Amazon_Sales_Report_Analysis.ipynb

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   "metadata": {
    "id": "lVPGy-mcYGZ4"
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4m0fN8Xd0JFQ"
   },
   "source": [
    "# **Exploratory Data Analysis (EDA)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tnyinMIZnZd"
   },
   "source": [
    "**1. Data Inspection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "height": 568
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    "id": "aBECpeXfYMRy",
    "outputId": "0b889bc6-3310-48f9-a936-54fdb95202d0"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7976\\1137165660.py:2: DtypeWarning: Columns (23) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('D:\\Slash project\\Amazon sales Report\\Amazon Sale Report.csv')\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>ship-service-level</th>\n",
       "      <th>Style</th>\n",
       "      <th>SKU</th>\n",
       "      <th>Category</th>\n",
       "      <th>...</th>\n",
       "      <th>currency</th>\n",
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       "      <th>ship-city</th>\n",
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       "      <th>ship-postal-code</th>\n",
       "      <th>ship-country</th>\n",
       "      <th>promotion-ids</th>\n",
       "      <th>B2B</th>\n",
       "      <th>fulfilled-by</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>405-8078784-5731545</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>SET389</td>\n",
       "      <td>SET389-KR-NP-S</td>\n",
       "      <td>Set</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>647.62</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>400081.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>171-9198151-1101146</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>JNE3781</td>\n",
       "      <td>JNE3781-KR-XXXL</td>\n",
       "      <td>kurta</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>406.00</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>560085.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>Amazon PLCC Free-Financing Universal Merchant ...</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>404-0687676-7273146</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>JNE3371</td>\n",
       "      <td>JNE3371-KR-XL</td>\n",
       "      <td>kurta</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>329.00</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>410210.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>IN Core Free Shipping 2015/04/08 23-48-5-108</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>403-9615377-8133951</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>J0341</td>\n",
       "      <td>J0341-DR-L</td>\n",
       "      <td>Western Dress</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>753.33</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>605008.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>407-1069790-7240320</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>JNE3671</td>\n",
       "      <td>JNE3671-TU-XXXL</td>\n",
       "      <td>Top</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>574.00</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>600073.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 24 columns</p>\n",
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      "text/plain": [
       "   index             Order ID      Date                        Status  \\\n",
       "0      0  405-8078784-5731545  04-30-22                     Cancelled   \n",
       "1      1  171-9198151-1101146  04-30-22  Shipped - Delivered to Buyer   \n",
       "2      2  404-0687676-7273146  04-30-22                       Shipped   \n",
       "3      3  403-9615377-8133951  04-30-22                     Cancelled   \n",
       "4      4  407-1069790-7240320  04-30-22                       Shipped   \n",
       "\n",
       "  Fulfilment Sales Channel  ship-service-level    Style              SKU  \\\n",
       "0   Merchant      Amazon.in           Standard   SET389   SET389-KR-NP-S   \n",
       "1   Merchant      Amazon.in           Standard  JNE3781  JNE3781-KR-XXXL   \n",
       "2     Amazon      Amazon.in          Expedited  JNE3371    JNE3371-KR-XL   \n",
       "3   Merchant      Amazon.in           Standard    J0341       J0341-DR-L   \n",
       "4     Amazon      Amazon.in          Expedited  JNE3671  JNE3671-TU-XXXL   \n",
       "\n",
       "        Category  ... currency  Amount    ship-city   ship-state  \\\n",
       "0            Set  ...      INR  647.62       MUMBAI  MAHARASHTRA   \n",
       "1          kurta  ...      INR  406.00    BENGALURU    KARNATAKA   \n",
       "2          kurta  ...      INR  329.00  NAVI MUMBAI  MAHARASHTRA   \n",
       "3  Western Dress  ...      INR  753.33   PUDUCHERRY   PUDUCHERRY   \n",
       "4            Top  ...      INR  574.00      CHENNAI   TAMIL NADU   \n",
       "\n",
       "  ship-postal-code  ship-country  \\\n",
       "0         400081.0            IN   \n",
       "1         560085.0            IN   \n",
       "2         410210.0            IN   \n",
       "3         605008.0            IN   \n",
       "4         600073.0            IN   \n",
       "\n",
       "                                       promotion-ids    B2B  fulfilled-by  \\\n",
       "0                                                NaN  False     Easy Ship   \n",
       "1  Amazon PLCC Free-Financing Universal Merchant ...  False     Easy Ship   \n",
       "2       IN Core Free Shipping 2015/04/08 23-48-5-108   True           NaN   \n",
       "3                                                NaN  False     Easy Ship   \n",
       "4                                                NaN  False           NaN   \n",
       "\n",
       "  Unnamed: 22  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "# Load the dataset and inspect the first few rows to understand its structure.\n",
    "df = pd.read_csv('D:\\Slash project\\Amazon sales Report\\Amazon Sale Report.csv')\n",
    "df.head()"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 128975 entries, 0 to 128974\n",
      "Data columns (total 24 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   index               128975 non-null  int64  \n",
      " 1   Order ID            128975 non-null  object \n",
      " 2   Date                128975 non-null  object \n",
      " 3   Status              128975 non-null  object \n",
      " 4   Fulfilment          128975 non-null  object \n",
      " 5   Sales Channel       128975 non-null  object \n",
      " 6   ship-service-level  128975 non-null  object \n",
      " 7   Style               128975 non-null  object \n",
      " 8   SKU                 128975 non-null  object \n",
      " 9   Category            128975 non-null  object \n",
      " 10  Size                128975 non-null  object \n",
      " 11  ASIN                128975 non-null  object \n",
      " 12  Courier Status      122103 non-null  object \n",
      " 13  Qty                 128975 non-null  int64  \n",
      " 14  currency            121180 non-null  object \n",
      " 15  Amount              121180 non-null  float64\n",
      " 16  ship-city           128942 non-null  object \n",
      " 17  ship-state          128942 non-null  object \n",
      " 18  ship-postal-code    128942 non-null  float64\n",
      " 19  ship-country        128942 non-null  object \n",
      " 20  promotion-ids       79822 non-null   object \n",
      " 21  B2B                 128975 non-null  bool   \n",
      " 22  fulfilled-by        39277 non-null   object \n",
      " 23  Unnamed: 22         79925 non-null   object \n",
      "dtypes: bool(1), float64(2), int64(2), object(19)\n",
      "memory usage: 22.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Check the data types of each column and identify any potential issues.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sX9-QfFWW0Pa"
   },
   "source": [
    "* index, Qty, and ship-postal-code should be converted into int\n",
    "\n",
    "* Date should be converted into datetime\n",
    "\n",
    "* 'index', 'SKU', 'Category', 'Size', 'ASIN', 'Courier Status', 'Qty', 'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code', 'ship-country', 'promotion-ids', 'B2B', 'fulfilled-by', and 'Unnamed: 22' have missing values\n",
    "\n",
    "* The last column (Unnamed:22) isnt necessary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kztcfMhmZzpp"
   },
   "source": [
    "**2. Summary Statistics**"
   ]
  },
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   "execution_count": 4,
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       "      <td>121180.000000</td>\n",
       "      <td>128942.000000</td>\n",
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       "      <th>mean</th>\n",
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       "      <td>0.904431</td>\n",
       "      <td>648.561465</td>\n",
       "      <td>463966.236509</td>\n",
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       "      <th>std</th>\n",
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       "      <td>281.211687</td>\n",
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       "      <th>min</th>\n",
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       "      <td>110001.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>32243.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>382421.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64487.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>500033.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
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       "      <td>788.000000</td>\n",
       "      <td>600024.000000</td>\n",
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       "      <td>128974.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>989898.000000</td>\n",
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      "text/plain": [
       "               index            Qty         Amount  ship-postal-code\n",
       "count  128975.000000  128975.000000  121180.000000     128942.000000\n",
       "mean    64487.000000       0.904431     648.561465     463966.236509\n",
       "std     37232.019822       0.313354     281.211687     191476.764941\n",
       "min         0.000000       0.000000       0.000000     110001.000000\n",
       "25%     32243.500000       1.000000     449.000000     382421.000000\n",
       "50%     64487.000000       1.000000     605.000000     500033.000000\n",
       "75%     96730.500000       1.000000     788.000000     600024.000000\n",
       "max    128974.000000      15.000000    5584.000000     989898.000000"
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   "source": [
    "# Generate summary statistics for numerical and categorical variables.\n",
    "\n",
    "# Numerical variables summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <td>1</td>\n",
       "      <td>5787</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>171-5057375-2831560</td>\n",
       "      <td>05-03-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>JNE3797</td>\n",
       "      <td>JNE3797-KR-L</td>\n",
       "      <td>Set</td>\n",
       "      <td>M</td>\n",
       "      <td>B09SDXFFQ1</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>INR</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>IN</td>\n",
       "      <td>IN Core Free Shipping 2015/04/08 23-48-5-108</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>12</td>\n",
       "      <td>2085</td>\n",
       "      <td>77804</td>\n",
       "      <td>89698</td>\n",
       "      <td>128851</td>\n",
       "      <td>88615</td>\n",
       "      <td>4224</td>\n",
       "      <td>773</td>\n",
       "      <td>50284</td>\n",
       "      <td>22711</td>\n",
       "      <td>773</td>\n",
       "      <td>109487</td>\n",
       "      <td>121180</td>\n",
       "      <td>11217</td>\n",
       "      <td>22260</td>\n",
       "      <td>128942</td>\n",
       "      <td>46100</td>\n",
       "      <td>39277</td>\n",
       "      <td>79925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Order ID      Date   Status Fulfilment Sales Channel   \\\n",
       "count                128975    128975   128975     128975         128975   \n",
       "unique               120378        91       13          2              2   \n",
       "top     171-5057375-2831560  05-03-22  Shipped     Amazon      Amazon.in   \n",
       "freq                     12      2085    77804      89698         128851   \n",
       "\n",
       "       ship-service-level    Style           SKU Category    Size        ASIN  \\\n",
       "count              128975   128975        128975   128975  128975      128975   \n",
       "unique                  2     1377          7195        9      11        7190   \n",
       "top             Expedited  JNE3797  JNE3797-KR-L      Set       M  B09SDXFFQ1   \n",
       "freq                88615     4224           773    50284   22711         773   \n",
       "\n",
       "       Courier Status currency  ship-city   ship-state ship-country  \\\n",
       "count          122103   121180     128942       128942       128942   \n",
       "unique              3        1       8955           69            1   \n",
       "top           Shipped      INR  BENGALURU  MAHARASHTRA           IN   \n",
       "freq           109487   121180      11217        22260       128942   \n",
       "\n",
       "                                       promotion-ids fulfilled-by Unnamed: 22  \n",
       "count                                          79822        39277       79925  \n",
       "unique                                          5787            1           1  \n",
       "top     IN Core Free Shipping 2015/04/08 23-48-5-108    Easy Ship       False  \n",
       "freq                                           46100        39277       79925  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical variables summary statistics\n",
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "fVH29MhJVHzu",
    "outputId": "4fed94f4-fc17-4852-dd19-3f8361a33d86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of sales\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df['Amount'], bins=20, kde=True, color='skyblue')\n",
    "plt.title(\"Distribution of Sales Amount\")\n",
    "plt.xlabel(\"Sales Amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "cf3MxfUOovpu",
    "outputId": "d63e0c2a-2fe8-4087-d58f-f38ef0c8cf76"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of Quantity\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(df['Qty'], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Quantity (Qty)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Order Quantities')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 858
    },
    "id": "cWcd6Lxvq5hU",
    "outputId": "bc4bc59f-5cd4-4fbf-b319-850473bfdaa9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7976\\880410121.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data=df, x='Status', palette='pastel')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of Status\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(data=df, x='Status', palette='pastel')\n",
    "plt.xlabel('Order Status')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Order Status Distribution')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dOasWbMI0Unr"
   },
   "source": [
    "# **Data Preprocessing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6yHa9pQ8cNzV"
   },
   "source": [
    "**1. Handling Missing Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PgmtCUkEcWzX",
    "outputId": "0d3ca846-26e8-4062-c74f-8bae1bad9d96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index                          int32\n",
      "Order ID                      object\n",
      "Date                  datetime64[ns]\n",
      "Status                        object\n",
      "Fulfilment                    object\n",
      "Sales Channel                 object\n",
      "ship-service-level            object\n",
      "Style                         object\n",
      "SKU                           object\n",
      "Category                      object\n",
      "Size                          object\n",
      "ASIN                          object\n",
      "Courier Status                object\n",
      "Qty                            int64\n",
      "currency                      object\n",
      "Amount                       float64\n",
      "ship-city                     object\n",
      "ship-state                    object\n",
      "ship-postal-code             float64\n",
      "ship-country                  object\n",
      "promotion-ids                 object\n",
      "B2B                             bool\n",
      "fulfilled-by                  object\n",
      "Unnamed: 22                   object\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7976\\2215402068.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df['Date'] = pd.to_datetime(df['Date'])\n"
     ]
    }
   ],
   "source": [
    "# Remove null values of index column first to convert because they cant be converted when they have null values\n",
    "df.dropna(subset=['index'], inplace=True)\n",
    "\n",
    "# Convert columns to specified data types\n",
    "df['index'] = df['index'].astype(int)\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "\n",
    "# Check the updated data types\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QO42z22dbouZ",
    "outputId": "c8c1f4b4-b088-40cc-cf97-e28b7d642886"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Courier Status', 'currency', 'Amount', 'ship-city', 'ship-state', 'ship-postal-code', 'ship-country', 'promotion-ids', 'fulfilled-by', 'Unnamed: 22']\n"
     ]
    }
   ],
   "source": [
    "# Identify columns with missing values\n",
    "columns_with_missing_values = df.columns[df.isnull().any()].tolist()\n",
    "\n",
    "# Check the columns with missing values\n",
    "print(columns_with_missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_qbI8PjFZNi2",
    "outputId": "2720b5f4-83ca-4e86-b94b-fa863211916e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7976\\2315490396.py:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Courier Status'].fillna(df['Status'], inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index                 0\n",
      "Order ID              0\n",
      "Date                  0\n",
      "Status                0\n",
      "Fulfilment            0\n",
      "Sales Channel         0\n",
      "ship-service-level    0\n",
      "Style                 0\n",
      "SKU                   0\n",
      "Category              0\n",
      "Size                  0\n",
      "ASIN                  0\n",
      "Courier Status        0\n",
      "Qty                   0\n",
      "currency              0\n",
      "Amount                0\n",
      "ship-city             0\n",
      "ship-state            0\n",
      "ship-postal-code      0\n",
      "ship-country          0\n",
      "promotion-ids         0\n",
      "B2B                   0\n",
      "fulfilled-by          0\n",
      "Unnamed: 22           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in columns_with_missing_values:\n",
    "    if df[col].dtype == 'int64' or df[col].dtype == 'float64':\n",
    "        # Calculate mean\n",
    "        mean_value = df[col].mean()\n",
    "        # Fill missing values using .loc for assignment\n",
    "        df.loc[df[col].isnull(), col] = mean_value\n",
    "    elif col == 'Courier Status':\n",
    "        # Fill 'Courier Status' with 'Status' column values where missing\n",
    "        df['Courier Status'].fillna(df['Status'], inplace=True)\n",
    "    else:\n",
    "        # Calculate mode\n",
    "        mode_value = df[col].mode().iloc[0]\n",
    "        # Fill missing values using .loc for assignment\n",
    "        df.loc[df[col].isnull(), col] = mode_value\n",
    "\n",
    "# Verify that missing values have been handled\n",
    "print(df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nwHmRDO7cXpx"
   },
   "source": [
    "**2. Data Type Conversion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6GiBmxSPttWL",
    "outputId": "ba0d81c2-c570-4231-942f-61ec5259db0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index                          int32\n",
      "Order ID                      object\n",
      "Date                  datetime64[ns]\n",
      "Status                        object\n",
      "Fulfilment                    object\n",
      "Sales Channel                 object\n",
      "ship-service-level            object\n",
      "Style                         object\n",
      "SKU                           object\n",
      "Category                      object\n",
      "Size                          object\n",
      "ASIN                          object\n",
      "Courier Status                object\n",
      "Qty                            int32\n",
      "currency                      object\n",
      "Amount                       float64\n",
      "ship-city                     object\n",
      "ship-state                    object\n",
      "ship-postal-code               int32\n",
      "ship-country                  object\n",
      "promotion-ids                 object\n",
      "B2B                             bool\n",
      "fulfilled-by                  object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Convert columns to specified data types\n",
    "df['Qty'] = df['Qty'].astype(int)\n",
    "df['ship-postal-code'] = df['ship-postal-code'].astype(int)\n",
    "\n",
    "# drop the last column\n",
    "df.drop(columns=['Unnamed: 22'], inplace=True)\n",
    "\n",
    "# Check the updated data types\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CwBWjiymcfUi"
   },
   "source": [
    "**3. Outlier Detection and Treatment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lNauYVq-xidT",
    "outputId": "cdb0e676-6054-439a-9e6d-03fc45223bae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128975, 23)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "4FcHhFAoiU7U",
    "outputId": "05be8691-e63d-460e-a417-97eadc436d69"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         False\n",
      "1         False\n",
      "2         False\n",
      "3         False\n",
      "4         False\n",
      "          ...  \n",
      "128970    False\n",
      "128971    False\n",
      "128972    False\n",
      "128973    False\n",
      "128974    False\n",
      "Length: 128975, dtype: bool\n",
      "0         True\n",
      "1         True\n",
      "2         True\n",
      "3         True\n",
      "4         True\n",
      "          ... \n",
      "128970    True\n",
      "128971    True\n",
      "128972    True\n",
      "128973    True\n",
      "128974    True\n",
      "Length: 128975, dtype: bool\n",
      "1220\n",
      "(127755, 23)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_columns = ['Qty', 'Amount']\n",
    "\n",
    "# Calculate z-scores for each numerical column\n",
    "z_scores = pd.DataFrame()\n",
    "for col in numerical_columns:\n",
    "    z_scores[col] = (df[col] - df[col].mean()) / df[col].std()\n",
    "\n",
    "# Set a threshold for outlier detection (e.g., z-score > 3 or < -3)\n",
    "threshold = 3\n",
    "\n",
    "# Identify outliers based on z-scores\n",
    "outliers = (z_scores.abs() > threshold).any(axis=1)\n",
    "\n",
    "# The rows that are false have no outlier\n",
    "print(outliers)\n",
    "print(~outliers)\n",
    "print(outliers.sum())\n",
    "\n",
    "# Remove rows that have outliers by negating the outliers condition where it selects all rows where the value of outliers is False, effectively excluding the outliers.\n",
    "df = df[~outliers]\n",
    "\n",
    "# Verify that outliers have been removed\n",
    "print(df.shape)\n",
    "\n",
    "# Visualize the distribution of quantity and amount columns\n",
    "plt.figure(figsize=(10, 6))\n",
    "for i, col in enumerate(numerical_columns, 1):\n",
    "    plt.subplot(1, len(numerical_columns), i)\n",
    "    sns.boxplot(data=df, y=col, color='skyblue')\n",
    "    plt.title(f'Distribution of {col}')\n",
    "    plt.grid(axis='y')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "id": "1jwxs8gG2O5A",
    "outputId": "42917ff6-3279-40a6-ec69-91357cd07a2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "688\n",
      "(127067, 23)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since there are still some outliers in the amount column, removing them is a must\n",
    "\n",
    "# Recompute z-scores for the \"Amount\" column\n",
    "z_scores_amount = (df['Amount'] - df['Amount'].mean()) / df['Amount'].std()\n",
    "\n",
    "# Set a threshold for outlier detection (e.g., z-score > 3 or < -3)\n",
    "threshold_amount = 3\n",
    "\n",
    "# Identify outliers based on updated z-scores\n",
    "outliers_amount = (z_scores_amount.abs() > threshold_amount)\n",
    "\n",
    "# Remove rows with outliers in the \"Amount\" column\n",
    "df = df[~outliers_amount]\n",
    "\n",
    "# Verify that remaining outliers have been removed\n",
    "print(outliers_amount.sum())\n",
    "print(df.shape)\n",
    "\n",
    "\n",
    "# Visualize the distribution of the \"Amount\" column (after removing remaining outliers)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(data=df, y='Amount', color='skyblue')\n",
    "plt.title(\"Distribution of Amount (Cleaned)\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4de3afWB0Ofi"
   },
   "source": [
    "# **Data Visualization**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "slJc9xTA5XRE"
   },
   "source": [
    "**1. Using Matplotlib and Seaborn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "5RPgcNDB4jy_",
    "outputId": "b2bee239-d769-4f97-f6fd-bd24261ea98f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of quantity\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Qty'], bins=20, kde=True, color='skyblue')\n",
    "plt.title(\"Distribution of Order Quantities\")\n",
    "plt.xlabel(\"Quantity\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "ZecxiZZ75nuR",
    "outputId": "d55bbb96-bfe9-4f90-e6ce-d675b992429c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of sales\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Amount'], bins=20, kde=True, color='skyblue')\n",
    "plt.title(\"Distribution of Amount\")\n",
    "plt.xlabel(\"Amount\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 738
    },
    "id": "fKc_VLol72vs",
    "outputId": "000711f6-b614-4cbf-c362-ecd437d0a626"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7976\\1736340730.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data=df, x='Category', palette='Set3')\n"
     ]
    },
    {
     "data": {
      "image/png": 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vvlC/fv2cUFGhQgW1bdtWb7/9tiIiIlSzZk2tWrUqUc+oJLVp00aDBg1SixYt9NRTT+nChQuaMmWKihcv7nUyXLFixTR06FC99NJLql27tlq2bKmAgAD98MMPypMnjzOXb+XKlTVlyhS9/PLLKlasmHLmzOnMA30t77//vho2bKiWLVuqWbNmql+/vjJkyKB9+/Zp7ty5Onr0qMaMGaPAwEDVqVNHo0ePVmxsrPLmzavly5c7c0tfrXLlypKkoUOHqk2bNkqTJo2aNWumokWL6uWXX9aQIUOcKd0yZcqk/fv3a/78+XriiSf0zDPPyN/fX88//7z69Omj++67T48++qgOHDigmTNnqmjRol69lk888YSmTZumxx9/XFu2bFGhQoX02Wefaf369ZowYcI/PilRktq1a6dnn31W8+fPV48ePZwx0v/E3r17NXv2bJmZIiMjnSvunT9/XuPGjdMDDzzwl/etX7++Hn30UZUuXVqpU6fW/PnzdezYMbVp08Zrv17P7/avFC9eXF27dtUPP/yg4OBgTZ8+XceOHdOMGTOcNg0bNlSBAgXUtWtXDRw4UKlSpdL06dOVI0cOhYWFeW3vn9aWJk0avf766+rcubPq1q2rtm3bOlPAFSpUSP3797+h5wPg//lsXg0AyVrC1FU//PDDX7br1KmTZciQ4Zrrzp07Z/3797c8efJYmjRp7K677rI33njDaxo2M7OLFy/aU089ZdmyZbMMGTJYs2bN7ODBg4mmgDMzW758uZUtW9b8/f2tRIkSNnv27D+dfmv69OlWsWJFCwgIsCxZsljdunVtxYoVzvrw8HBr0qSJZcqUKdF0aX/mwoULNmbMGKtataplzJjR/P397a677rI+ffo409qZmR06dMhatGhhmTNntqCgIHvkkUfsyJEj13xOL730kuXNm9f8/PwSTRf2+eefW61atSxDhgyWIUMGK1mypPXq1cv27NnjtY2JEydawYIFLSAgwKpVq2br16+3ypUr2wMPPODV7tixY9a5c2fLnj27+fv729133+1Mr5YgYdq1v5tmrXHjxibJNmzY8Lf7LYEk5+bn52eZM2e2ihUrWt++fW3Xrl2J2rungDt58qT16tXLSpYsaRkyZLCgoCCrXr26ffLJJ173+7Pf7V+9rv9sCrgmTZrYsmXLrFy5chYQEGAlS5a0Tz/9NNH9t2zZYtWrVzd/f38rUKCAjRs37prb/LPa3FPAJfj444+d13HWrFmtffv2zhSMCf7sffhn7w0AZh4z13E6AECKFx8frxw5cqhly5Z69913b8ljtGjRQjt27Lhmrz8AJHWMSQaAFO7SpUuJxi2///77On369F9eavvfOHr0qBYtWnRTTtgDAF+gJxkAUrg1a9aof//+euSRR5QtWzZt3bpV//3vf1WqVClt2bJF/v7+N+2x9u/fr/Xr1+u9997TDz/8oN9+++2WzRMMALcSJ+4BQApXqFAh5c+fXxMnTtTp06eVNWtWdezYUa+99tpNDciStHbtWnXu3FkFChTQrFmzCMgAki16kgEAAAAXxiQDAAAALoRkAAAAwIUxyTdJfHy8jhw5okyZMt3Uy50CAADg5jAznTt3Tnny5JGf31/3FROSb5IjR44of/78vi4DAAAAf+PgwYPKly/fX7YhJN8kCZdrPXjwoAIDA31cDQAAANwiIyOVP39+J7f9FULyTZIwxCIwMJCQDAAAkIT9k6GxnLgHAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFx8GpKff/55eTwer1vJkiWd9ZcuXVKvXr2ULVs2ZcyYUa1atdKxY8e8thEWFqYmTZooffr0ypkzpwYOHKjLly97tVmzZo0qVaqkgIAAFStWTDNnzkxUy+TJk1WoUCGlTZtW1atX1/fff39LnjMAAACSPp/3JJcpU0ZHjx51bt9++62zrn///vrqq6/06aefau3atTpy5IhatmzprI+Li1OTJk0UExOjDRs2aNasWZo5c6ZGjBjhtNm/f7+aNGmievXqadu2berXr5+6deumZcuWOW0+/vhjDRgwQCNHjtTWrVtVvnx5hYaG6vjx47dnJwAAACBJ8ZiZ+erBn3/+eS1YsEDbtm1LtC4iIkI5cuTQhx9+qIcffliStHv3bpUqVUobN25UjRo1tGTJEjVt2lRHjhxRcHCwJGnq1KkaNGiQTpw4IX9/fw0aNEiLFi3Szp07nW23adNGZ8+e1dKlSyVJ1atXV9WqVTVp0iRJUnx8vPLnz68+ffpo8ODB/+i5REZGKigoSBEREQoMDPw3uwUAAAC3wPXkNZ/3JO/bt0958uRRkSJF1L59e4WFhUmStmzZotjYWDVo0MBpW7JkSRUoUEAbN26UJG3cuFF33323E5AlKTQ0VJGRkdq1a5fT5uptJLRJ2EZMTIy2bNni1cbPz08NGjRw2lxLdHS0IiMjvW4AAABIGXwakqtXr66ZM2dq6dKlmjJlivbv36/atWvr3LlzCg8Pl7+/vzJnzux1n+DgYIWHh0uSwsPDvQJywvqEdX/VJjIyUhcvXtTJkycVFxd3zTYJ27iWUaNGKSgoyLnlz5//hvYBAAAAkp7UvnzwRo0aOf8vV66cqlevroIFC+qTTz5RunTpfFjZ3xsyZIgGDBjg/BwZGUlQBgAASCF8PtziapkzZ1bx4sX166+/KleuXIqJidHZs2e92hw7dky5cuWSJOXKlSvRbBcJP/9dm8DAQKVLl07Zs2dXqlSprtkmYRvXEhAQoMDAQK8bAAAAUgaf9iS7nT9/Xr/99ps6dOigypUrK02aNFq1apVatWolSdqzZ4/CwsIUEhIiSQoJCdErr7yi48ePK2fOnJKkFStWKDAwUKVLl3baLF682OtxVqxY4WzD399flStX1qpVq9S8eXNJV07cW7VqlXr37n07nvYd6+LFVb4u4ZZJl66+r0sAAAD/gk97kp955hmtXbtWBw4c0IYNG9SiRQulSpVKbdu2VVBQkLp27aoBAwbo66+/1pYtW9S5c2eFhISoRo0akqSGDRuqdOnS6tChg7Zv365ly5Zp2LBh6tWrlwICAiRJTz75pH7//Xc9++yz2r17t95++2198skn6t+/v1PHgAED9O6772rWrFn65Zdf1KNHD0VFRalz584+2S8AAADwLZ/2JB86dEht27bVqVOnlCNHDtWqVUvfffedcuTIIUkaP368/Pz81KpVK0VHRys0NFRvv/22c/9UqVJp4cKF6tGjh0JCQpQhQwZ16tRJL774otOmcOHCWrRokfr3768333xT+fLl03vvvafQ0FCnTevWrXXixAmNGDFC4eHhqlChgpYuXZroZD4AAADcGXw6T3JKwjzJ14/hFgAA4HZKVvMkAwAAAEkNIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgkqYuJpGRTfljn6xJumR5V6/i6BAAAgJuKnmQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuSSYkv/baa/J4POrXr5+z7NKlS+rVq5eyZcumjBkzqlWrVjp27JjX/cLCwtSkSROlT59eOXPm1MCBA3X58mWvNmvWrFGlSpUUEBCgYsWKaebMmYkef/LkySpUqJDSpk2r6tWr6/vvv78VTxMAAADJQJIIyT/88IOmTZumcuXKeS3v37+/vvrqK3366adau3atjhw5opYtWzrr4+Li1KRJE8XExGjDhg2aNWuWZs6cqREjRjht9u/fryZNmqhevXratm2b+vXrp27dumnZsmVOm48//lgDBgzQyJEjtXXrVpUvX16hoaE6fvz4rX/yAAAASHJ8HpLPnz+v9u3b691331WWLFmc5REREfrvf/+rcePG6b777lPlypU1Y8YMbdiwQd99950kafny5fr55581e/ZsVahQQY0aNdJLL72kyZMnKyYmRpI0depUFS5cWGPHjlWpUqXUu3dvPfzwwxo/frzzWOPGjVP37t3VuXNnlS5dWlOnTlX69Ok1ffr027szAAAAkCT4PCT36tVLTZo0UYMGDbyWb9myRbGxsV7LS5YsqQIFCmjjxo2SpI0bN+ruu+9WcHCw0yY0NFSRkZHatWuX08a97dDQUGcbMTEx2rJli1cbPz8/NWjQwGlzLdHR0YqMjPS6AQAAIGVI7csHnzt3rrZu3aoffvgh0brw8HD5+/src+bMXsuDg4MVHh7utLk6ICesT1j3V20iIyN18eJFnTlzRnFxcddss3v37j+tfdSoUXrhhRf+2RMFAABAsuKznuSDBw+qb9++mjNnjtKmTeurMm7YkCFDFBER4dwOHjzo65IAAABwk/gsJG/ZskXHjx9XpUqVlDp1aqVOnVpr167VxIkTlTp1agUHBysmJkZnz571ut+xY8eUK1cuSVKuXLkSzXaR8PPftQkMDFS6dOmUPXt2pUqV6pptErZxLQEBAQoMDPS6AQAAIGXwWUiuX7++duzYoW3btjm3KlWqqH379s7/06RJo1WrVjn32bNnj8LCwhQSEiJJCgkJ0Y4dO7xmoVixYoUCAwNVunRpp83V20hok7ANf39/Va5c2atNfHy8Vq1a5bQBAADAncVnY5IzZcqksmXLei3LkCGDsmXL5izv2rWrBgwYoKxZsyowMFB9+vRRSEiIatSoIUlq2LChSpcurQ4dOmj06NEKDw/XsGHD1KtXLwUEBEiSnnzySU2aNEnPPvusunTpotWrV+uTTz7RokWLnMcdMGCAOnXqpCpVqqhatWqaMGGCoqKi1Llz59u0NwAAAJCU+PTEvb8zfvx4+fn5qVWrVoqOjlZoaKjefvttZ32qVKm0cOFC9ejRQyEhIcqQIYM6deqkF1980WlTuHBhLVq0SP3799ebb76pfPny6b333lNoaKjTpnXr1jpx4oRGjBih8PBwVahQQUuXLk10Mh8AAADuDB4zM18XkRJERkYqKChIERER1xyfPOWHdT6o6vboUbXODd3v4sVVf98omUqXrr6vSwAAAC5/l9eu5vN5kgEAAICkhpAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcfBqSp0yZonLlyikwMFCBgYEKCQnRkiVLnPWXLl1Sr169lC1bNmXMmFGtWrXSsWPHvLYRFhamJk2aKH369MqZM6cGDhyoy5cve7VZs2aNKlWqpICAABUrVkwzZ85MVMvkyZNVqFAhpU2bVtWrV9f3339/S54zAAAAkj6fhuR8+fLptdde05YtW7R582bdd999euihh7Rr1y5JUv/+/fXVV1/p008/1dq1a3XkyBG1bNnSuX9cXJyaNGmimJgYbdiwQbNmzdLMmTM1YsQIp83+/fvVpEkT1atXT9u2bVO/fv3UrVs3LVu2zGnz8ccfa8CAARo5cqS2bt2q8uXLKzQ0VMePH799OwMAAABJhsfMzNdFXC1r1qx644039PDDDytHjhz68MMP9fDDD0uSdu/erVKlSmnjxo2qUaOGlixZoqZNm+rIkSMKDg6WJE2dOlWDBg3SiRMn5O/vr0GDBmnRokXauXOn8xht2rTR2bNntXTpUklS9erVVbVqVU2aNEmSFB8fr/z586tPnz4aPHjwP6o7MjJSQUFBioiIUGBgYKL1U35Y96/2S1LWo2qdG7rfxYurbnIlSUe6dPV9XQIAAHD5u7x2tSQzJjkuLk5z585VVFSUQkJCtGXLFsXGxqpBgwZOm5IlS6pAgQLauHGjJGnjxo26++67nYAsSaGhoYqMjHR6ozdu3Oi1jYQ2CduIiYnRli1bvNr4+fmpQYMGTptriY6OVmRkpNcNAAAAKYPPQ/KOHTuUMWNGBQQE6Mknn9T8+fNVunRphYeHy9/fX5kzZ/ZqHxwcrPDwcElSeHi4V0BOWJ+w7q/aREZG6uLFizp58qTi4uKu2SZhG9cyatQoBQUFObf8+fPf0PMHAABA0uPzkFyiRAlt27ZNmzZtUo8ePdSpUyf9/PPPvi7rbw0ZMkQRERHO7eDBg74uCQAAADdJal8X4O/vr2LFikmSKleurB9++EFvvvmmWrdurZiYGJ09e9arN/nYsWPKlSuXJClXrlyJZqFImP3i6jbuGTGOHTumwMBApUuXTqlSpVKqVKmu2SZhG9cSEBCggICAG3vSAAAASNJ83pPsFh8fr+joaFWuXFlp0qTRqlX/O7lrz549CgsLU0hIiCQpJCREO3bs8JqFYsWKFQoMDFTp0qWdNldvI6FNwjb8/f1VuXJlrzbx8fFatWqV0wYAAAB3Fp/2JA8ZMkSNGjVSgQIFdO7cOX344Ydas2aNli1bpqCgIHXt2lUDBgxQ1qxZFRgYqD59+igkJEQ1atSQJDVs2FClS5dWhw4dNHr0aIWHh2vYsGHq1auX08v75JNPatKkSXr22WfVpUsXrV69Wp988okWLVrk1DFgwAB16tRJVapUUbVq1TRhwgRFRUWpc+fOPtkvAAAA8C2fhuTjx4+rY8eOOnr0qIKCglSuXDktW7ZM999/vyRp/Pjx8vPzU6tWrRQdHa3Q0FC9/fbbzv1TpUqlhQsXqkePHgoJCVGGDBnUqVMnvfjii06bwoULa9GiRerfv7/efPNN5cuXT++9955CQ0OdNq1bt9aJEyc0YsQIhYeHq0KFClq6dGmik/kAAABwZ0hy8yQnV8yTfP2YJxkAANxOyXKeZAAAACCpICQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuNxSSixQpolOnTiVafvbsWRUpUuRfFwUAAAD40g2F5AMHDiguLi7R8ujoaB0+fPhfFwUAAAD4Uurrafzll186/1+2bJmCgoKcn+Pi4rRq1SoVKlTophUHAAAA+MJ1heTmzZtLkjwejzp16uS1Lk2aNCpUqJDGjh1704oDAAAAfOG6QnJ8fLwkqXDhwvrhhx+UPXv2W1IUAAAA4EvXFZIT7N+//2bXAQAAACQZNxSSJWnVqlVatWqVjh8/7vQwJ5g+ffq/LgwAAADwlRsKyS+88IJefPFFValSRblz55bH47nZdQEAAAA+c0MheerUqZo5c6Y6dOhws+sBAAAAfO6G5kmOiYlRzZo1b3YtAAAAQJJwQyG5W7du+vDDD292LQAAAECScEPDLS5duqR33nlHK1euVLly5ZQmTRqv9ePGjbspxQEAAAC+cEMh+aefflKFChUkSTt37vRax0l8AAAASO5uKCR//fXXN7sOAAAAIMm4oTHJAAAAQEp2Qz3J9erV+8thFatXr77hggAAAABfu6GQnDAeOUFsbKy2bdumnTt3qlOnTjejLgAAAMBnbigkjx8//prLn3/+eZ0/f/5fFQQAAAD42k0dk/zYY49p+vTpN3OTAAAAwG13U0Pyxo0blTZt2pu5SQAAAOC2u6HhFi1btvT62cx09OhRbd68WcOHD78phQEAAAC+ckMhOSgoyOtnPz8/lShRQi+++KIaNmx4UwoDAAAAfOWGQvKMGTNudh0AAABAknFDITnBli1b9Msvv0iSypQpo4oVK96UogAAAABfuqGQfPz4cbVp00Zr1qxR5syZJUlnz55VvXr1NHfuXOXIkeNm1ggAAADcVjc0u0WfPn107tw57dq1S6dPn9bp06e1c+dORUZG6qmnnrrZNQIAAAC31Q31JC9dulQrV65UqVKlnGWlS5fW5MmTOXEPAAAAyd4N9STHx8crTZo0iZanSZNG8fHx/7ooAAAAwJduKCTfd9996tu3r44cOeIsO3z4sPr376/69evftOIAAAAAX7ihkDxp0iRFRkaqUKFCKlq0qIoWLarChQsrMjJSb7311s2uEQAAALitbmhMcv78+bV161atXLlSu3fvliSVKlVKDRo0uKnFAQAAAL5wXT3Jq1evVunSpRUZGSmPx6P7779fffr0UZ8+fVS1alWVKVNG33zzza2qFQAAALgtriskT5gwQd27d1dgYGCidUFBQfrPf/6jcePG3bTiAAAAAF+4rpC8fft2PfDAA3+6vmHDhtqyZcu/LgoAAADwpesKyceOHbvm1G8JUqdOrRMnTvzrogAAAABfuq6QnDdvXu3cufNP1//000/KnTv3vy4KAAAA8KXrCsmNGzfW8OHDdenSpUTrLl68qJEjR6pp06Y3rTgAAADAF65rCrhhw4Zp3rx5Kl68uHr37q0SJUpIknbv3q3JkycrLi5OQ4cOvSWFAgAAALfLdYXk4OBgbdiwQT169NCQIUNkZpIkj8ej0NBQTZ48WcHBwbekUAAAAOB2ue6LiRQsWFCLFy/WmTNn9Ouvv8rMdNdddylLliy3oj4AAADgtruhK+5JUpYsWVS1atWbWQsAAACQJFzXiXsAAADAnYCQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXn4bkUaNGqWrVqsqUKZNy5syp5s2ba8+ePV5tLl26pF69eilbtmzKmDGjWrVqpWPHjnm1CQsLU5MmTZQ+fXrlzJlTAwcO1OXLl73arFmzRpUqVVJAQICKFSummTNnJqpn8uTJKlSokNKmTavq1avr+++/v+nPGQAAAEmfT0Py2rVr1atXL3333XdasWKFYmNj1bBhQ0VFRTlt+vfvr6+++kqffvqp1q5dqyNHjqhly5bO+ri4ODVp0kQxMTHasGGDZs2apZkzZ2rEiBFOm/3796tJkyaqV6+etm3bpn79+qlbt25atmyZ0+bjjz/WgAEDNHLkSG3dulXly5dXaGiojh8/fnt2BgAAAJIMj5mZr4tIcOLECeXMmVNr165VnTp1FBERoRw5cujDDz/Uww8/LEnavXu3SpUqpY0bN6pGjRpasmSJmjZtqiNHjig4OFiSNHXqVA0aNEgnTpyQv7+/Bg0apEWLFmnnzp3OY7Vp00Znz57V0qVLJUnVq1dX1apVNWnSJElSfHy88ufPrz59+mjw4MF/W3tkZKSCgoIUERGhwMDAROun/LDuX++fpKpH1To3dL+LF1fd5EqSjnTp6vu6BAAA4PJ3ee1qSWpMckREhCQpa9askqQtW7YoNjZWDRo0cNqULFlSBQoU0MaNGyVJGzdu1N133+0EZEkKDQ1VZGSkdu3a5bS5ehsJbRK2ERMToy1btni18fPzU4MGDZw2btHR0YqMjPS6AQAAIGVIMiE5Pj5e/fr10z333KOyZctKksLDw+Xv76/MmTN7tQ0ODlZ4eLjT5uqAnLA+Yd1ftYmMjNTFixd18uRJxcXFXbNNwjbcRo0apaCgIOeWP3/+G3viAAAASHKSTEju1auXdu7cqblz5/q6lH9kyJAhioiIcG4HDx70dUkAAAC4SVL7ugBJ6t27txYuXKh169YpX758zvJcuXIpJiZGZ8+e9epNPnbsmHLlyuW0cc9CkTD7xdVt3DNiHDt2TIGBgUqXLp1SpUqlVKlSXbNNwjbcAgICFBAQcGNPGAAAAEmaT3uSzUy9e/fW/PnztXr1ahUuXNhrfeXKlZUmTRqtWvW/E7z27NmjsLAwhYSESJJCQkK0Y8cOr1koVqxYocDAQJUuXdppc/U2EtokbMPf31+VK1f2ahMfH69Vq1Y5bQAAAHDn8GlPcq9evfThhx/qiy++UKZMmZzxv0FBQUqXLp2CgoLUtWtXDRgwQFmzZlVgYKD69OmjkJAQ1ahRQ5LUsGFDlS5dWh06dNDo0aMVHh6uYcOGqVevXk5P75NPPqlJkybp2WefVZcuXbR69Wp98sknWrRokVPLgAED1KlTJ1WpUkXVqlXThAkTFBUVpc6dO9/+HQMAAACf8mlInjJliiTp3nvv9Vo+Y8YMPf7445Kk8ePHy8/PT61atVJ0dLRCQ0P19ttvO21TpUqlhQsXqkePHgoJCVGGDBnUqVMnvfjii06bwoULa9GiRerfv7/efPNN5cuXT++9955CQ0OdNq1bt9aJEyc0YsQIhYeHq0KFClq6dGmik/kAAACQ8iWpeZKTM+ZJvn7MkwwAAG6nZDtPMgAAAJAUEJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALik9nUBAP5n3de/+LqEW6ZOvVK+LgEAgH+MnmQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALik9nUBAPBXTox/2dcl3DI5+g/zdQkAgD/h057kdevWqVmzZsqTJ488Ho8WLFjgtd7MNGLECOXOnVvp0qVTgwYNtG/fPq82p0+fVvv27RUYGKjMmTOra9euOn/+vFebn376SbVr11batGmVP39+jR49OlEtn376qUqWLKm0adPq7rvv1uLFi2/68wUAAEDy4NOQHBUVpfLly2vy5MnXXD969GhNnDhRU6dO1aZNm5QhQwaFhobq0qVLTpv27dtr165dWrFihRYuXKh169bpiSeecNZHRkaqYcOGKliwoLZs2aI33nhDzz//vN555x2nzYYNG9S2bVt17dpVP/74o5o3b67mzZtr586dt+7JAwAAIMny6XCLRo0aqVGjRtdcZ2aaMGGChg0bpoceekiS9P777ys4OFgLFixQmzZt9Msvv2jp0qX64YcfVKVKFUnSW2+9pcaNG2vMmDHKkyeP5syZo5iYGE2fPl3+/v4qU6aMtm3bpnHjxjlh+s0339QDDzyggQMHSpJeeuklrVixQpMmTdLUqVNvw54AAABAUpJkT9zbv3+/wsPD1aBBA2dZUFCQqlevro0bN0qSNm7cqMyZMzsBWZIaNGggPz8/bdq0yWlTp04d+fv7O21CQ0O1Z88enTlzxmlz9eMktEl4nGuJjo5WZGSk1w0AAAApQ5INyeHh4ZKk4OBgr+XBwcHOuvDwcOXMmdNrferUqZU1a1avNtfaxtWP8WdtEtZfy6hRoxQUFOTc8ufPf71PEQAAAElUkg3JSd2QIUMUERHh3A4ePOjrkgAAAHCTJNmQnCtXLknSsWPHvJYfO3bMWZcrVy4dP37ca/3ly5d1+vRprzbX2sbVj/FnbRLWX0tAQIACAwO9bgAAAEgZkmxILly4sHLlyqVVq1Y5yyIjI7Vp0yaFhIRIkkJCQnT27Flt2bLFabN69WrFx8erevXqTpt169YpNjbWabNixQqVKFFCWbJkcdpc/TgJbRIeBwAAAHcWn4bk8+fPa9u2bdq2bZukKyfrbdu2TWFhYfJ4POrXr59efvllffnll9qxY4c6duyoPHnyqHnz5pKkUqVK6YEHHlD37t31/fffa/369erdu7fatGmjPHnySJLatWsnf39/de3aVbt27dLHH3+sN998UwMGDHDq6Nu3r5YuXaqxY8dq9+7dev7557V582b17t37du8SAAAAJAE+nQJu8+bNqlevnvNzQnDt1KmTZs6cqWeffVZRUVF64okndPbsWdWqVUtLly5V2rRpnfvMmTNHvXv3Vv369eXn56dWrVpp4sSJzvqgoCAtX75cvXr1UuXKlZU9e3aNGDHCay7lmjVr6sMPP9SwYcP03HPP6a677tKCBQtUtmzZ27AXAAAAkNR4zMx8XURKEBkZqaCgIEVERFxzfPKUH9b5oKrbo0fVOjd0v4sXV/19o2QqXbr6N3S/dV//cpMrSTrq1Ct1Q/fjstQAgJvl7/La1ZLsmGQAAADAVwjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHAhJAMAAAAuhGQAAADAhZAMAAAAuBCSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwAAAC4EJIBAAAAF0IyAAAA4EJIBgAAAFwIyQAAAIALIRkAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgktrXBQAArs8bC773dQm3zMDm1XxdAgBIoicZAAAASISeZAAAkOJd3HnE1yXcMunK5vF1CSkSPckAAACACyEZAAAAcCEkAwAAAC6MSXaZPHmy3njjDYWHh6t8+fJ66623VK0aZ1sDQFJ1YuGzvi7hlsrRdLSvSwDuSPQkX+Xjjz/WgAEDNHLkSG3dulXly5dXaGiojh8/7uvSAAAAcBsRkq8ybtw4de/eXZ07d1bp0qU1depUpU+fXtOnT/d1aQAAALiNGG7x/2JiYrRlyxYNGTLEWebn56cGDRpo48aNidpHR0crOjra+TkiIkKSFBkZec3tXzwfdZMrTjr+7Dn/nYsXU+4+iY29sX0SFXX+JleSdNzo6+TcpUs3uZKkI+AG98mlC7xOrnbuQvTfN0rGbuR1svCn5296HUlF03LP39D9Lp4/d3MLSUJib/Cz5E6U8BljZn/blpD8/06ePKm4uDgFBwd7LQ8ODtbu3bsTtR81apReeOGFRMvz589/y2pMqp72dQFAcvXcK76uIMkZ4esCkqSJvi4giRnv6wKQApw7d05BQUF/2YaQfIOGDBmiAQMGOD/Hx8fr9OnTypYtmzwejw8ru/ItKX/+/Dp48KACAwN9WktSwP5IjH2SGPskMfZJYuyTxNgnibFPEksq+8TMdO7cOeXJ8/cXYCEk/7/s2bMrVapUOnbsmNfyY8eOKVeuXInaBwQEKCAgwGtZ5syZb2WJ1y0wMJA351XYH4mxTxJjnyTGPkmMfZIY+yQx9kliSWGf/F0PcgJO3Pt//v7+qly5slatWuUsi4+P16pVqxQSEuLDygAAAHC70ZN8lQEDBqhTp06qUqWKqlWrpgkTJigqKkqdO3f2dWkAAAC4jQjJV2ndurVOnDihESNGKDw8XBUqVNDSpUsTncyX1AUEBGjkyJGJhoPcqdgfibFPEmOfJMY+SYx9khj7JDH2SWLJcZ947J/MgQEAAADcQRiTDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshOQUzM8XHx/u6DAD4W3xWAUhqCMkp1Llz5+TxeOTn56etW7dq7969vi4JUMKMkwcOHFBUVJSPq7k2wtrtk/B6+O677/TJJ58oIiLCxxUBwP8QklOg8PBwNWzYUMuXL9fixYtVtWpVnTlzxtdl4Q5nZvJ4PPryyy8VGhqqefPm6cKFC74uy0t8fLz8/K58LO7fv1+//PKLsxw3V8LrYd68eWrcuLF+++03HT161FmHlCPh93ny5EmdO3dOx44d83FFSC6u/izwxecCV9xLgU6cOKGyZcvqiSeeUHh4uObOnavq1at7BQD8uYQ/3ri5EgJyu3bt9PLLL6tmzZpKnz69r8vykvD+GDx4sObOnavTp0+rWLFiatu2rbp166YsWbL4uMKUw+PxaM2aNerSpYveeOMNde3a1dn/sbGx8vf3T/afWQmfJfv27ZPH41Hq1KlVqFAhX5d1WyXsg6+++kpjx47V2bNnFR8frwEDBujxxx/3dXk+l7B/YmJiFBMTo4wZMyZad6e6+v1/8eJFxcbGKjAw8LbWkHw/ffCn7r77bt17770KCwtT1qxZlSFDBklXAgA9NP+TsC82b96sqVOnatq0adq5c6c8Hg/76RY4c+aMRo0apSFDhqhfv37Knz+/IiIi9PHHH2vLli2KiYnxWW1X9xTPnj1bH3zwgcaOHatVq1apSpUqmjdvnl566SWGA9wkCe+vr776Svfff7+6d++uCxcu6JtvvtGTTz6pxx9/XFu2bEn2n1kJPeW1a9fWfffdp0ceeUTvv/++r8u6rTwejxYvXqxHH31ULVq00NSpU/XAAw+oS5cu+u6773xdnk8lhOBFixapdevWqlixonr27Kk5c+ZIEgH5/wPyG2+8oWbNmikkJES9e/fWiRMnblsd9CSnMHFxcUqVKpUKFiyod955R1u3btXTTz+tqKgoPfLII04AvJPffJL3od7evXsrf/78ypAhg4YMGaLPP/9c9erVYz/dZJcuXdLZs2dVsmRJHT16VFOmTNG6dev03XffqUyZMurdu7c6d+7sk9oSPowXLFigEydOaNCgQWrVqpUkqWrVqnr11Vf1ySefqFatWmrZsqVPakxJ4uPjlSpVKmXMmFFHjhzR3LlzNX/+fJ0/f15nzpxR9uzZ1bJlS23evFk5cuTwdbk37NixY3rhhRf02muvKUuWLPrmm2/Ut29fRUVFqUePHr4u77aIi4vTRx99pMGDB6tv374KCwvTvHnz1L17d9WoUcPX5fmUx+PRwoUL1bp1az377LPq0qWLJk2apMWLF6tIkSIKCQnxdYk+k/CZPHToUM2YMUP9+/dXqVKl1Lp1a509e1ZDhgxRmTJlbn0hhhQhPj7ezMzOnDljkZGRzvI9e/ZY586drWTJkvbZZ585y5csWWJ79+697XUmJevWrbMcOXLYO++8Y2ZmW7ZsMY/HY2nSpLH58+eb2f/2K65fwr7bv3+/s6xVq1aWPXt2y5o1q7Vs2dImT55sERERds8991ivXr18VOkVJ06csAwZMpjH47F+/folWn/vvffaQw89dPsLSyESXg/fffedLVq0yKKjo23NmjXWpEkTCw4Otg4dOtiSJUvMzOzLL7+0e+65x86cOePDim9MwvOMi4uzY8eOWbdu3ezixYtmZnb48GEbPny4ZcqUyd5++21flnnbXLhwwUqWLGnz58+3s2fPWt68ee2JJ55w9tOUKVNs+/btPq7y9ouPj7czZ87Y/fffb2+88YaZmUVFRVlwcPA1P3/uRAsXLrQSJUrYt99+a2ZX/mYHBARY+vTprV69erZr165bXgMhOQVI+LD58ssvrWbNmlaiRAmrWrWqTZs2zc6fP28///yzdevWzUqUKGETJ060kSNHWtq0aS0sLMzHld8+8fHxXoE3NjbWXnzxRRs2bJiZmR08eNAKFChgXbp0se7du1vq1Klt+fLlzn1xfRL22RdffGHFixe39957z1n3/vvv24cffmhRUVEWGxtrZmadOnWyp59+2uLi4m7b/r7W4+zatcvKlCljFSpU8Ar3ZmYjR4600NBQi4mJuS31pSQJ+/rzzz+3LFmy2Msvv2y//fabmZkdP37cfv/9d6/2gwYNspo1a9rZs2dve63/RsLzXLRokT366KP22GOPWZUqVezChQtOmyNHjtjw4cMta9asNnbsWF+Vels99dRT9sQTT1jevHntySefdN73586ds3bt2tn48eMtLi7Ox1XeftHR0RYSEmLbt2+3AwcOWJ48eax79+7O+qVLl9ovv/ziwwpvr8uXL3v9/PXXX9ukSZPM7Mq+yJIli82ePdt+/fVXy5Ahg7Vp08Z+/PHHW1oTITmFWLJkiaVPn95GjRplv//+u7Vt29YyZ85sK1asMDOznTt3Wv/+/a1AgQJWrlw527x5s48rvj0SPnivDja//fabRUdH286dO23Dhg127tw5CwkJsSeeeMLMrvR0eTwe83g89tVXX/mk7pRgwYIFlj59epswYcKffuM/efKkDR061DJnznxb/xhc/WEcGxvr/NE2u/JeyZMnj9WvX9927txp58+ft6ioKKtRo4a1a9futtWY0qxatcoCAwPtvffes0uXLjnLr97333//vfXv39+CgoJs27Ztvijzhlz9hWvVqlWWIUMGa9mypTVq1Mg8Ho/TU5jg6NGjNmDAAMufP7+dOXMmxXwRT3gekZGRduLECWf5+++/b9myZbNatWrZyZMnnbbPPfecFSlSJNGXpJTq6qMMZlc+/8qXL2+vv/66FStWzLp16+Z8Nh08eNBat25t8+bN81m9t9Pu3bud/7/55pu2evVqu3jxoh08eNAiIyOtTp069vLLL5uZ2enTp61MmTLm8Xisd+/et7QuQnIydfW37osXL9rDDz9sQ4YMMTOzU6dOWeHCha1Hjx5e94mNjbVjx47Z8ePHb2utvhYWFmbNmze3+Ph4+/LLL61AgQK2Z88eZ/3mzZutcuXKTpD75ZdfrE2bNjZ06FD7+eeffVV2snby5EmrVq2ajR492syufEk5d+6cffbZZ7Zv3z67dOmSrV271kJDQ61o0aK2devW21bb1cORxowZY23atLFKlSrZG2+8Yd9//72Zme3YscPy5ctnOXPmtNq1a9vDDz9sFStWtOjoaDPj6MKN6Nu3r7Vt29bMrhxW/u6776xnz57Wr18/W79+vZ09e9Y6duxodevWTbaH38PCwuzdd9+1CRMmmNmV98GYMWPMz8/PWZYgPDw8RX0WX330qG7dulawYEFr3Lix0xM4YsQIK1asmDVt2tR69epljz76qGXJkuWW9wQmFQn7Z82aNfbyyy87R0mmTJliHo/HGjRo4NV+6NChVrp0aTtw4MBtr/V2+/nnn83j8di0adNs4MCBliVLFq+/0YcOHbJSpUrZF198YWZmERER9tRTT9nPP/+cqPf5ZiMkJ0P79++3adOm2Q8//OAsu//++23dunV28uRJy507t9MramY2b94827Fjxx37hz1hGErFihUtTZo09tFHH3mtX7FihXk8Hmd/Dhs2zEJDQ+38+fO+KDdFCAsLs8KFC9vKlSstMjLSnn/+eatdu7alTp3aypQpY4sWLbLTp0/b9OnTncPut8P7779vL7zwgpldOaSfNWtWe/75561Dhw5WrVo1q1Wrlq1atcrMrvQoly1b1nLnzm0bNmxw3j8Mt7gxTz31lDVs2NC++OILe+yxx6xRo0ZWoUIFa9KkiVWrVs2io6Ntz549yTY4Hjx40DwejwUGBnoNo7hw4YKNGTPGPB6PTZw40YcV3nqLFy+29OnT22uvvWY7duyw9u3bW1BQkK1du9bMzN59913r0aOHNWjQwAYOHHjHdEIkfHZ89tlnljlzZhs8eLBt2bLFzK4MN+rbt695PB4bNmyYPf/88/bEE09YYGDgHfMFIjo62qZOnWpp0qSxoKAgZ6hbQmfgqVOnLDg42Nq1a2cffvihhYaGWrVq1Zz9eiuDMiE5mfnpp5+sePHi1qJFC1u0aJGz/IEHHrCHHnrIihYtaj169HD+kEdERFiLFi1s8uTJd1xIvvr5vvzyy+bxeKxMmTJOT2LCYd7o6Gh79NFHzePxWOXKlS1TpkzJ6lBvUvXggw9alixZLDg42Jo3b24TJkywS5cuWdmyZe2pp5667fVMnTrVPB6Pc9JqiRIlbPXq1c761atXW5s2baxBgwb266+/mtmVMcq5c+e2Bx54IEUdFr/Vrt5PV/egVa5c2XLkyGHt2rVzhjK9//77VrNmTYuKivJJrTfTjBkzLF26dNatWzevISUXLlywcePGmcfjsSlTpviwwlsjLi7OoqKirGXLljZy5EgzMzt79qzly5fP+vTpk2i88Z04/njDhg0WFBTknCieIC4uzi5evGhTpkyxypUrW506daxTp062c+dOH1XqG4sWLXKGOU6dOtVZnvB3ev369ZYrVy4rW7as3XvvvU7GudWfyYTkZOSXX36xLFmy2ODBg+3w4cNe61avXm133XWXlShRwmv50KFDrWjRore1ty6pWbNmjc2cOdOee+45Cw0NtXr16tnBgwfN7H9vwFOnTtmcOXNs4sSJtm/fPl+Wm+wkfEjt2LHDVq9ebR9//LGZXTmkPmPGDJs+fbpFREQ4+/qxxx6zESNG3NY/lO+//76lSZPG+WK5detWy5w5s61bt86r3ZIlS6xgwYK2Zs0aZ9nOnTutYMGCFhISYqdOnbptNSdXCa+HFStW2ODBgy00NNRmz55tx48ft4sXLzpjDxPaPfvss1a3bl2LiIjwWc034s/+OL/33nvm5+dnL730klcP14ULF2zSpEkpqvfU3ZPXsGFDW716tR06dMjy5MnjdUTzyy+/tE2bNvmkTl9K2EejR4+2xo0bm9mVWai+/PJLe+SRR6xq1arObEoJ74GEYV0p2ZYtW5we46FDh9qOHTssLCzMJk+ebB6PxxmedPXfiUuXLtnx48edfXr1+Qy3CiE5mbh48aI98sgjiabJiomJsaNHj9p3331nr732mpUtW9buu+8+Z/xf5syZb+t4z6Rmw4YN5vF4bN68eRYTE2Off/653XvvvVavXj07dOiQ027Lli1ePT/4Z66etaBQoUJWsWJFK168uJUsWdK++eYbr7anTp2y4cOHW5YsWW7rSXozZswwj8dj999/v7Nsx44dVrx4cfvggw+8noeZWfHixe2ll17y2sb27dutVKlS9scff9yeopO5efPmWcaMGa1Pnz7Wo0cPK1++vN13331eX+43bdpkAwcOtMDAwGR35Cbh9fLtt9/axIkTbejQofbtt98640ynTp16zaCcEr3//vvWtm1bO3v2rN17773WuXNnK1asmD3xxBNOiDl58qQ9+uij9t57790xR2PcQW769OmWOXNmmzRpkj3wwAPWpEkTe+SRR6xr166WKlUqr79HKX0f7d6920qXLm0DBgyw7t27m8fjcXrOrx6e9NZbbzn3GThwoFfnxe3qZCEkJxOxsbFWu3ZtrxfN0qVLrV+/fpYxY0YrU6aMVaxY0ZYtW2bt2rWzZs2aWd++fe+o6WPcdu3aZYsXL3bOiE3w+eefW7169axu3bq2c+dOGzlypBUpUsTrbGz8cxs2bLDMmTPb9OnTzcxs37595vF4nBN2zK70KjZv3twKFSp0W7+0vfPOO+bn52fdunWzPHnyWJ8+fZx1jz32mOXMmdPWr1/vLDt9+rRVqFDBZs6cmWhbd0Lvzs1w4MABu/vuu23atGlmduWIQsaMGW3w4MFOm4MHD1rz5s2tRo0ayfYkvc8++8wyZMhgjRs3tnz58lmZMmWsXbt2zpjqadOmWUBAgA0ePDjFBeWEEHfq1CkrWbKkM3vH6tWrLVu2bFahQgWv9kOHDrW77rrrjpnFIsH69ettzJgxFhERYYcOHbJ+/fpZ/vz5rVu3bvbNN99YfHy87d+/3ypVqnTHXbdgypQpFhwcbAEBAbZ48WIz+1/wvXjxohOUO3bsaLVr17aSJUvelp5jN0JyMhEREWElS5a07t272+7du+3VV1+1EiVKWKtWrWzChAn23nvvWcmSJZ15f81S/rfRv3LixAkLDAw0j8djTz/9tJl5f/P88ssvrV69epY9e3YrXLiwM6sBrt/UqVOtffv2Zma2d+9eK1SokNdhVrMrZydPmzbNGet7O4wfP948Ho/zATx16lTLnj2719GYJk2aWLZs2ax///726quv2v3332933323Tz6Mkyv3HOT79u2zsmXLWmRkpO3bt8/y5cvnNfdrwoUBfvvtNwsPD7/t9d4Mv/76qxUuXNhr7OSUKVOsXr161qFDB+e8h0mTJlm2bNlS5BfwlStX2tNPP23du3d3TnKOioqy8ePHW6pUqeyRRx6xHj16WIcOHe7YI5pPPvmkFSxY0N58803ngjLHjh3zajNo0CC7++67nanxUrqEL4zLli2zIkWKWIkSJeyZZ55J9LchLi7OPvnkE3vggQesW7duzhjk2/2Fk5CcjKxatcpSp05tBQsWtEyZMtnUqVOd8bMxMTHWsGFDe+yxx5z2d1pIdj/fL774wooVK2b169d3AvLV4ef48eO2fv16Z3wy/pmE/ZzwR2/gwIHWvHlzi4yMtPz583tdTeu///2vvfLKKz6pc82aNV4zmZw9e9amTZuWKCgPHjzYmjRpYjVr1rSOHTv67MM4uVuwYIF98803tm3bNrv77rtt27ZtVrhwYevWrZvz/tu6dat16dIl2Q2vMPP+fPn2228tT548tmPHDmdZdHS0vfXWW1amTBmv3vHkdkGUfyI6OtqGDRvmzFZztfPnz9vKlSutSZMm9tBDD93xRzT79OljZcqUsbFjx9rp06ed5evWrbP//Oc/ljVr1jtiFgv33+ezZ89aZGSkTZo0ySpUqGB9+vS5ZifK1RfioScZfyssLMw2b96cqGciLi7OHnnkEacn+U4LyAlWrlxpX375pZld2QcLFy60zJkze315IPz8e4sWLbKsWbPapk2bbOPGjVajRg0LDAx0epATQtFTTz1lbdq08el0ele/FyIiIq4ZlKOiopyeHjPffBgnRwn79scffzSPx+MMU2nYsKF5PJ5ERxQGDRpk1atXt6NHj972Wm/U1VeBTAh7O3futKJFi9rSpUudNmZX9keWLFlszJgxvin2Frv6vbR//3578cUXE01tl7Av3P/eKY4ePer1WWJm1rNnT7v77rtt3LhxFhERYceOHbPnn3/eGjdu7PVFK6W6+jVw7Ngx++OPP7yWjRkzxipUqGD9+/d3Jhl49NFHvc5r8VWmISSnAAnf6vPkyXPHjWty69Chg3k8Hlu4cKGzbOHChRYYGGgdO3Z0lt2pXyL+jYR9dvDgQevSpYtNnjzZzK4MpWjfvr3dddddNmPGDDO78kE4dOhQy5kzZ5I7mz8hKOfIkeOaU9Hx2rg+W7ZssQULFngdMdi+fbuFhIRY6dKl7ZtvvrF58+ZZ//79LVOmTMlqDPLevXvtP//5j5mZffzxx5Y7d2775Zdf7NKlS1apUiWrV6+eHTlyxGl/8eJFq127ts2ePdtXJd8SfzabQFhYmA0ZMsTSp0+faNquhPvcSe+nrVu3WpkyZWzWrFmJTgTv0qWLZc2a1SZOnGgXLlywiIgIO3PmjG8KvY2uDsPPP/+83XPPPZYxY0b7z3/+Y5999pmzbuzYsValShWrXbu21axZ03LlypUk5qQnJCdzH3zwgT311FMWHBx8R475upYuXbpYYGCg1yWlFy5caNmyZbMWLVr4sLLkb9OmTfbwww9b1apVvS5ms3PnTmvevLkVLlzY8uXLZzVq1LCCBQsm2ddkRESEvfPOO15TDeH6HTt2zEqWLGkej8f69evnLI+NjbUffvjBGjZsaHnz5rVSpUpZ/fr1k90wi/Xr15vH47H77rvPq6fc7MqXxTx58ljdunXts88+s++++84GDx5sWbNmTVFTbiaE3FWrVtnjjz9u7dq1s0GDBjnrDx48aEOHDrVMmTIlmgP4TnT//fdbxYoVbc6cOV49ynFxcZYnTx7Lly+fTZo06Y768mBmNnz4cMuRI4d9/PHHtnz5cqtVq5ZVrlzZOeHbzGz27Nk2dOhQ69Wrl/OFzNdH9QjJydju3bvt3nvvtRYtWiS53rrbIeFDJuEkmas/dDp16pQoKH/++edWoECBRHNM459bvXq1lS9f3tKkSWPvvfee17rDhw/bpk2bbPTo0bZ48eIkP13amTNnbMGCBQy/+Qf+7A/6hQsX7LPPPrMKFSpYpUqVrtlm9+7ddurUqWQ1D3J8fLzzuhg+fLh5PB675557nPUJvWOHDx+2mjVr2l133WUFChSwsmXLJtkvhjci4fc+b948CwwMtO7du9ugQYOsUKFC9uCDDzr76ODBgzZixAjzeDzO0aQ7wZ+9Lx588EErW7aszZkzx+lRDgsLs3bt2tkTTzxxx83ysXr1aitTpowzk9C6dessICDAatSoYZUqVfrTIy++DshmhORk79ixYynyxJC/c/U8pYUKFfLq1UzQrl07y5Ytmy1ZssRpz6Wm/7nz58/bmTNnbO3atbZ//36nV+S7776zmjVrWt26dZ0xmcldUvgwTqoSAmF4eLgtX77cBg4caMOGDbPVq1c7+23hwoWWL18+r7moE6bMS249Zldf+W///v326quv2jPPPGNBQUHWvn37a16xMywszLZv355iZrG4+hD5tm3brHjx4vb222+b2ZV9kjt3bvN4PFarVi1nPxw4cMBefvll52IxKV3C63rTpk02YcIEe/vtt23JkiXO+ubNm1v58uVt7Nix9tNPP9nIkSOtadOmKeLKktfrwIEDNnr0aIuLi7OlS5datmzZbPr06c7sN+XKlfOaMjQpISQj2Un4cPrqq6/shRdesBo1anjNv5vwAb9v3z5Lly6dpU2bNsWEudtlz5491r59eytVqpR5PB7Lnj273Xvvvc6E9998843VqlXLHnzwQVu+fLlzv+QWiPDXEt5Lu3btspCQEKtVq5YVLFjQihYtah6Px7p37+7MsPPVV1/ZXXfdZQ888IBz/+TWS3/w4EFr2bKl/fjjj7ZgwQJLlSqV8/zWrl1rgYGB1r59e6+gs3HjRl+Ve0vs37/fpk2b5kyLuXjxYuvfv7+ZXekNLVKkiHXv3t1WrVplGTNmtObNmztjR++0L5ufffaZBQYG2j333GNly5a11KlT28CBA531Xbt2tZIlS1quXLmsQIECtmXLFh9We3tc60TN2NhYi4iIsEuXLlmzZs1s5MiRzmdDo0aNrFSpUvbUU08lyb8fhGQkC4sWLXJO+El4Iz388MPOWdUNGjSw/Pnzex3q3Lt3r3Xv3t26det2R09BdL22b99uuXPntp49e9r7779vv/32m40YMcKKFi1q+fPnd3qKvv76a6tVq5a1bNnS60RJpAwJf+y2bdtmmTNntmeeeca5KtaJEydswoQJ5vF4rE2bNnbo0CGLjY21L774wkqXLm0hISG+LP2GrV692ho2bGgVK1a0tGnT2pw5c8zsf/vim2++scyZM1vbtm1t165dNmLECCtQoECyne/Z7aeffrLixYtbixYtvN7T27Zts/j4eGvevLm1b9/e4uPj7fz581alShXzeDzWsGFDH1btG3v37rVcuXI5PeynTp2y2bNnW7p06bzGbG/bts2+/fZbryvqpVRXB+Tdu3fbzp07vU6+i4qKsjJlyjgX+IqKirJ27drZ3Llzk+yJnoRkJHnh4eFWuHBh69y5s+3atctZXqtWLWeqpfPnz1toaKjlzZvXlixZYvv27bMXXnjBmjZtmiTOkE0utm3bZhkyZLBBgwZ5feDFxMTYypUrrWzZslasWDHnkPO3335rZcuWtXbt2t2RhxFTup9++smCgoJs6NChZva/nuGEf9977z3zeDw2evRoM7syu8Onn35qVapUSfJj0v9MQvgvXbq0bdq0yVme8H7YtGmTBQUFWcmSJS1nzpy2efNmX5V6U/3yyy+WJUsWGzx48DXP2zh79qyVL1/e5s+fb2Zmly5dsm7dutmiRYtS/BjbGTNm2IEDB7yWbdiwwUqUKJEo/M6aNcvSpUvndQnlO83gwYMtd+7cliNHDsubN69NmDDBDhw4YLGxsfbII4/YAw88YMOHD7f777/fqlatmqSnCyQkI1nYsmWLVa1a1bp16+bMK9mwYUP74IMPzOzKt8/Y2Fhr0aKFpU2b1goXLmzZs2dPUSfR3GpHjhyx9OnTO3PbJhw6TQhEcXFx9tlnn1mWLFnshRdecL7xf/fdd7Z//36f1IxbIz4+3i5cuGA5cuSwggULeo3ld19hr0uXLpYjRw7ncswxMTF27ty5217zv5XwZXrOnDn26quvWvPmza1Bgwa2atUqp03Ce+HUqVP29ddfp5iTgC9evGiPPPKI19zhZlf2yaFDh2zv3r0WFRVllStXtubNm9v+/fvtmWeeseLFiyerOa9vRGRkpAUHB1ulSpW8Ljy1ZcsW8/Pzc8Lw1VNkFilSxOtCRind1eH2iy++sODgYPviiy/sxx9/tIEDB1qpUqVs0KBBdvHiRduwYYO1bt3aqlevbg899JDzvkuKAdmMkIxkZOvWrVapUiXr0qWL7dixw1q3bm0rV65M1G7evHn23XffJfrmj7/2ww8/WK1atax06dJOL2BCUE74AxAXF2fVqlWztm3b+qxO3D5fffWVBQQEWK9evRKFoYQ/arNnz7asWbMm297EhNe2+4jT0qVLrXHjxla/fn1bvXq1s3zNmjUp7qhJbGys1a5d29566y1n2dKlS61fv34WGBhoBQsWtIYNG9q8efOsaNGiljdv3kTD21KysLAwK126tFWrVs3CwsLM7Ep4btq0qT388MNeUxsmzKE9a9YsX5XrM9OnT7e3337b3njjDa/lY8eOtbx58zrzIkdGRtqlS5f+dP7tpMRjZiYgmfjxxx/1xBNPqEyZMvr888+VM2dOFSlSRB6PR5cuXZK/v7/y58+vadOmyd/f39flJjubN2/W4MGDdeDAAa1atUoFCxZUXFycUqVK5bRp1KiRgoKCNHfuXB9WilstPj5efn5+WrRokR588EE9+eSTGjFihIKDg73WT5w4Uf/973+1YcMGZciQwcdVXx8zk8fj0YoVKzRr1ixdunRJOXPm1KhRoxQUFKSvv/5aY8eO1aVLl9S9e3ft2bNHo0aN0oEDB5z9kBJERkaqevXqql27tp5++mnNmzdPs2bNUtmyZVWnTh1lzJhRY8aMUWhoqIYMGaJ9+/apaNGiypUrl69LvyUSXtvx8fEyM6VKlUqHDh1SgwYNFBQUpM8//1z58uXTJ598orfeekvZsmXTk08+qYIFC2rWrFmaMWOGNm3apEKFCvn6qdw2J0+eVPXq1bV//3717NlTkyZN0uXLl5U6dWpJ0iOPPKJDhw5p48aNzv6V5PX/pCi1rwsArkfFihU1bdo0Pf744ypatKjKlCmj0NBQnT17VqdPn1ZAQIAefPBBAvJ1SggLVapU0ahRo/Tcc8+pfv36Wr16tQoUKOB82B06dEhxcXFq2LCh1/2Q8iSEhCZNmujLL7/Ugw8+KI/Ho+HDhys4OFh+fn6Kjo7Wtm3bVLNmzWT5nvN4PPriiy/06KOP6vHHH5e/v79WrlypZcuW6cMPP1S9evUUHx+vd955R4MGDVJAQIC++eabFBWQJSkwMFCTJ09WaGioli9frtOnT+uNN95Q/fr1VaxYMcXExGju3Lk6ffq0cubMqZw5c/q65FsmIbTt3btXEydO1OHDh1WzZk0NHDhQK1euVIMGDdSiRQt9+eWXevTRR+Xn56ePPvpIjRs3VsmSJRUbG6ulS5feUQE5Pj5e2bNn1/z589WvXz8tXrxYhw8fVt68eZ2/EeXKlVNERITMzCsUJ+WALEkMt0Cy9OOPPzpjlBkPe3NcPc70+++/t/r161vRokW9hq0MHjzYypcv7xxyRMqXMKxi4cKF5ufnZz179nRmcxgxYoTly5fP9uzZ48sSb0hcXJydPn3aqlSp4pxtb3Zl3uP69etbkSJFnLHYR48etV9//TXFj78NCwuzzZs3J5rvOS4uzh5++GEbNmxYojHpKcnVM7rkyJHDmjdvbm3atLHUqVM7J6cePHjQSpQoYRUrVnQuSR4TE2M///yz7dq1yxmbf6d45ZVXbMSIEc686Dt37rRy5cpZ2bJlbc+ePXb69Gnncu2PPPKIj6u9foRkJFtbt261qlWrWuvWre/IKw7eCu6g3KBBAytatKidOnXKxo4daxkyZEh2lxbGv+cOys8884w9/fTTljZt2mQ1LjU+Pt7rBKHjx49bsWLFbMGCBWb2v3HJFy5csCJFitizzz7r3O9OFR0dbcOGDbM8efLY3r17fV3OLZPwuti+fbulS5fOnnvuOWd57969rW/fvs5Y9LCwMKtYsaJVrFjR62S+O9Ho0aOdGW6uDsoVKlSwLFmyWKVKlaxTp0529913O++v5PR+IiQjWfv++++tbt26zjd6/HN/9kHlDsqhoaHm8XgsTZo0KWa6K3hL+J2fOXPGuYyuW0KIWLRokXk8HvN4PMkqIO/Zs8d69+5tLVq0cKaONDMrUaKEPfnkk87PMTExzpzACTO93Kk++OADe+qppyw4ODhZ/a5vVFhYmGXPnj1Rj2fr1q2tfPnyVqJECQsNDbW5c+c6Qfmuu+66I+ZANvvzGSgmT55sHo/HXnvtNSco79ixwxo0aGCZMmWyn376yWmblE/Su5YkPhgE+GtVq1bV0qVLlTt3bl+XkmzY/5+rGx8f77U84WePx+O0qVq1qkaMGKGOHTtqy5Ytqly58u0tFrec/f+YwYULF6pz585au3atLl68mKhdwhjlxo0ba926ddq1a5cqVqzog4qv3/bt21WrVi0dOnRIAQEBGjJkiF577TVJUu/evbV+/XqNGzdOkpQmTRp5PB75+fkpICBAdqUzyZfl+8SePXv03//+VwcPHtTXX3+dbH7X/0ZcXJwKFy6s6OhorV+/XpL02muv6auvvtLDDz+sgQMH6o8//tCwYcMUFRWl+fPnK3PmzIqNjfVx5bdHwvjhXbt2eS3v2bOnJk6cqCFDhmj8+PGKjo5W2bJlNX78eBUqVEgdOnTQhQsXvLaRbPg0ogO4rRJ6DFesWGGdO3e2gQMH2ldffeWs/7OegosXL96W+uAbCxYssPTp09tLL730t4fUk9OhUrO/PnxudmW8cd++fa1y5crWoUMHe++99+w///mPZcqU6Y4fxnXs2DE7e/asr8u4rfbu3WsPPPCAPfjgg9atWzfLmTOnLVu2zFn/xx9/mMfjsSlTpphZ8usZ/be+/vpr83g8zjUKrjZ69GhLkyaNTZo0yS5cuGBmV4ZeVKpUyQoUKJAs508nJAN3mBUrVljq1KmtTZs2VqJECQsJCbGXXnrJWZ9UJ3XHrXH06FGrUKGCjR071mt5cgvD1/J3h8+LFy9urVu3tldeecXeeecdq1q1qlWqVMnq16/P2Ps72J49e+z++++3dOnSOUNz4uPjnYurlCtXzj799FMfV3l7HD161H766Sf74IMPbMeOHXbmzBl79dVXLSAgwGbPnu3V9pdffrHAwMBEIXrbtm12zz332G+//Xa7y//XmAIOuIPs379fmzZt0ptvvqmePXvq8OHDeuuttzRv3jzFxcVp5MiR8vPzSzQ3MlKG119/Xffff78qVarkLIuLi1NERISqVKniLLOrpvaLiopKdvMfJ3AfPr/nnnucw+dDhgxRrly5NGbMGP3888+aO3euunfvrgsXLsjj8ShdunS+Lh8+Urx4cU2ZMkU9e/bUqlWrVK1aNdWuXVtp0qTRtGnTdO7cOVWvXt3XZd5y8+bN03//+19t3bpVUVFRunz5su6//36NGDFC6dKlU6dOnSRJ7du3lyQFBASod+/eqlChglq0aOFsp3z58lq9enWynCaSnmQgBbu6NzDhRIoSJUp4HT48cuSIDR482CpWrGgvvviiL8rELZYwq0O1atXsl19+8Vq3e/duS506tS1cuNDMrlx6OeF1s337dluyZEmiq9ElJ393+PzAgQPm8Xhs8uTJPqwSSVHCayc0NNS2bt1qr7/+erKb0eVGvfPOO5YlSxYbM2aMrVy50s6cOWMvvPCClSxZ0kqUKGGbNm2yN954wzwej40cOdI+/fRTa9asmTVt2tTZRkoYikJIBlKwhLCzadMmmzVrlrVu3dqCgoJs5MiRXu2OHj1qQ4cOtUKFCtlrr73mg0pxK7mH0Kxbt842bdpkly9fNjOzNm3aWPXq1e3777/3atezZ09r3769M74wufq7w+fly5e/Yw6f4/rs3bvXmjZtajlz5rxjZvh55513zN/f3z7//PNE6z7++GMrV66c1a5d206dOmVvvfWW5cuXz0qWLGl16tRJ1l+or4WQDKRAV/cgL1++3Dwej23YsMH27NljnTt3tgoVKtjUqVO97nP48GF74YUX7Pfff7/d5eI2SXhdlC5d2goWLGg//PCDmZktW7bMQkNDrVKlSvbxxx/b/PnzrV+/fpY5c2av6ZuSs19//dUaNmxojRo1snXr1jnLhw8fboULF+YCOfhTu3fvtgcffNB27tzp61JuuYQT81544QUzM+fiMVf3Cr/11luWNm1a++yzz8zMbP/+/Xb48GHny3hK6EFO4DG7A+e2Ae4Q4eHh+vzzzxUZGakhQ4ZIkn799Ve9+uqr2r17tzp16qT//Oc/TnvGIt8ZYmNjVa1aNcXExGj27NmqWLGivv32W73//vv66KOPlD9/fmXOnFlTpkxR+fLlfV3uTbNv3z499dRTMjONGjVKK1as0MiRI7Vhw4Y7Yooz3LjY2FilSZPG12Xccvv27VPXrl2VNWtWPf3006pdu7azLuGS3ZJUrlw53XPPPZoyZYrX8qv/nxIQkoEUav/+/SpatKiyZs2q4cOHq2/fvs66ffv2adSoUfr111/VqlUrr3VIWez/T8I7e/as0qdPLz8/P6VOnVqxsbGqWLGi4uPjNWfOHCckHj58WOnTp1eqVKkUGBjo4+pvvn379mnAgAH6/vvvdebMGW3cuJH5v4GrXP1lctiwYapVq5ak/32WREZGqnLlyurYsaOGDx/u42pvrZQT9wFI+t/FQgoXLqzRo0fr9OnT2rdvn6Kjo502d911l5577jkFBwdr8eLFOnv2rI+qxa3m8Xj0xRdfqHnz5qpcubLGjh2rbdu2KU2aNPrxxx/l8Xj02GOPafPmzYqLi1PevHmVJUuWFBmQpSuv/TFjxqhGjRr68ccfCciAy1133aWJEyfK4/Ho5Zdfdi6skuD3339Xvnz5VKNGDUlK0RfboScZSCESvuXbVdN3SVem/RoyZIjeeust9erVy+s+v/32m9KnT88VC1OwzZs367777tOAAQN0+PBhbd68WSVLllSfPn1Us2ZNxcbGqmrVqjp58qS++uqrO2bYwZ1y+By4UVf3KA8dOlS1a9fW5cuX9dBDD8nPz09ffPFFihpacS2EZCAFSAjGa9eu1YoVK3T8+HGVL19enTt3Vvr06fXqq69q+PDhmjRpknr06OHrcnGb/P7775ozZ45Sp07tjEn/7LPP9NZbbyk4OFj9+vVTzZo1FRMTo7p162rOnDkqUqSIj6sGkFQkBGU/Pz8999xzGjdunHbv3u0cjUppY5DdUu4zA+4gHo9H8+bNU9OmTXX69GnFxsbqv//9rxo0aKDY2Fg999xzevXVV9W/f3+NGzfO1+XiNvjjjz/UunVrvf3224qJiXGWP/zww+rTp4/Cw8P11ltvae3atfL399fGjRsJyAC8XD30ol69etq1a5cTkC9fvpyiA7JESAZShLCwMA0fPlyvv/663n77bY0cOVIHDx5UpUqVnEPKgwYN0jPPPKNXX32VMch3gIIFC6pjx45Kly6d1q5dq3379jnrHn74YfXr108///yzZs6cqUuXLqXocYUAblzCOP4nn3xSO3fudAJy6tQp/6LNDLcAkqmEt67H49GWLVvUtm1b7dq1S0ePHlWtWrXUqFEjTZs2TZK0cuVK1a1bV2nSpNGpU6eULVs2X5aOm+zy5ctKlSqVPB5Poj9eb7/9tqZNm6Y6deqob9++KlasmLPuiy++UIUKFVSwYEFflA0gGbpTArJETzKQ7MTHxztjkH///XdJUubMmVWgQAFt2LDBCciTJ0+WJO3atUufffaZdu7cKUkE5BTk+++/lySlTp1aHo9Hy5cv12OPPabevXtrxowZkqSePXuqS5cuWr9+vcaPH69ff/3Vuf9DDz1EQAZwXe6UgCwRkoFkZe/evRo2bJg8Ho8++eQT1a9fX7/++quyZ8+u8PBw1atXTw0bNtS0adOcD7IZM2Zox44dyps3r4+rx8309ddfq2nTpnrjjTckSWvXrlWTJk2cqd3GjBmjp59+WpLUt29fPf744/rhhx/00ksvOV+uAAB/7s75OgCkAPv27dNrr72mXbt26auvvtKMGTOcw+fz589XzZo1dfLkSS1evFjp0qXTF198oRkzZuibb75Rzpw5fVw9bqaCBQuqU6dOmjlzpqQrRxMmTJigXr16KTw8XB999JEmTpyouLg4TZgwQU899ZQuXryoJUuWKH369L4tHgCSAcYkA8lM37599dZbbyk0NFRLliyR9L/LSW/evFmdO3dWVFSU/P39lTNnTr311lsp6tLC+N+Uf/v379e0adO0cOFCXbp0Sa+99poefvhhSdLJkyc1e/Zsvfnmm2rZsqXGjh0rSTpz5oyyZMniy/IBIFmgJxlIBq6+QEjevHnVtWtXffDBB+rbt69Gjx6tgIAAxcfHq0qVKtq4caNOnz6t+Ph4Zc2aNcVeOe1OkzAfacIXIknKkSOHRowYIUmaMmWKNm/e7ITk7Nmzq0OHDvLz89OwYcMUEBCgV199VZkzZ/bVUwCAZIWQDCRxCQF548aNOnr0qPr16yd/f381adJErVu3liSNGTPGmept3759d8xV0+4kfn5+2rNnjxYvXqz+/fvrk08+UZ8+fbR371716NFD8fHx+uijj5QjRw5nLHK2bNnUrl07+fv7q0GDBpLkdTVGAMCfIyQDSVhCQJ43b566d++ufv36qXTp0ipZsqSaN2+uuXPnqm3btjIzDRgwQLNmzdKcOXO0YcMGZc+e3dfl4ya4+ijCypUr9fTTT2vr1q366KOP9N577ykoKEhBQUHq3bu3/Pz89O6770qSE5SzZ8+uJ554IsVP+g8ANxtjkoEk7uuvv1bz5s01ZswYdenSxTnUHhMTI39/f3355Zdq2bKlSpUqpaNHj2rZsmWqXLmyj6vGzZAwxCI8PFy5cuWSJHXp0kWzZs1Sq1at9Mknn3i1/+OPPzRlyhQtWrRIrVu31rBhw3xRNgCkCHQtAEncl19+qUaNGql79+66ePGi1q9fr549e6pbt27atm2bHnzwQe3Zs0evv/66tm3bRkBOIRIC8rZt21S9enXnJE1Jat26tT777DONGjVKERERzvKCBQvqySefVN26dbVgwQKdPn3aF6UDQIrAcAsgicuYMaMOHz6sTz/9VPPmzVNERIROnz6trFmzqkWLFtq0aZOKFi2qokWL+rpU3CQJAXn79u0KCQlR//791ahRI0nS9OnTJUlVqlTRM888IzNT7969nRM006VLp7FjxyoiIkJZs2b12XMAgOSOnmQgCUkY/XT1KKj7779fGTJkUI8ePZQ6dWr16dNH3333nZ544gnly5dPAQEBvioXt8C1AvKrr77qrP/5558lSQMGDNCECRM0bNgwTZo0SQcOHNDLL7+sKlWq6PLly8yLDQD/Ej3JQBKRcILWypUrtXTpUu3atUsPPfSQmjVrpqVLl+q3337z6i3euHGjOKUg5fHz89Ovv/6qGjVq6JlnntFLL73kvDZeeeUVbdy4Ue+++65y586tp556Sn5+fho0aJA+++wzhYWFaenSpcqQIYOvnwYAJHuEZCCJ8Hg8mj9/vh577DH16tVLxYoV04cffqgpU6Zo2bJlTkDesmWL5syZo+nTp2vt2rUKCgryceW4meLj4zV9+nRlypRJ2bJlk3TltTFq1Ci98cYb+vjjj5U7d26nx7l37966++67FRERoXLlyqlQoUK+fQIAkEIQkgEfio6OVkBAgMxMR44c0UsvvaQxY8aoR48eOnXqlIoVK6YuXbo4MxscOHBAb775pg4cOKB169apXLlyPn4GuNkSgu+FCxc0d+5cpU2bVpGRkRo7dqw+/vhjhYaGOu0S1K1b11flAkCKxRRwgI8cOnRIoaGhWr58ufLmzau9e/eqadOmWr9+vaKiolS7dm01atRI77zzjiRp1apVqlu3rsLCwpQpUyblyJHDx88At1J4eLheeeUVrVixQr/99puWLVum++67T5cvX1bq1Ff6N0aOHKmjR486rxEAwM3DiXuAj5iZLl26pCFDhig2NlYBAQHKli2b9uzZo3r16qlRo0aaMmWKJGnXrl2aO3eufvzxRxUpUoSAfAfIlSuXhg0bptDQUJUuXVo//vijJHkF5NGjR+s///mPL8sEgBSLkAz4SJ48efSf//xH27dv1+rVq1WwYEEFBASoTp06ql+/vt555x3nwiHvv/++tm3bpvz58/u4atxOwcHBGjJkiOrUqaNPP/1Ur7/+uiTplVde0ejRo/Xtt98yLzYA3CIMtwBuozNnzihLlizOzxEREapTp46Cg4O1fPlyhYeHq1mzZoqOjtarr77qXDxk+vTp+vbbbxmDfIdKGHqxfft2RUdH66effiIgA8AtRk8ycJv89ttvKl68uJo3b67jx4/rwoULCgoK0rvvvqtvvvlGb7zxhnLlyqVPPvlEOXPm1NNPP60RI0Zo9+7d+uabbwjId7BcuXJp6NChKlasmE6fPq2NGzcSkAHgFqMnGbhN9u3bp2rVqikiIkL333+/GjdurPr166ts2bIaMGCA1q1bp7ffflvVqlWTJP3xxx/KmDGjAgIClDFjRh9Xj6TgxIkTio+PV3BwsK9LAYAUj5AM3EIJF4FImJFg4sSJOnDggNKnT69Tp05py5YtevHFF5UtWzZ16NBB7dq10/Dhw+XxeHxdOgAAdzSGWwC3UFRUlKT/zUhQvnx5/fLLL7rnnns0btw4dezYUW3bttW3336rwoULa/z48dq1a5cvSwYAACIkA7dMeHi4SpcuraFDhyosLEzSlYs+3HPPPerYsaNOnz6t3r1766uvvtLOnTuVKlUqRURE6LnnnlNcXByXnAYAwIcYbgHcImfPntXEiRM1btw4Va5cWc2aNVO/fv0kSY8//rgk6c0331RQUJCOHTumn3/+WWPHjtWoUaN09913+65wAABASAZutZ9//lkjR47Utm3blC9fPk2dOlU//fSTFi1apMcee0wNGjRw2iaMYQYAAL5FSAZug4Rpu4YPH66IiAg9+uijWrFihSpXrqxp06b5ujwAAOBCSAZus/79+2v37t3asWOHjhw5onfeeUfdunXzdVkAAOAqhGTgNrl6KMWaNWu0dOlSvf322/r+++9VsmRJH1cHAACuRkgGbiP3mOPIyEgFBgb6sCIAAHAthGQAAADAhXmSAQAAABdCMgAAAOBCSAYAAABcCMkAAACACyEZAAAAcCEkAwAAAC6EZAAAAMCFkAwASdjjjz+u5s2b+7oMALjjEJIB4Do9/vjj8ng88ng88vf3V7FixfTiiy/q8uXLvi7tb82cOVOZM2f+R21jYmI0evRolS9fXunTp1f27Nl1zz33aMaMGYqNjf1H2zhw4IA8Ho+2bdt240UDgA+k9nUBAJAcPfDAA5oxY4aio6O1ePFi9erVS2nSpNGQIUMStY2JiZG/v78PqrxxMTExCg0N1fbt2/XSSy/pnnvuUWBgoL777juNGTNGFStWVIUKFXxd5nVLjr8LAL5BTzIA3ICAgADlypVLBQsWVI8ePdSgQQN9+eWXkv43ROKVV15Rnjx5VKJECUnSjh07dN999yldunTKli2bnnjiCZ0/f97ZZlxcnAYMGKDMmTMrW7ZsevbZZ2VmXo9bqFAhTZgwwWtZhQoV9Pzzzzs/nz17Vv/5z38UHBystGnTqmzZslq4cKHWrFmjzp07KyIiwukJv/p+V5swYYLWrVunVatWqVevXqpQoYKKFCmidu3aadOmTbrrrrskSUuXLlWtWrWcmps2barffvvN2U7hwoUlSRUrVpTH49G9997rrHvvvfdUqlQppU2bViVLltTbb7/tVcOGDRtUoUIFpU2bVlWqVNGCBQsS9UqvXbtW1apVU0BAgHLnzq3Bgwd79ejfe++96t27t/r166fs2bMrNDRUXbp0UdOmTb0eKzY2Vjlz5tR///vfa+4PAHceepIB4CZIly6dTp065fy8atUqBQYGasWKFZKkqKgohYaGKiQkRD/88IOOHz+ubt26qXfv3po5c6YkaezYsZo5c6amT5+uUqVKaezYsZo/f77uu+++f1xHfHy8GjVqpHPnzmn27NkqWrSofv75Z6VKlUo1a9bUhAkTNGLECO3Zs0eSlDFjxmtuZ86cOWrQoIEqVqyYaF2aNGmUJk0a53kNGDBA5cqV0/nz5zVixAi1aNFC27Ztk5+fn77//ntVq1ZNK1euVJkyZZxe3Dlz5mjEiBGaNGmSKlasqB9//FHdu3dXhgwZ1KlTJ0VGRqpZs2Zq3LixPvzwQ/3xxx/q16+fVx2HDx9W48aN9fjjj+v999/X7t271b17d6VNm9Yr/M+aNUs9evTQ+vXrJUmnTp1SnTp1dPToUeXOnVuStHDhQl24cEGtW7f+x/saQApnAIDr0qlTJ3vooYfMzCw+Pt5WrFhhAQEB9swzzzjrg4ODLTo62rnPO++8Y1myZLHz5887yxYtWmR+fn4WHh5uZma5c+e20aNHO+tjY2MtX758zmOZmRUsWNDGjx/vVU/58uVt5MiRZma2bNky8/Pzsz179lyz9hkzZlhQUNDfPsd06dLZU0899bft3E6cOGGSbMeOHWZmtn//fpNkP/74o1e7okWL2ocffui17KWXXrKQkBAzM5syZYply5bNLl686Kx/9913vbb13HPPWYkSJSw+Pt5pM3nyZMuYMaPFxcWZmVndunWtYsWKieosXbq0vf76687PzZo1s8cff/y6ny+AlIvhFgBwAxYuXKiMGTMqbdq0atSokVq3bu3Ve3n33Xd7jX395ZdfVL58eWXIkMFZds899yg+Pl579uxRRESEjh49qurVqzvrU6dOrSpVqlxXXdu2bVO+fPlUvHjxG39yUqJhHn9m3759atu2rYoUKaLAwEAVKlRIkhQWFvan94mKitJvv/2mrl27KmPGjM7t5ZdfdoZq7NmzR+XKlVPatGmd+1WrVs1rO7/88otCQkLk8XicZffcc4/Onz+vQ4cOOcsqV66cqIZu3bppxowZkqRjx45pyZIl6tKlyz96zgDuDAy3AIAbUK9ePU2ZMkX+/v7KkyePUqf2/ji9OgzfTH5+fokC7NUzTaRLl+6mPE7x4sW1e/fuv23XrFkzFSxYUO+++67y5Mmj+Ph4lS1bVjExMX96n4Rx2O+++67XlwJJSpUq1b8r/Bqu9bvo2LGjBg8erI0bN2rDhg0qXLiwateufdMfG0DyRU8yANyADBkyqFixYipQoECigHwtpUqV0vbt2xUVFeUsW79+vfz8/FSiRAkFBQUpd+7c2rRpk7P+8uXL2rJli9d2cuTIoaNHjzo/R0ZGav/+/c7P5cqV06FDh7R3795r1uHv76+4uLi/rbddu3ZauXKlfvzxx0TrYmNjFRUVpVOnTmnPnj0aNmyY6tevr1KlSunMmTOJHk+S12MGBwcrT548+v3331WsWDGvW8KJfiVKlNCOHTsUHR3t3O+HH37w2napUqW0ceNGry8N69evV6ZMmZQvX76/fH7ZsmVT8+bNNWPGDM2cOVOdO3f+230C4M5CSAaA26B9+/ZKmzatOnXqpJ07d+rrr79Wnz591KFDBwUHB0uS+vbtq9dee00LFizQ7t271bNnT509e9ZrO/fdd58++OADffPNN9qxY4c6derk1ftat25d1alTR61atdKKFSu0f/9+LVmyREuXLpV0ZXaM8+fPa9WqVTp58qQuXLhwzXr79eune+65R/Xr19fkyZO1fft2/f777/rkk09Uo0YN7du3T1myZFG2bNn0zjvv6Ndff9Xq1as1YMAAr+3kzJlT6dKl09KlS3Xs2DFFRERIkl544QWNGjVKEydO1N69e7Vjxw7NmDFD48aNk3QlpMfHx+uJJ57QL7/8omXLlmnMmDGS5Ayv6Nmzpw4ePKg+ffpo9+7d+uKLLzRy5EgNGDBAfn5//+etW7dumjVrln755Rd16tTpb9sDuMP4eEw0ACQ7V5+4dz3rf/rpJ6tXr56lTZvWsmbNat27d7dz584562NjY61v374WGBhomTNntgEDBljHjh29thUREWGtW7e2wMBAy58/v82cOdPrxD0zs1OnTlnnzp0tW7ZsljZtWitbtqwtXLjQWf/kk09atmzZTJLX/dwuXbpko0aNsrvvvtup+Z577rGZM2dabGysmZmtWLHCSpUqZQEBAVauXDlbs2aNSbL58+c723n33Xctf/785ufnZ3Xr1nWWz5kzxypUqGD+/v6WJUsWq1Onjs2bN89Zv379eitXrpz5+/tb5cqV7cMPPzRJtnv3bqfNmjVrrGrVqubv72+5cuWyQYMGObWZXTlxr2/fvtd8fvHx8VawYEFr3Ljxn+4DAHcuj9k/PDsDAAAfmjNnjjPP880Ye33+/HnlzZtXM2bMUMuWLW9ChQBSEk7cAwAkSe+//76KFCmivHnzavv27Ro0aJAeffTRfx2Q4+PjdfLkSY0dO1aZM2fWgw8+eJMqBpCSEJIBAElSeHi4RowYofDwcOXOnVuPPPKIXnnllX+93bCwMBUuXFj58uXTzJkz/9GJlwDuPAy3AAAAAFyY3QIAAABwISQDAAAALoRkAAAAwIWQDAAAALgQkgEAAAAXQjIAAADgQkgGAAAAXAjJAAAAgAshGQAAAHD5P7qWBwuPdLALAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of product category\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(data=df, x='Category', palette='Set3')\n",
    "plt.title(\"Product Category Distribution\")\n",
    "plt.xlabel(\"Product Category\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EwcscxbThooO"
   },
   "source": [
    "**2. Visual Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "id": "MpT4BnNphn_T",
    "outputId": "ca719754-0bdc-43b2-e481-c6ee593d2299"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Sales Trends Over Time (Monthly Sales Trends)\n",
    "monthly_sales = df.groupby(df['Date'].dt.to_period('M'))['Amount'].sum()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "monthly_sales.plot(kind='line', marker='o', color='skyblue')\n",
    "plt.title(\"Monthly Sales Trends\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Total Sales Amount\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 631
    },
    "id": "D4K0n3JH58Pn",
    "outputId": "0b6703cb-258d-4833-d557-88b8602392b1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top-Selling product categories\n",
    "top_selling_categories = df.groupby('Category')['Amount'].sum().nlargest(10)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "top_selling_categories.plot(kind='bar', color='skyblue')\n",
    "plt.title(\"Top 10 Selling Categories\")\n",
    "plt.xlabel(\"Category\")\n",
    "plt.ylabel(\"Total Sales Amount\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 711
    },
    "id": "pISImQ72sHXV",
    "outputId": "0383c16d-6f7d-4bb4-8ae1-931b811d5859"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7976\\2201134303.py:12: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=top_5_states.index, y=top_5_states.values, palette='Set3')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data by 'ship-state' and calculate the total sales\n",
    "sales_by_state = df.groupby('ship-state')['Amount'].sum()\n",
    "\n",
    "# Sort the data by total sales in descending order\n",
    "sales_by_state = sales_by_state.sort_values(ascending=False)\n",
    "\n",
    "# Get the top 5 states with the highest sales (sorted)\n",
    "top_5_states = sales_by_state.sort_values(ascending=False).head(5)\n",
    "\n",
    "# Create a bar chart to visualize the regional sales distribution (top 5 states)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=top_5_states.index, y=top_5_states.values, palette='Set3')\n",
    "plt.title('Top 5 States with Highest Sales')\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Total Sales Amount')\n",
    "plt.xticks(rotation=90, ha='right')  # Rotate x-axis labels for better readability\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kXyeppDsCGLZ"
   },
   "source": [
    "# Predictive Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NEaAZ_gOCN9g"
   },
   "source": [
    "**1. Building Predictive Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "GbYrPxIO-FJ3"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Feature selection\n",
    "X = df[['Sales Channel ', 'ship-service-level', 'Category', 'Size', 'Qty', 'Amount']]\n",
    "y = df['Status']\n",
    "\n",
    "# Convert categorical features to numerical using one-hot encoding\n",
    "X_encoded = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize and train the logistic regression model\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_LR = LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "lx5EZXhQUyWa"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Initialize and train the decision tree classifier\n",
    "DT = DecisionTreeClassifier(max_depth=5)\n",
    "DT.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_DT = DT.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "amav8xthUvhb"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Initialize and train the random forest classifier\n",
    "RF = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "RF.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_RF = RF.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cKUla3emCPrA"
   },
   "source": [
    "**2. Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-70vkOrCUlDA",
    "outputId": "217a37cf-15e5-4b7a-a9a3-608aa43c506c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Metrics:\n",
      "Accuracy: 0.8447705988825057\n",
      "Precision: 0.8355394229444675\n",
      "Recall: 0.8447705988825057\n",
      "F1-score: 0.8001062615516927\n",
      "\n",
      "Classification Report:\n",
      "                                precision    recall  f1-score   support\n",
      "\n",
      "                    Cancelled       0.95      0.21      0.35      3691\n",
      "                      Pending       0.00      0.00      0.00       132\n",
      "Pending - Waiting for Pick Up       0.00      0.00      0.00        61\n",
      "                      Shipped       0.89      0.99      0.94     15178\n",
      " Shipped - Delivered to Buyer       0.73      0.99      0.84      5725\n",
      "    Shipped - Lost in Transit       0.00      0.00      0.00         2\n",
      "   Shipped - Out for Delivery       0.00      0.00      0.00         8\n",
      "          Shipped - Picked Up       0.00      0.00      0.00       198\n",
      "  Shipped - Rejected by Buyer       0.00      0.00      0.00         1\n",
      " Shipped - Returned to Seller       0.00      0.00      0.00       392\n",
      "Shipped - Returning to Seller       0.00      0.00      0.00        25\n",
      "                     Shipping       0.00      0.00      0.00         1\n",
      "\n",
      "                     accuracy                           0.84     25414\n",
      "                    macro avg       0.21      0.18      0.18     25414\n",
      "                 weighted avg       0.84      0.84      0.80     25414\n",
      "\n",
      "Decision Tree Metrics:\n",
      "Accuracy: 0.9155190052726844\n",
      "Precision: 0.8902946652789993\n",
      "Recall: 0.9155190052726844\n",
      "F1-score: 0.8971235658467344\n",
      "\n",
      "Classification Report:\n",
      "                                precision    recall  f1-score   support\n",
      "\n",
      "                    Cancelled       0.99      0.68      0.81      3691\n",
      "                      Pending       0.00      0.00      0.00       132\n",
      "Pending - Waiting for Pick Up       0.00      0.00      0.00        61\n",
      "                      Shipped       0.92      1.00      0.95     15178\n",
      " Shipped - Delivered to Buyer       0.88      0.98      0.93      5725\n",
      "    Shipped - Lost in Transit       0.00      0.00      0.00         2\n",
      "   Shipped - Out for Delivery       0.00      0.00      0.00         8\n",
      "          Shipped - Picked Up       0.00      0.00      0.00       198\n",
      "  Shipped - Rejected by Buyer       0.00      0.00      0.00         1\n",
      " Shipped - Returned to Seller       0.00      0.00      0.00       392\n",
      "Shipped - Returning to Seller       0.00      0.00      0.00        25\n",
      "                     Shipping       0.00      0.00      0.00         1\n",
      "\n",
      "                     accuracy                           0.92     25414\n",
      "                    macro avg       0.23      0.22      0.22     25414\n",
      "                 weighted avg       0.89      0.92      0.90     25414\n",
      "\n",
      "Random Forest Metrics:\n",
      "Accuracy: 0.9115054694263005\n",
      "Precision: 0.8889128659806378\n",
      "Recall: 0.9115054694263005\n",
      "F1-score: 0.8948875841638896\n",
      "\n",
      "Classification Report:\n",
      "                                precision    recall  f1-score   support\n",
      "\n",
      "                    Cancelled       0.98      0.68      0.80      3691\n",
      "                      Pending       0.00      0.00      0.00       132\n",
      "Pending - Waiting for Pick Up       0.00      0.00      0.00        61\n",
      "                      Shipped       0.92      0.99      0.95     15178\n",
      " Shipped - Delivered to Buyer       0.88      0.97      0.92      5725\n",
      "    Shipped - Lost in Transit       0.00      0.00      0.00         2\n",
      "   Shipped - Out for Delivery       0.00      0.00      0.00         8\n",
      "          Shipped - Picked Up       0.08      0.02      0.03       198\n",
      "  Shipped - Rejected by Buyer       0.00      0.00      0.00         1\n",
      " Shipped - Returned to Seller       0.00      0.00      0.00       392\n",
      "Shipped - Returning to Seller       0.00      0.00      0.00        25\n",
      "                     Shipping       0.00      0.00      0.00         1\n",
      "\n",
      "                     accuracy                           0.91     25414\n",
      "                    macro avg       0.24      0.22      0.23     25414\n",
      "                 weighted avg       0.89      0.91      0.89     25414\n",
      "\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Evaluate model performance\n",
    "print(\"Logistic Regression Metrics:\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_LR))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_LR, average='weighted'))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_LR, average='weighted'))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_LR, average='weighted'))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_LR))\n",
    "\n",
    "print(\"Decision Tree Metrics:\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_DT))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_DT, average='weighted'))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_DT, average='weighted'))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_DT, average='weighted'))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_DT))\n",
    "\n",
    "print(\"Random Forest Metrics:\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_RF))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_RF, average='weighted'))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_RF, average='weighted'))\n",
    "print(\"F1-score:\", f1_score(y_test, y_pred_RF, average='weighted'))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_RF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yCDYa_FwCScn",
    "outputId": "5560e66e-e3a4-44bb-a30e-63d2f506d5f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression - Precision (macro): [0.21533802 0.21450575 0.21519006 0.19967898 0.20827454]\n",
      "Logistic Regression - Recall (macro): [0.17266571 0.18624185 0.18399945 0.1566609  0.18287498]\n",
      "\n",
      "\n",
      "Decision Tree - Precision (macro): [0.23345687 0.23269957 0.23368375 0.21426406 0.23263526]\n",
      "Decision Tree - Recall (macro): [0.22589487 0.22122779 0.22589398 0.20437172 0.21972398]\n",
      "\n",
      "\n",
      "Random Forest - Precision (macro): [0.29612783 0.22161091 0.32672764 0.30486313 0.23786561]\n",
      "Random Forest - Recall (macro): [0.22754719 0.2035542  0.23994318 0.21803302 0.21803264]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Perform cross-validation for each model\n",
    "models = [LR, DT, RF]\n",
    "model_names = ['Logistic Regression', 'Decision Tree', 'Random Forest']\n",
    "\n",
    "for model, name in zip(models, model_names):\n",
    "    scoring = ['precision_macro', 'recall_macro']\n",
    "    scores = cross_validate(model, X_encoded, y, scoring=scoring, cv=5)\n",
    "    print(f\"{name} - Precision (macro):\", scores['test_precision_macro'])\n",
    "    print(f\"{name} - Recall (macro):\", scores['test_recall_macro'])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hpTVNlePnAFL"
   },
   "source": [
    "# Dashboard Development\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ib4O0KIHnC90"
   },
   "source": [
    "**1. Dashboard Design**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 671
    },
    "id": "iR9rfLp0jr2N",
    "outputId": "38c1aa07-594c-438f-dd28-b1a7bf7a30af"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1bbd3664c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import dash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "# Create a Dash application\n",
    "app = dash.Dash(__name__)\n",
    "\n",
    "# Define the app layout\n",
    "app.layout = html.Div(children=[\n",
    "    html.H1(children='Amazon Sales Dashboard'),\n",
    "\n",
    "    # Sales Trends over Time\n",
    "    dcc.Graph(\n",
    "        id='sales-trend-graph',\n",
    "        figure={\n",
    "            'data': [\n",
    "                {\n",
    "                    'x': df['Date'],\n",
    "                    'y': df['Amount'],\n",
    "                    'type': 'line',\n",
    "                    'name': 'Sales Amount'\n",
    "                }\n",
    "            ],\n",
    "            'layout': {\n",
    "                'title': 'Monthly Sales Trends'\n",
    "            }\n",
    "        }\n",
    "    ),\n",
    "\n",
    "    # Top-Selling Categories\n",
    "    dcc.Graph(\n",
    "        id='top-categories-graph',\n",
    "        figure={\n",
    "            'data': [\n",
    "                {\n",
    "                    'x': df.groupby('Category')['Amount'].sum().nlargest(10).index,\n",
    "                    'y': df.groupby('Category')['Amount'].sum().nlargest(10).values,\n",
    "                    'type': 'bar',\n",
    "                    'name': 'Sales Amount'\n",
    "                }\n",
    "            ],\n",
    "            'layout': {\n",
    "                'title': 'Top 10 Selling Categories'\n",
    "            }\n",
    "        }\n",
    "    ),\n",
    "\n",
    "    # Regional Sales Distribution\n",
    "    dcc.Graph(\n",
    "        id='regional-sales-graph',\n",
    "        figure={\n",
    "            'data': [\n",
    "                {\n",
    "                    'text': df['ship-state'],\n",
    "                    'type': 'scattergeo',\n",
    "                    'marker': {\n",
    "                        'size': df['Amount'] * 0.01,\n",
    "                        'color': 'skyblue'\n",
    "                    }\n",
    "                }\n",
    "            ],\n",
    "            'layout': {\n",
    "                'title': 'Regional Sales Distribution',\n",
    "                'geo': {\n",
    "                    'showframe': False,\n",
    "                    'projection': {'type': 'natural earth'}\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "])\n",
    "\n",
    "# Run the app\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "akc6ywBcp99P"
   },
   "source": [
    "# Conclusion\n",
    "The provided code demonstrates the implementation of a data analysis project for an Amazon sales dataset. It covers data exploration, cleaning, visualization, model building, and the creation of an interactive dashboard using Python libraries like pandas, matplotlib, seaborn, and Dash.\n"
   ]
  }
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