5tW+fftUrlw5u+1//PGHAgICdOPGDYOSAY8PNzc37dy5U35+fkZHcXh//2NZkqxWq86cOaOhQ4fqwIED2rlzpzHBAOAB3bmGye0/329v+/vXrGeSvWbNmpXhsV27ds3CJLifunXrqmnTpmrdurVatGihzZs3q3r16tq8ebNefPFF/fnnn0ZHzBZc8w8bX19frV69Ol35X716tXx9fQ1KBTxeeD815yhUqFC6Bf+sVqt8fX21YMECg1IBwINbu3at0RFwDxT6x8Mnn3yi9u3ba+zYseratauqV68uSVq6dKmeeuopg9NlH8o/bCIiIhQWFqadO3eqfv36slgsioqK0syZM/Wf//zH6HgA8EDu/GPZyclJRYsWVbly5bgXNoDHSpMmTYyOgAw6cuSIZsyYoSNHjug///mPvLy8tGLFCvn6+qpKlSpGx3NYgYGBunDhgi5dumS3WGbv3r3l4uJiYLLsxbR/2Fm8eLHGjx9vu77/9mr/wcHBBicDAABwTLt3787wWG4pa5zIyEg9//zzatCggX777TfFxMTIz89PY8aM0datW/X9998bHREOjvIPSdKtW7c0YsQI9ejRgyn+wCO4fv26UlJS7La5ubkZlAZHjhzRpEmT7G5fGh4errJlyxodDQAy7PY1///0ZzvX/BurXr16eumll/TOO+/I1dVVu3btkp+fn7Zt26Z27drp1KlTRkd0KLVq1dLq1avl4eGhmjVrprsU8O+io6OzMZlxmPcISVKuXLls18AAeDDXrl3Te++9p4ULF+rixYvp9vOHmDF++eUXBQUFqUaNGna3L61SpYp+/PFHPfvss0ZHBIAMOXbsmNERkAF79uzRvHnz0m0vWrToXf8+QNYKDg5W3rx5bZ/fr/w7Cso/bJo3b65169apW7duRkcBHivvvvuu1q5dqy+++EJdunTR559/rlOnTumrr77S6NGjjY7nsAYNGqT+/funOwaDBg3SwIEDKf8AHhulSpVKt23//v2KjY3VzZs3bdssFstdxyJ7FCpUSGfOnFGZMmXstu/YsUMlSpQwKJXj+uijj2yfDx061LggOQjT/mHz1VdfaejQoerUqZNq166tAgUK2O0PCgoyKBmQs5UsWVKzZ89WYGCg3NzcFB0drXLlymnOnDmaP3++fvrpJ6MjOqR8+fJpz549Kl++vN32Q4cOqVq1aty+FMBj6ejRo2rfvr327NljdynA7bOazDYzznvvvadNmzbpu+++U4UKFRQdHa2zZ8+qS5cu6tKli10ZRfbq3r27OnfurGbNmjn0DADO/MPmjTfekCRNmDAh3T6uIQPuLT4+3vYuv5ubm+Lj4yVJDRs2tP3/CtmvaNGi2rlzZ7ryv3PnTnl5eRmUCgAeTXh4uMqUKaNff/1Vfn5+2rJli+Lj4xUREaFx48YZHc+hjRgxQt26dVOJEiVktVpVuXJlpaamKiQkRB9++KHR8RzaxYsX1bp1axUpUkSvvPKKQkNDVaNGDaNjZTvKP2zS0tKMjgA8lvz8/HT8+HGVKlVKlStX1sKFC/XUU0/pxx9/VKFChYyO57B69eql3r176+jRo3a3L/3kk08UERFhdDwAeCibNm3SmjVrVLRoUTk5OcnZ2VkNGzbUqFGjFBYWph07dhgd0WHlzp1bc+fO1bBhw7Rjxw6lpaWpZs2a6d6ERvZbunSpEhMTtXDhQs2bN0+TJk2Sv7+/OnfurJCQEJUuXdroiNmCaf+wOXny5D1X+t+8ebOefvrpbE4EPB4mTpwoZ2dnhYWFae3atWrdurVSU1N169YtTZgwQeHh4UZHdEhWq1WTJk3S+PHjdfr0aUmSj4+P3n33XYWFhTn0tD8Ajy8PDw9t375dfn5+Klu2rP773/+qadOmOnLkiKpWrapr164ZHRHI8f7880/Nnz9fX3/9tQ4fPqxbt24ZHSlbUP5hU7FiRW3YsEFFihSx275hwwa1bt1aiYmJxgQDHjOxsbH6/fffVbZsWVWvXt3oOJB0+fJlSZKrq6vBSQDg0TRq1EgRERFq166dQkJClJCQoA8//FBTp07V9u3btXfvXqMjOpR33nknw2Pvdmktsl9KSoqWL1+ub775RsuXL1fhwoUd5jaMTPuHTaNGjdSiRQutW7fO9gfyb7/9prZt27JCJvAASpYsqZIlSxodw+Fdv35dVqtVLi4ucnV11YkTJzR9+nRVrlxZLVq0MDoeADyUDz/8UFevXpUkDR8+XG3atFGjRo1UpEgRffvttwanczwZvcyC2WbGW7t2rebNm6dFixYpNTVVHTp00I8//qhmzZoZHS3bcOYfNlarVS+99JLOnTunlStXatOmTQoKCtLw4cOZtgz8g9WrV2v16tU6d+5cuvUzvv76a4NSObYWLVqoQ4cOev3115WYmCh/f3/lyZNHFy5c0IQJE1iMEYBpxMfHy8PDg4IJ3MMTTzyhixcvqmXLlurUqZPatm2rfPnyGR0r21H+YSclJUWtW7fW1atXtXv3bo0aNUpvvfWW0bGAHO3jjz/WsGHDVKdOHRUvXjzdH1+LFy82KJlj8/T0VGRkpKpUqaL//ve/+uyzz7Rjxw4tWrRIQ4YMUUxMjNERAQBANpg6dapeeukleXh4GB3FUJR/B7d79+502y5fvqxXX31VrVu3tjszVq1ateyMBjw2ihcvrjFjxig0NNToKPgbFxcXHThwQCVLllTHjh1VpUoVffTRRzp58qT8/f1ZFAsAAAf0559/ymKxqESJEkZHyXaUfwfn5OQki8Wiv/9n8Pevb39usViUmppqVEwgRytSpIi2bt2qsmXLGh0Ff1OtWjW99tprat++vQICArRixQrVq1dP27dvV+vWrRUXF2d0RAAAkA3S0tI0fPhwjR8/XleuXJH01yLAERER+uCDD+Tk5GRwwuzBgn8O7tixY0ZHAB57r732mubNm6fBgwcbHQV/M2TIEIWEhKh///565plnVK9ePUnSypUrVbNmTYPTAQCA7PLBBx9o+vTpGj16tBo0aCCr1aoNGzZo6NChunHjhkaMGGF0xGzBmX8AeETh4eGaPXu2qlWrpmrVqil37tx2+7m1j3Hi4uJ05swZVa9e3fau/tatW+Xm5qaKFSsanA4AAGQHHx8fTZkyRUFBQXbblyxZor59+zrMrf4o/0hn//79io2N1c2bN+223/l/FgB/adq06T33WSwWrVmzJhvTAAAA4O/y5cun3bt3q0KFCnbbDx48qBo1auj69esGJctelH/YHD16VO3bt9eePXvSXfcviWv+AQAAADx26tatq7p16+rTTz+12/72229r27Zt2rx5s0HJshflHzZt27aVs7Ozpk2bJj8/P23dulUXL15URESExo0bp0aNGhkdEcjxHHkFWQAAgJwoMjJSrVu3VsmSJVWvXj1ZLBZt3LhRJ0+e1E8//eQwPccxljVEhmzatEnDhg1T0aJF5eTkJCcnJzVs2FCjRo1SWFiY0fGAHCstLU3Dhg2Tu7u7SpUqpZIlS6pQoUL697//rbS0NKPjAQAAOLQmTZro0KFDat++vRITExUfH68OHTro4MGDDlP8JVb7x9+kpqaqYMGCkiRPT0+dPn1a/v7+KlWqlA4ePGhwOiDnYgVZAACAnM3Hx8fh/yaj/MMmICBAu3fvlp+fn+rWrasxY8YoT548mjp1qvz8/IyOB+RYs2bN0n//+1+7RTGrV6+uEiVKqG/fvg7/Dw0AAEB22717d4bHVqtWLQuT5ByUf9h8+OGHunr1qiRp+PDhatOmjRo1aqQiRYro22+/NTgdkHPFx8ff9bZxFStWVHx8vAGJAAAAHFuNGjXsFjG/F4vF4jALm7PgH+4rPj5eHh4ethX/AaTHCrIAAAA5y4kTJzI8tlSpUlmYJOeg/APAI2IFWQAAAOR0lH8AyASnT5/W559/rgMHDshqtapy5crq27evfHx8jI4GAADg0EaNGqVixYqpR48edtu//vprnT9/XgMHDjQoWfai/AMAAAAATKt06dKaN2+e6tevb7d9y5YteuWVV3Ts2DGDkmUvFvwDgEyQmJio6dOnKyYmRhaLRZUrV1aPHj3k7u5udDQAAACHFhcXp+LFi6fbXrRoUZ05c8aARMZwMjoAADzufv/9d5UtW1YTJ05UfHy8Lly4oAkTJqhs2bKKjo42Oh4AAIBD8/X11YYNG9Jt37Bhg0NdosmZfwB4RP3791dQUJCmTZumXLn++rV669Ytvfbaa+rXr59+++03gxMCAAA4rtt/k6WkpKhZs2aSpNWrV+u9995TRESEwemyD9f8A8Ajyp8/v3bs2KGKFSvabd+/f7/q1Kmja9euGZQMAAAAVqtVgwYN0qeffqqbN29KkvLly6eBAwdqyJAhBqfLPpz5B4BH5ObmptjY2HTl/+TJk3J1dTUoFQAAACTJYrHok08+0eDBgxUTE6P8+fOrfPnyyps3r9HRshXlHwAe0csvv6yePXtq3Lhxql+/viwWi6KiovTuu+/q1VdfNToeAAAAJBUsWFBPPvmk0TEMQ/kHgEc0btw4WSwWdenSRbdu3ZIk5c6dW2+88YZGjx5tcDoAAACAa/4BINNcu3ZNR44ckdVqVbly5eTi4mJ0JAAAAEASt/oDgEfWo0cPXb58WS4uLqpataqqVasmFxcXXb16VT169DA6HgAAAMCZfwB4VM7Ozjpz5oy8vLzstl+4cEHe3t62SwEAAAAAo3DNPwA8pEuXLslqtcpqtery5cvKly+fbV9qaqp++umndG8IAAAAAEag/APAQypUqJAsFossFosqVKiQbr/FYtHHH39sQDIAAADAHtP+AeAhRUZGymq1qlmzZlq0aJEKFy5s25cnTx6VKlVKPj4+BiYEAAAA/kL5B4BHdOLECZUsWVIWi8XoKAAAAMBdUf4B4CHs3r07w2OrVauWhUkAAACAf0b5B4CH4OTkJIvFon/6FWqxWJSamppNqQAAAIC7Y8E/AHgIx44dMzoCAAAAkGGUfwB4CKVKlUq3bf/+/YqNjdXNmzdt2ywWy13HAgAAANmJ8g8Aj+jo0aNq37699uzZY3cpwO0FAJn2DwAAAKM5GR0AAB534eHhKlOmjM6ePSsXFxft3btXv/32m+rUqaN169YZHQ8AAABgwT8AeFSenp5as2aNqlWrJnd3d23dulX+/v5as2aNIiIitGPHDqMjAgAAwMFx5h8AHlFqaqoKFiwo6a83Ak6fPi3pr3UBDh48aGQ0AAAAQBLX/APAIwsICNDu3bvl5+enunXrasyYMcqTJ4+mTp0qPz8/o+MBAAAATPsHgEf1yy+/6OrVq+rQoYOOHj2qNm3a6MCBAypSpIi+/fZbNWvWzOiIAAAAcHCUfwDIAvHx8fLw8LCt+A8AAAAYifIPAAAAAIDJseAfAAAAAAAmR/kHAAAAAMDkKP8AAAAAAJgc5R8AgP/X3t2ERLX/cRx/H3pwoqfthCEaIpOmSEhBLUqCiiCCIIs2WVGg1KYCFzJmidMixx6IombAMRAiCAJbVBC4CFc9WIJTs5AgQqlFRRAFjd7FpeEO917/9S/r7/zfLzibc87v6ew+M+f3PfqfMjAwQBAEvHv37ndPRZKkgmH4lyRJ36yxsZEgCAiCgNmzZ1NSUkJTUxNv3779aWOsWbOGsbExFi9e/NP6lCTp/53hX5IkfZfNmzczNjbGixcvSCaT9Pf309zc/NP6nzt3LuFw2E9lSpL0Exn+JUnSdykqKiIcDrN06VI2btzIzp07uXv3bu56T08Py5cvJxQKEYlEuHjxYl77wcFBamtrCYVC1NXVcfPmTYIgYGhoCPjn1/5v3LhBVVUVRUVFlJaWEo/H8/osLS0lFouxb98+Fi5cSElJCVeuXJm2ZyBJ0kxj+JckSf+10dFRbt++zZw5cwBIJBK0trbS2dlJOp0mFosRjUbp7e0F4MOHD2zdupXq6moePXpER0cHLS0tU47x8OFDGhoa2LVrF8PDw7S3txONRkmlUnn3xeNx6urqePz4Mc3NzTQ1NfHs2bNpWbckSTPN7N89AUmSNLPcunWLBQsWkM1m+fTpEwDd3d0AdHR0EI/H2b59OwBlZWWMjIxw+fJl9uzZQ19fH0EQkEgkCIVCVFZW8urVKw4cOPCv43V3d7Nhwwai0SgAFRUVjIyMcPr0aRobG3P3bdmyJbf9oKWlhTNnzjAwMEAkEpmOxyBJ0oxi+JckSd+lvr6eS5cu8fHjR5LJJJlMhsOHD/PmzRtevnzJ/v3788L8ly9fcsX7nj9/Tk1NDaFQKHd91apVU46XTqfZtm1b3rm1a9dy9uxZstkss2bNAqCmpiZ3PQgCwuEwr1+//uH1SpJUCAz/kiTpu8yfP5/y8nIAzp8/T319PSdOnODQoUPAn6/+r169Oq/N14A+OTn5t0J+k5OTU473rW2+bj34KggCJiYmvmFFkiQVPvf8S5KkH3L8+HG6urrIZrMUFxczOjpKeXl53lFWVgZAJBLh6dOnfP78Odf+wYMHU/ZfWVnJ/fv3884NDg5SUVGR+1FBkiRNzfAvSZJ+yPr166mqqiIWi9He3s6pU6c4d+4cmUyG4eFhenp6cjUBdu/ezcTEBAcPHiSdTnPnzh26uroA/vXTfkePHuXevXt0dHSQyWTo7e3lwoULHDt27JetUZKkmc7wL0mSftiRI0dIJBJs2rSJZDJJKpWiurqadevWkUqlcv/8L1q0iP7+foaGhqitraW1tZW2tjaAvDoAf7Vy5UquX7/OtWvXWLFiBW1tbZw8eTKv2J8kSZpaMPmfNtpJkiRNo76+Pvbu3cv79++ZN2/e756OJEkFyYJ/kiTpl7p69SrLli2juLiYJ0+e0NLSQkNDg8FfkqRpZPiXJEm/1Pj4OG1tbYyPj7NkyRJ27NhBZ2fn756WJEkFzdf+JUmSJEkqcBb8kyRJkiSpwBn+JUmSJEkqcIZ/SZIkSZIKnOFfkiRJkqQCZ/iXJEmSJKnAGf4lSZIkSSpwhn9JkiRJkgqc4V+SJEmSpAJn+JckSZIkqcD9AdQFZoDaxIkZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_grouped = clean_data.groupby('region')['price'].mean().sort_values(ascending=False)\n",
    "region_grouped.plot(kind='bar', figsize=(12, 6))\n",
    "plt.title('Mean Price by Region')\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Mean Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "238df8c6-3f7c-4e7f-9409-7892b767433c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Group by vehicle type and calculate the median price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f34c505b-4d79-4395-8510-668d16251758",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Price')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_type_grouped = clean_data.groupby('type')['price'].median().sort_values(ascending=False)\n",
    "vehicle_type_grouped.plot(kind='bar', figsize=(12, 6))\n",
    "plt.title('Median Price by Vehicle Type')\n",
    "plt.xlabel('Vehicle Type')\n",
    "plt.ylabel('Median Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdeb71df-aa17-474b-b3ee-07236eb47626",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Correlation Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b4a89e17-dc71-42c9-b69c-e08d1725e755",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_2284\\3770096520.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  correlation_matrix = clean_data.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Heatmap')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = clean_data.corr()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Heatmap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "52070615-8fc3-43b2-8111-ba98b7adf1b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fit an ARIMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d80e1b4a-f396-43f4-a5b8-41ddedb7a654",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "# Fit an ARIMA model\n",
    "model = ARIMA(clean_data['price'], order=(5, 1, 0))\n",
    "model_fit = model.fit()\n",
    "\n",
    "# Forecast with the ARIMA model\n",
    "forecast = model_fit.forecast(steps=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c7d28737-eb70-47d7-844d-e22a7bde3861",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Build the time-series chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "14b68d4d-aeb6-4770-a427-65cc2e12b1cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(clean_data.index, clean_data['price'], label='Price', color='blue')\n",
    "plt.title('Vehicle Prices Over Time')\n",
    "plt.xlabel('Posting Date')\n",
    "plt.ylabel('Price')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ba8e4a2-b790-451d-9c1e-513a218e681b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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