{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30729d37",
   "metadata": {},
   "source": [
    "# Hotel Booking & Inventory Analysis\n",
    "\n",
    "**Python • Pandas • Matplotlib**\n",
    "\n",
    "This portfolio project analyses a synthetic 13-unit resort dataset covering **January 2025 to August 2026**.\n",
    "\n",
    "The objective is to understand inventory utilisation, realised revenue, occupancy, ADR, RevPAR, booking-channel behaviour, cancellations, and booking lead time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee99a278",
   "metadata": {},
   "source": [
    "## 1. Data Loading\n",
    "\n",
    "The project uses two source datasets:\n",
    "\n",
    "- `hotel_bookings.csv` — one row per booking\n",
    "- `room_inventory.csv` — one row per accommodation category\n",
    "\n",
    "Dates are parsed during import so they can be used directly for time-based analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8eda696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bookings shape: (4476, 11)\n",
      "Inventory shape: (4, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in</th>\n",
       "      <th>check_out</th>\n",
       "      <th>room_type</th>\n",
       "      <th>channel</th>\n",
       "      <th>lead_time_days</th>\n",
       "      <th>nights</th>\n",
       "      <th>nightly_rate</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>booking_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00001</td>\n",
       "      <td>2024-12-10</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01-03</td>\n",
       "      <td>Cottage</td>\n",
       "      <td>Direct</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>5428.20</td>\n",
       "      <td>False</td>\n",
       "      <td>10856.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00002</td>\n",
       "      <td>2024-12-21</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01-02</td>\n",
       "      <td>Cottage</td>\n",
       "      <td>Direct</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5478.93</td>\n",
       "      <td>False</td>\n",
       "      <td>5478.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00003</td>\n",
       "      <td>2024-12-15</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01-04</td>\n",
       "      <td>Cottage</td>\n",
       "      <td>MakeMyTrip</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5381.83</td>\n",
       "      <td>False</td>\n",
       "      <td>16145.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B00004</td>\n",
       "      <td>2024-12-09</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01-04</td>\n",
       "      <td>Cottage</td>\n",
       "      <td>Booking.com</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>5387.16</td>\n",
       "      <td>True</td>\n",
       "      <td>16161.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B00005</td>\n",
       "      <td>2024-11-24</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01-04</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Booking.com</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>11259.63</td>\n",
       "      <td>False</td>\n",
       "      <td>33778.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  booking_id booking_date   check_in  check_out room_type      channel  \\\n",
       "0     B00001   2024-12-10 2025-01-01 2025-01-03   Cottage       Direct   \n",
       "1     B00002   2024-12-21 2025-01-01 2025-01-02   Cottage       Direct   \n",
       "2     B00003   2024-12-15 2025-01-01 2025-01-04   Cottage   MakeMyTrip   \n",
       "3     B00004   2024-12-09 2025-01-01 2025-01-04   Cottage  Booking.com   \n",
       "4     B00005   2024-11-24 2025-01-01 2025-01-04     Villa  Booking.com   \n",
       "\n",
       "   lead_time_days  nights  nightly_rate  cancelled  booking_value  \n",
       "0              22       2       5428.20      False       10856.40  \n",
       "1              11       1       5478.93      False        5478.93  \n",
       "2              17       3       5381.83      False       16145.49  \n",
       "3              23       3       5387.16       True       16161.48  \n",
       "4              38       3      11259.63      False       33778.89  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "bookings = pd.read_csv(\n",
    "    \"data/hotel_bookings.csv\",\n",
    "    parse_dates=[\"booking_date\", \"check_in\", \"check_out\"]\n",
    ")\n",
    "\n",
    "inventory = pd.read_csv(\"data/room_inventory.csv\")\n",
    "\n",
    "print(\"Bookings shape:\", bookings.shape)\n",
    "print(\"Inventory shape:\", inventory.shape)\n",
    "\n",
    "bookings.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96661a6",
   "metadata": {},
   "source": [
    "## 2. Data Quality Checks\n",
    "\n",
    "Before calculating KPIs, the data is checked for duplicate booking IDs, missing values, data types, and basic date consistency.\n",
    "\n",
    "A booking ID should represent one unique booking.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56d0cb75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate booking IDs: 0\n",
      "\n",
      "Missing values:\n",
      "booking_id        0\n",
      "booking_date      0\n",
      "check_in          0\n",
      "check_out         0\n",
      "room_type         0\n",
      "channel           0\n",
      "lead_time_days    0\n",
      "nights            0\n",
      "nightly_rate      0\n",
      "cancelled         0\n",
      "booking_value     0\n",
      "dtype: int64\n",
      "\n",
      "Data types:\n",
      "booking_id                   str\n",
      "booking_date      datetime64[us]\n",
      "check_in          datetime64[us]\n",
      "check_out         datetime64[us]\n",
      "room_type                    str\n",
      "channel                      str\n",
      "lead_time_days             int64\n",
      "nights                     int64\n",
      "nightly_rate             float64\n",
      "cancelled                   bool\n",
      "booking_value            float64\n",
      "dtype: object\n",
      "\n",
      "Bookings with check-out on/before check-in: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Duplicate booking IDs:\", bookings[\"booking_id\"].duplicated().sum())\n",
    "\n",
    "print(\"\\nMissing values:\")\n",
    "print(bookings.isna().sum())\n",
    "\n",
    "print(\"\\nData types:\")\n",
    "print(bookings.dtypes)\n",
    "\n",
    "invalid_dates = (bookings[\"check_out\"] <= bookings[\"check_in\"]).sum()\n",
    "print(\"\\nBookings with check-out on/before check-in:\", invalid_dates)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14792a7b",
   "metadata": {},
   "source": [
    "## 3. Inventory Overview\n",
    "\n",
    "The property contains **13 accommodation units** across Cottage, Villa, Dormitory, and Camping Tent categories.\n",
    "\n",
    "Inventory is required to calculate available room nights and occupancy rather than relying only on booking counts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06d93a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>units</th>\n",
       "      <th>base_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cottage</td>\n",
       "      <td>8</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Villa</td>\n",
       "      <td>1</td>\n",
       "      <td>10500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dormitory</td>\n",
       "      <td>2</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Camping Tent</td>\n",
       "      <td>2</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      room_type  units  base_rate\n",
       "0       Cottage      8       5200\n",
       "1         Villa      1      10500\n",
       "2     Dormitory      2       2200\n",
       "3  Camping Tent      2       2800"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inventory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "518981e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total accommodation units: 13\n"
     ]
    }
   ],
   "source": [
    "total_units = inventory[\"units\"].sum()\n",
    "print(\"Total accommodation units:\", total_units)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cda03e",
   "metadata": {},
   "source": [
    "## 4. Data Preparation\n",
    "\n",
    "Cancelled bookings are useful for channel and cancellation analysis, but they should not contribute to realised occupancy or realised room revenue.\n",
    "\n",
    "For operating KPIs, a separate `active` dataset is therefore created containing non-cancelled bookings only.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "952244a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All bookings: 4476\n",
      "Non-cancelled bookings: 3945\n",
      "Cancelled bookings: 531\n"
     ]
    }
   ],
   "source": [
    "active = bookings.loc[~bookings[\"cancelled\"]].copy()\n",
    "\n",
    "active[\"month\"] = active[\"check_in\"].dt.to_period(\"M\").astype(str)\n",
    "active[\"revenue\"] = active[\"booking_value\"]\n",
    "\n",
    "print(\"All bookings:\", len(bookings))\n",
    "print(\"Non-cancelled bookings:\", len(active))\n",
    "print(\"Cancelled bookings:\", bookings[\"cancelled\"].sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ada28e",
   "metadata": {},
   "source": [
    "## 5. Monthly Occupancy, ADR & RevPAR\n",
    "\n",
    "Three core hotel KPIs are calculated:\n",
    "\n",
    "- **Occupancy %** = Sold room nights ÷ Available room nights\n",
    "- **ADR** = Average Daily Rate / average realised nightly rate\n",
    "- **RevPAR** = Realised room revenue ÷ Available room nights\n",
    "\n",
    "Available room nights are calculated from the property's 13 units and the number of calendar days in each month.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a78e4110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>bookings</th>\n",
       "      <th>room_nights</th>\n",
       "      <th>revenue</th>\n",
       "      <th>adr</th>\n",
       "      <th>days</th>\n",
       "      <th>available_room_nights</th>\n",
       "      <th>occupancy_pct</th>\n",
       "      <th>revpar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-01</td>\n",
       "      <td>193</td>\n",
       "      <td>370</td>\n",
       "      <td>1936640.42</td>\n",
       "      <td>5153.46</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>91.81</td>\n",
       "      <td>4805.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-02</td>\n",
       "      <td>165</td>\n",
       "      <td>315</td>\n",
       "      <td>1737616.70</td>\n",
       "      <td>5295.13</td>\n",
       "      <td>28</td>\n",
       "      <td>364</td>\n",
       "      <td>86.54</td>\n",
       "      <td>4773.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-03</td>\n",
       "      <td>175</td>\n",
       "      <td>358</td>\n",
       "      <td>1801894.53</td>\n",
       "      <td>5057.25</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>88.83</td>\n",
       "      <td>4471.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-04</td>\n",
       "      <td>154</td>\n",
       "      <td>326</td>\n",
       "      <td>1499043.65</td>\n",
       "      <td>4627.79</td>\n",
       "      <td>30</td>\n",
       "      <td>390</td>\n",
       "      <td>83.59</td>\n",
       "      <td>3843.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-05</td>\n",
       "      <td>163</td>\n",
       "      <td>331</td>\n",
       "      <td>1528816.23</td>\n",
       "      <td>4802.06</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>82.13</td>\n",
       "      <td>3793.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025-06</td>\n",
       "      <td>192</td>\n",
       "      <td>370</td>\n",
       "      <td>1928989.14</td>\n",
       "      <td>5126.17</td>\n",
       "      <td>30</td>\n",
       "      <td>390</td>\n",
       "      <td>94.87</td>\n",
       "      <td>4946.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2025-07</td>\n",
       "      <td>192</td>\n",
       "      <td>398</td>\n",
       "      <td>2148973.93</td>\n",
       "      <td>5387.41</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>98.76</td>\n",
       "      <td>5332.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2025-08</td>\n",
       "      <td>223</td>\n",
       "      <td>451</td>\n",
       "      <td>2440588.58</td>\n",
       "      <td>5388.05</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>111.91</td>\n",
       "      <td>6056.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2025-09</td>\n",
       "      <td>177</td>\n",
       "      <td>327</td>\n",
       "      <td>1673445.32</td>\n",
       "      <td>5141.90</td>\n",
       "      <td>30</td>\n",
       "      <td>390</td>\n",
       "      <td>83.85</td>\n",
       "      <td>4290.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025-10</td>\n",
       "      <td>236</td>\n",
       "      <td>441</td>\n",
       "      <td>2615104.02</td>\n",
       "      <td>5840.31</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>109.43</td>\n",
       "      <td>6489.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2025-11</td>\n",
       "      <td>277</td>\n",
       "      <td>527</td>\n",
       "      <td>3433712.41</td>\n",
       "      <td>6425.75</td>\n",
       "      <td>30</td>\n",
       "      <td>390</td>\n",
       "      <td>135.13</td>\n",
       "      <td>8804.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2025-12</td>\n",
       "      <td>302</td>\n",
       "      <td>586</td>\n",
       "      <td>4129492.34</td>\n",
       "      <td>7103.10</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>145.41</td>\n",
       "      <td>10246.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2026-01</td>\n",
       "      <td>215</td>\n",
       "      <td>407</td>\n",
       "      <td>2179102.99</td>\n",
       "      <td>5464.13</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>100.99</td>\n",
       "      <td>5407.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2026-02</td>\n",
       "      <td>169</td>\n",
       "      <td>307</td>\n",
       "      <td>1577667.19</td>\n",
       "      <td>5097.63</td>\n",
       "      <td>28</td>\n",
       "      <td>364</td>\n",
       "      <td>84.34</td>\n",
       "      <td>4334.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2026-03</td>\n",
       "      <td>176</td>\n",
       "      <td>335</td>\n",
       "      <td>1605399.73</td>\n",
       "      <td>4650.94</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>83.13</td>\n",
       "      <td>3983.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2026-04</td>\n",
       "      <td>144</td>\n",
       "      <td>278</td>\n",
       "      <td>1304384.98</td>\n",
       "      <td>4710.22</td>\n",
       "      <td>30</td>\n",
       "      <td>390</td>\n",
       "      <td>71.28</td>\n",
       "      <td>3344.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2026-05</td>\n",
       "      <td>174</td>\n",
       "      <td>342</td>\n",
       "      <td>1557570.44</td>\n",
       "      <td>4641.04</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>84.86</td>\n",
       "      <td>3864.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2026-06</td>\n",
       "      <td>190</td>\n",
       "      <td>355</td>\n",
       "      <td>1799981.86</td>\n",
       "      <td>5073.83</td>\n",
       "      <td>30</td>\n",
       "      <td>390</td>\n",
       "      <td>91.03</td>\n",
       "      <td>4615.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2026-07</td>\n",
       "      <td>220</td>\n",
       "      <td>433</td>\n",
       "      <td>2294448.72</td>\n",
       "      <td>5388.95</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>107.44</td>\n",
       "      <td>5693.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2026-08</td>\n",
       "      <td>208</td>\n",
       "      <td>418</td>\n",
       "      <td>2458802.59</td>\n",
       "      <td>5858.45</td>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>103.72</td>\n",
       "      <td>6101.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  bookings  room_nights     revenue      adr  days  \\\n",
       "0   2025-01       193          370  1936640.42  5153.46    31   \n",
       "1   2025-02       165          315  1737616.70  5295.13    28   \n",
       "2   2025-03       175          358  1801894.53  5057.25    31   \n",
       "3   2025-04       154          326  1499043.65  4627.79    30   \n",
       "4   2025-05       163          331  1528816.23  4802.06    31   \n",
       "5   2025-06       192          370  1928989.14  5126.17    30   \n",
       "6   2025-07       192          398  2148973.93  5387.41    31   \n",
       "7   2025-08       223          451  2440588.58  5388.05    31   \n",
       "8   2025-09       177          327  1673445.32  5141.90    30   \n",
       "9   2025-10       236          441  2615104.02  5840.31    31   \n",
       "10  2025-11       277          527  3433712.41  6425.75    30   \n",
       "11  2025-12       302          586  4129492.34  7103.10    31   \n",
       "12  2026-01       215          407  2179102.99  5464.13    31   \n",
       "13  2026-02       169          307  1577667.19  5097.63    28   \n",
       "14  2026-03       176          335  1605399.73  4650.94    31   \n",
       "15  2026-04       144          278  1304384.98  4710.22    30   \n",
       "16  2026-05       174          342  1557570.44  4641.04    31   \n",
       "17  2026-06       190          355  1799981.86  5073.83    30   \n",
       "18  2026-07       220          433  2294448.72  5388.95    31   \n",
       "19  2026-08       208          418  2458802.59  5858.45    31   \n",
       "\n",
       "    available_room_nights  occupancy_pct    revpar  \n",
       "0                     403          91.81   4805.56  \n",
       "1                     364          86.54   4773.67  \n",
       "2                     403          88.83   4471.20  \n",
       "3                     390          83.59   3843.70  \n",
       "4                     403          82.13   3793.59  \n",
       "5                     390          94.87   4946.13  \n",
       "6                     403          98.76   5332.44  \n",
       "7                     403         111.91   6056.05  \n",
       "8                     390          83.85   4290.89  \n",
       "9                     403         109.43   6489.09  \n",
       "10                    390         135.13   8804.39  \n",
       "11                    403         145.41  10246.88  \n",
       "12                    403         100.99   5407.20  \n",
       "13                    364          84.34   4334.25  \n",
       "14                    403          83.13   3983.62  \n",
       "15                    390          71.28   3344.58  \n",
       "16                    403          84.86   3864.94  \n",
       "17                    390          91.03   4615.34  \n",
       "18                    403         107.44   5693.42  \n",
       "19                    403         103.72   6101.25  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly = (\n",
    "    active.groupby(\"month\")\n",
    "    .agg(\n",
    "        bookings=(\"booking_id\", \"count\"),\n",
    "        room_nights=(\"nights\", \"sum\"),\n",
    "        revenue=(\"revenue\", \"sum\"),\n",
    "        adr=(\"nightly_rate\", \"mean\")\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "monthly[\"days\"] = pd.to_datetime(monthly[\"month\"] + \"-01\").dt.days_in_month\n",
    "monthly[\"available_room_nights\"] = monthly[\"days\"] * total_units\n",
    "monthly[\"occupancy_pct\"] = (\n",
    "    100 * monthly[\"room_nights\"] / monthly[\"available_room_nights\"]\n",
    ")\n",
    "monthly[\"revpar\"] = monthly[\"revenue\"] / monthly[\"available_room_nights\"]\n",
    "\n",
    "monthly.round(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54664724",
   "metadata": {},
   "source": [
    "### Occupancy Trend\n",
    "\n",
    "Occupancy is plotted over time to identify seasonality and periods of stronger or weaker inventory utilisation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "797f23c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "x = pd.to_datetime(monthly[\"month\"] + \"-01\")\n",
    "\n",
    "ax.plot(x, monthly[\"occupancy_pct\"], marker=\"o\")\n",
    "ax.set_title(\"Monthly Occupancy Rate\")\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"Occupancy %\")\n",
    "ax.grid(alpha=0.25)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60809595",
   "metadata": {},
   "source": [
    "### ADR and RevPAR Trend\n",
    "\n",
    "ADR shows the average realised nightly rate, while RevPAR combines room revenue with available inventory. Looking at both helps distinguish pricing performance from inventory utilisation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31cbc27b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "ax.plot(x, monthly[\"adr\"], marker=\"o\", label=\"ADR\")\n",
    "ax.plot(x, monthly[\"revpar\"], marker=\"o\", label=\"RevPAR\")\n",
    "\n",
    "ax.set_title(\"ADR vs RevPAR\")\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"₹\")\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.25)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66af708b",
   "metadata": {},
   "source": [
    "## 6. Booking Channel Analysis\n",
    "\n",
    "Channel analysis compares booking volume, cancellation rate, average lead time, and booking value across Direct, Booking.com, MakeMyTrip, and Agoda.\n",
    "\n",
    "Cancellation rate is calculated using all bookings because cancelled reservations are themselves part of the behaviour being measured.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7072fdab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel</th>\n",
       "      <th>bookings</th>\n",
       "      <th>cancellation_rate</th>\n",
       "      <th>avg_lead_time</th>\n",
       "      <th>gross_booking_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Direct</td>\n",
       "      <td>1732</td>\n",
       "      <td>7.16</td>\n",
       "      <td>11.54</td>\n",
       "      <td>18407481.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Booking.com</td>\n",
       "      <td>1150</td>\n",
       "      <td>15.91</td>\n",
       "      <td>19.82</td>\n",
       "      <td>12033020.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MakeMyTrip</td>\n",
       "      <td>1096</td>\n",
       "      <td>11.95</td>\n",
       "      <td>19.51</td>\n",
       "      <td>11640500.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agoda</td>\n",
       "      <td>498</td>\n",
       "      <td>18.67</td>\n",
       "      <td>19.63</td>\n",
       "      <td>5198070.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       channel  bookings  cancellation_rate  avg_lead_time  \\\n",
       "2       Direct      1732               7.16          11.54   \n",
       "1  Booking.com      1150              15.91          19.82   \n",
       "3   MakeMyTrip      1096              11.95          19.51   \n",
       "0        Agoda       498              18.67          19.63   \n",
       "\n",
       "   gross_booking_value  \n",
       "2          18407481.22  \n",
       "1          12033020.91  \n",
       "3          11640500.08  \n",
       "0           5198070.29  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel = (\n",
    "    bookings.groupby(\"channel\")\n",
    "    .agg(\n",
    "        bookings=(\"booking_id\", \"count\"),\n",
    "        cancellation_rate=(\"cancelled\", \"mean\"),\n",
    "        avg_lead_time=(\"lead_time_days\", \"mean\"),\n",
    "        gross_booking_value=(\"booking_value\", \"sum\")\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "channel[\"cancellation_rate\"] *= 100\n",
    "\n",
    "channel.round(2).sort_values(\"bookings\", ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d157ae5",
   "metadata": {},
   "source": [
    "### Cancellation Rate by Channel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb30ca49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OFnSA5U0Mkdz14mratKnNnj3bbrzxRrc/KAAndhYlpf+z2rdUl6zT+SrpUQ20xnLW+5CcsxLaH7Qf6KyJ3j8FdJXzqAVcraP631fLdEqlZF9LDZHYT4G0QuAF0pi+wNVS9eGHH7qAqQCp0786JagwrKDp0TpqPVFIEYVh1expKtLQ070KK2rBi+vCCy90X9Chp3gVXletWuUC7ldffeVChTpzKXh5rbEKwfpSV/BViJg4caI7natJH/RFH/pYcScmSGhZcp+HvqhVQ6qwolCsGkT9VGBL6HG1PXou+pLXKXmFD4XITz75xLVmn83rkxC9N1ovoTpOtXIpXCv09ezZ07XoqaOTWrDfeOMN9zeee+45N3mCXsvQiQf0XHWbfqqFWB2jFBZFE1ko7Kv1T++DwrxeA01FrH0oMXoNtK16bRQcdaChwKjnoAkZFCgTooMvlTTo+SQ0CUlCLrvsMncg1KdPH3ddByZ6/grZCkFxa8UVgBVa1Zqp+l7drpkF77333rBJSJKznvc8Qw+adICm/6kHH3zQPXcdhGj9uFMLJ3TfpOpOX3nlFffY+j/QbHKaSlr7mx4zoX0iLh24vfTSS67lXM9JrcM6k6F9Q89TByPe2YiUbFty97WUPGZKX5vk7qexML0zMrZMGqoh2hsBAIgu1YEqOKm3fUKz2SGcDlB0YPD444+7MxIAYhs1vACQwan1W52q1PpL2I0voVEMdCpfZyC8USgAxDZKGgAgg1L9rYae06l61ZOqNATxqeREJT6qD1aNs8aXVa24Tufr9D+A2EdJAwBkUKrHVu2uamRVT63JGxCfKv9Ue6sadHXOUgdDjd6geueUjv0LIDoIvAAAAPA1angBAADgawReAAAA+Bqd1hKh3reaSlIdFKjRAgAAiM0ae3XA1bjUoVNsx0XgTYTCbtw52wEAABB7NNJMiRIlEr2dwJsItex6L6BmwQIAAEBs0TT1aqD0cltiCLyJ8MoYFHYJvAAAALHrTOWndFoDAACArxF4AQAA4GsEXgAAAPgagRcAAAC+RuAFAACArxF4AQAA4GsEXgAAAPgagRcAAAC+RuAFAACArxF4AQAA4GsEXgAAAPgagRcAAAC+RuAFAACArxF4AQAA4GsEXgAAAPha1mhvAP6fMn2m8XL40Prnmkd7EwAAyNBo4QUAAICvEXgBAADgawReAAAA+BqBFwAAAL5G4AUAAICvEXgBAADgawReAAAA+BqBFwAAAL5G4AUAAICvEXgBAADgawReAAAA+BqBFwAAAL5G4AUAAICvEXgBAADgawReAAAA+BqBFwAAAL5G4AUAAICvZbUYsGPHDvvzzz/t4osvtnz58oXdtmTJEjt16lS8+xQpUsTKlSuX4OP99ddftn379rBluXPntho1akR4ywEAABDrohp4ly9fbkOHDrVZs2bZrl27bM6cOda0adOwdR5//HE7fPhw8PqJEyfsxx9/tD59+tiQIUMSfFw95oQJE6xixYrBZeXLl7cPP/wwFZ8NAAAAYlHUA++tt95qgwcPdoE0IbNnzw67/sknn9gdd9xhHTp0SPKxr7rqKps4cWJEtxcAAADpT1QDb8eOHd3PzZs3J/s+o0aNsiuuuMKqVq2a5HrHjx+3VatWWf78+a1UqVKWKVOmc95eAAAApD/pqtPahg0bXPlDly5dzrjuV1995VqCa9eubWXLlrXp06enyTYCAAAgtqSrwPvee++5Tm1t27ZNcr3rrrvOtRqvWbPGdV677bbb3EUd4xJz7Ngx279/f9gFAAAA6V+6CbynT5+20aNH21133WW5cuVKcl2F22LFirnfs2bN6jqx6efUqVMTvY86wKn8wbuULFky4s8BAAAAaS/dBN4ZM2bYxo0bk1XOEJfCbuHChW3Lli2JrtO3b1/bt29f8LJp06Zz3GIAAADEgpgYhze5ndXq1atnl156abzbVKpw8OBBN85uIBBw5Qk5c+YM3v7777/b+vXrrVq1aok+fo4cOdwFAAAA/hLVwLtz5077448/3E9Rza2CaokSJdzFs3v3bleO8MYbbyT4OBrWbNGiRbZ69Wo3Tm+dOnWsW7duLuCqVXjQoEEuKN95551p9twAAAAQG6IaeH/44Qd75pln3O/169e3MWPGuEvnzp3dxfP999+71l2NupAQjeF75MgR93v27NndiAzDhg2zL774ws4//3x7+OGHXQDWbQAAAMhYMgVUA4B4NEqDOq+pnjfudMeppUyfabwTPrT+uebR3gQAADJ0Xks3ndYAAACAs0HgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvpY12hsAIHWU6TONl9an1j/XPNqbAADpCi28AAAA8DUCLwAAAHyNwAsAAABfI/ACAADA1wi8AAAA8DUCLwAAAHyNwAsAAABfI/ACAADA1wi8AAAA8DUCLwAAAHyNwAsAAABfy2ox4NChQ/b333/bRRddZLly5Qq7befOnbZv376wZTly5LCSJUsm63H/+ecfu/DCCy1LliwR324AAADEvqi28P7666/24IMPWpkyZaxixYq2ePHieOv897//tZo1a1qzZs2Cl27duiX5uKdOnbIHHnjAChYsaDVq1LBixYrZ+PHjU/GZAAAAIFZFtYX3m2++scqVK9t3331nl1xySaLrKeROnDgx2Y/7/PPP26effmorVqywqlWr2ttvv23t27d3fyOpvwMAAAD/iWoLb48ePax79+6WP3/+M667detWV6KQHCNGjLDOnTu7sCtdu3a1cuXKueALAACAjCVddFqbNGmSK2tQiULt2rVt0aJFia67fft227RpkzVs2DBs+eWXX25Lly5Ng60FAABALIn5wKvgumbNGtuxY4frgFarVi278cYbbcuWLQmuv2vXLvezUKFCYcsLFy4cvC0hx44ds/3794ddAAAAkP7FfOC9++67g6UJuXPntjfffNNOnjxpkydPTnD9zJn/7ymdOHEibPnx48eTHKlhyJAhrrTCuyRnFAgAAADEvpgPvHFpSDKNurBhw4YEby9RooT7uW3btrDluu7dlpC+ffu64c+8i8oiAAAAkP7FdOANBAJuiLFQmzdvtvXr11uFChXCxur1AmrevHmtTp06Nn369LDW3ZkzZ1rTpk2TDNL58uULuwAAACD9i+qwZAcOHHCdzLzWWNXl/v77765zmi4qS2jUqJH16tXLqlWrZhs3brT+/ftb2bJl3TBjoa2z6si2evVqd/2pp56y1q1bu45uqgF++eWXLXv27HbfffdF7bkCAAAgA7bwzpgxw42xe88991j58uVdUNX1cePGudsVUseMGeNaZ7XOK6+8Yi1btrRly5bZeeedF3ycIkWKWKlSpYLXW7RoYRMmTLCpU6favffe65bNmzcvXkc2AAAA+F+mgOoGEI9GaVDnNdXzplV5Q5k+03gnfGj9c82j8nfZn/wrWvsUAKTXvBbTNbwAAADAuSLwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8Leu5PsCRI0ds7dq1FggErGrVqpY7d+7IbBkAAAAQ7cD79ddfW4cOHSxXrlzu+uHDh23cuHHWrFmzSGwbAAAAkLYlDadOnQq7fv/999sHH3xgGzdudBf9rmUAAABAugy8DRs2tFWrVgWvHzp0yMqXLx+8XqFCBbcMAAAASJclDQMGDLDWrVvb7bffbk8++aQ98cQT1qBBA2vSpIm7fd68efbUU0+l1rYCAAAAqdvCe9NNN9mKFStcK26tWrWsZs2atnDhQmvRooW7LFiwwB5++OGUbwUAAAAQK53WzjvvPHvttdds8eLF1qVLF6tfv7698MILVqBAgdTZQgAAACAtx+E9ceKErVu3zgXcRYsWWenSpV1r78SJE89lOwAAAIDoB97Zs2dbmTJlXOe1Ro0aWaVKleyKK66wb775xoYNG2a33nqrbdmyJXW2FAAAAEjtwNu1a1d7+eWXbc+ePbZ79273u8oaKleubHPnznU1vgrDAAAAQLoMvHv37rV69eoFr9etW9ctk0yZMlm3bt1cmQMAAACQLjut9erVyw1Ddt1117nrM2bMsJ49e4atU7x48chuIQAAAJBWgVfj7l5//fX2/fffu+sKu2rlBQAAAHwzLJkCbqRD7unTp+3w4cOWK1cuy5IlS6LrZM6cvAqMY8eOudEkQulx9fgAAADIWFI8LFkkbdu2zQYNGmRly5a1vHnz2vz58+OtM23aNLvqqqssX758bgzgG264wVavXp3k4z700ENu2LRixYoFL1deeWUqPhMAAADEqqgG3nfffdeOHDliH330UYK3nzp1ykaMGOGmNN65c6dt2rTJChYs6Moq9u3bl+Rjt2zZ0g4ePBi8/PDDD6n0LAAAAOCrkoZI6t+/v/u5efPmBG9XGcKXX34ZvK6SBM3qVrJkSTfTm4JvUk6ePGlZs0b1KQIAACAjt/CeDW9ii0KFCiW5noKyArJKITQ+8C+//JJGWwgAAABfBN4NGzbY0KFD3di7nq+//tqOHz9uqUWPrfpcDY1Wu3btRNerVq2a2xZ1hPv5558te/bsrg5YE2Yk1dFt//79YRcAAABk0MC7cOFCu+SSS1yHsrfffju4fObMmfbWW29ZalA9b/v27W3r1q32ySefuIkuEtO9e3cXcLNly+bKH8aOHetqfnW/xAwZMsTy588fvOh+AAAAyKCB97HHHnO1tPPmzQtb3rFjR3vjjTcsNcJuhw4dXNCeM2eOlSpVKkX31wgQF110kf3555+JrtO3b18Xir2LOsgBAAAg/TurHl0rV6606dOnu99DW1rLlCmTZKg8Gxp/9+6777a5c+fad999Z+XLl0+wHEHrJTbO7u7du23jxo1JttrmyJHDXQAAAOAvZ9XCq2CpEJlQEC5SpEiyH0ejKGjIMNXaioYo03WvDjgQCLhWY01hrE5oGk/XG2ZM9/U88MADVq9evWD41egNs2fPtu3bt9uSJUusdevWrpObSiIAAACQsZxV4G3VqpU9/vjjLqB6LbwKu126dLE2bdok+3FUU6sQqw5oefLksbZt27rrL774ortdncwmTpxohw4dsiuuuCJsIokPP/ww+Dg5c+a03Llzu9/VStuvXz/3GDVr1nStw1WqVLGlS5e6MXwBAACQsWQKqBk1hfbu3WstWrSwZcuWudCrAKpZ0xRKNTqCZkRL7zRKgzqvqZ5XQ5ulhTJ9pqXJ30HaWv9c86i85OxP/hWtfQoA0mteO6saXk3bqw5rqqlVy6nqZ9VKe+211yY5egIAAACQ1s4q8JYoUcLNjqahv3RJ6DYAAAAg3dbwerOdxXXixAnbsWPHuW4TAAAAEJ0WXo2UkNDvorKGRYsWWbly5SK3dQAAAEBaBt7QERjijsagWc00Du+rr756rtsEAIgxdIL0LzpBIiNIUeA9evSo+0mdLgAAAHxdw0unNAAAAPh6lIa4rb6hs56JH8bhBQAAQAZu4T1w4ICbzlfTCGua4bx584ZdAAAAgHQdePv06WPLly+3MWPGuOtz5syxoUOHugkpnn322UhvIwAAAJC2gXfq1Kn23nvvWbNmzdz1Jk2aWO/evW3s2LE2ZcqUs98aAAAAIBYC79atW61SpUrud5Uw/PPPP+53zbq2atWqyG4hAAAAkNaBNxAIWObM/3fXypUr2+effx4sbShUqNC5bA8AAAAQ/cBbvnz5sHrebt26uUknWrZsaT179ozk9gEAAABpPyzZ77//Hvz9tttucx3YlixZYlWqVLEGDRqc2xYBAAAAsTQOr1SrVs1d5K+//rKyZctG4mEBAACA6JQ0JGT37t2unEGtvAAAAEC6DLz79u2zDh06uI5pmk2tc+fOduzYMfvggw+sQoUKNnHiRBsxYkTqbS0AAACQmiUNTzzxhM2cOdP+85//uOujRo2yTZs22fz5861///7Wq1cvy5kzZ0q3AQAAAIiNwPvFF1+4iSUuu+wyd7158+bWqFEjmzx5srVq1Sq1thEAAABIm5IGTThRp06d4PV69eoFgy8AAACQ7gPvqVOnLEuWLMHrWbP+XwNx9uzZI79lAAAAQDSGJatbt+4Zly1duvTctgoAAACIRuB94IEH4i1jogkAAAD4JvAOHz489bYEAAAAiOWJJwAAAIBYROAFAACArxF4AQAA4GsEXgAAAPgagRcAAAC+luJxeOXYsWM2cuRIW7Bgge3Zsyfe7TNnzozEtgEAAADRCbwPPvigTZ061Vq1amXlypU7960AAAAAYinwTp482WbPnm2XXnpp5LcIAAAAiHYNb/bs2WnZBQAAgH8D74033mgff/xx5LcGAAAAiIWShuPHj9t9991nkyZNsgoVKlimTJnCbmcKYgAAAKTrwHvw4EG75ZZb3O9btmyJ9DYBAAAA0Q28U6ZMidwWAAAAAH6deOLAgQNuPN+aNWtazpw5bd68eQmu9+abb1rlypWtQIEC1rhxY/vhhx/O+Nhncx8AAAD4z1kH3rVr11rnzp3t8ssvt0aNGrnftSwlhgwZYsuXL7dBgwa5ySxOnz4db50xY8ZYr1697Nlnn7Wff/7Zateubdddd12SpRRncx8AAAD401kF3q+++spq1Khhv/zyizVs2NCFXv2uZV9//XWyH2fw4MH21ltvuRbexAwdOtQ6depkbdq0sYsuusheeeUVy5Mnj2vBjeR9AAAA4E9nVcPbr18/F1Yfe+yxsOUvvPCCPfHEE27Yskj4559/bM2aNTZw4MDgssyZM9tVV13lpjWO1H0AAADgX2fVwqsyga5du8Zb3qVLFxc2I+Xvv/92P4sUKRK2XNe3bdsWsfuISir2798fdgEAAEAGDbyFCxe2n376Kd7yVatWudsiTS20ca8HAoGI3kf1xPnz5w9eSpYseY5bDQAAgHQbeO+99167/fbb3QgLP/74o7uMGDHC2rVr526LlKJFi7qfO3fuDFu+Y8eO4G2RuI/07dvX9u3bF7xs2rQpAs8AAAAA6bKG9+mnn7YcOXJY79693dBikjdvXnv00UddfW+kFCpUyCpWrGhz5861Vq1auWVqpdX1O++8M2L3ET0fXQAAAOAvZ9XCmyVLFnvyySdt7969tn79etuwYYP7Xct0WyT17NnTRo0aZbNmzbJDhw7ZgAEDbNeuXdatW7fgOv/5z3+sTp06KboPAAAAMoazauENrYstXbr0Wd9/7NixrqOb5/rrr3eP2b9/f3fxwqxGXlDr7O7du61q1ar25ZdfWrly5YL3O3HihOt05knOfQAAAJAxZAqcqffX/++ee+5xP0ePHh38PTFaJzlOnTrlwmpcWbNmdZe4NDFF3M5ocvLkSXdb9uzZk32fM9EoDeq8pnrefPnyWVoo02damvwdpK31zzWPykvO/uRf0din2J/8K1qfUUAkJDevJbuFV6Eyod/PhcofUlICkVhwTSgcn+k+AAAAyBiSHXjHjRsX/P3hhx+2unXrJrje0qVLI7NlAAAAQAScVfNnvXr1zuo2AAAAIK1ljnQdxXnnnRfJhwQAAADSbpSGzp07J/i71zlMM63RwgsAAIB0G3gPHjyY4O+SLVs2u/HGG92QYAAAAEC6DLzjx493PwsXLmzDhw9PrW0CAAAAolvDS9gFAACA72da08xmy5Yts40bN8Ybl7d9+/aR2DYAAAAgOoH3559/thYtWtj27dvt8OHDwRkupGjRogReAAAApO+Shp49e1qrVq3swIED7vrevXtt3bp1Vr9+fXcbAAAAkK4D75IlS6xv375u2t5MmTLZiRMnrFKlSvb+++/biBEjIr+VAAAAQFoGXrXoaqQG0c+tW7e630uWLGnbtm07220BAAAAYm+mtUaNGtnAgQNtxYoV1q9fP6tSpUpktgwAAACIVqe1Rx55JPj70KFDrWXLlq6coVixYjZhwoRIbBcAAAAQvcD74osvBn+vXLmyrV271o3SkC9fPlfTCwAAAKT7cXjj0tBkAAAAQLoNvPfcc0+yH3T06NFnuz0AAABAdAJv3NnUAAAAAF8F3nHjxqXulgAAAACxOCwZAAAAEMuo4QUAAICvUcMLAAAAX6OGFwAAAL5GDS8AAAB87awCbyAQsHfffdcaNmxoRYsWDS7v37+/bd26NZLbBwAAAKR94B0+fLgNGDDA2rZtazt27AguL1OmjA0aNOjctggAAACIhcA7YcIE69WrV9jyG264wSZNmhSpbQMAAACiE3jXr19vNWvWdL9nypQpuDxv3ry2d+/ec98qAAAAIJqBt1SpUrZixYp4gXfKlClWuXLlSG0bAAAAkHbDkoXq0aOH3X333fb888+764sXL7ZvvvnGXR82bNi5bxUAAAAQzcDbvXt3O3r0qHXs2NFOnz5tDRo0sPz589vAgQOtU6dOkdo2AAAAIDqBVx577DHXae3PP/90obdcuXKWLVu2c98iAAAAIBYCr2TJksUqVqwYua0BAAAAYqHT2qxZs6xz587xlmvZ7NmzI7FdAAAAQPQCr8oZHnzwwXjLH3jgAXv88ccjsV0AAABA9ALvmjVrXM1uXOXLl7fVq1dHYrsAAACA6AVeTSE8Y8aMeMunT59uJUuWjMR2AQAAANHrtKZyhi5dutiGDRusSZMmFggEbN68efbMM8/YoEGDLJKqVatmhw4dire8ffv2if6tfv362Ycffhi2TBNiKJADAAAgYznrwHvgwAF7+umnbd++fW6ZxuFV/W5Ctb3n4uuvv3bDnnmWLl1qbdu2tYYNGyZ6n927d9vFF19sb775ZnBZ9uzZI7pdAAAA8PmwZH379nWd1/744w83vbBqerNmPadRzhKdxjjUc889ZyVKlLBmzZoleb/cuXO70gsAAABkbOeUUBVwVSqQVg4fPmwff/yx9ezZ040BnJT58+e7Vl61PDdu3NiVOeh3AAAAZCxnFXiPHTtmI0eOtAULFtiePXvi3T5z5kxLDRMmTLCDBw+6KY2TUqhQIevfv79deeWVtmXLFhd2v/rqK1u2bJnlyJEj0eeki2f//v0R334AAACkvbOu4Z06daq1atUqweHJUsuoUaPshhtuiFfmEJc6z2XO/H8DUNSoUcMuvfRSV94wfvx4u/vuuxO8z5AhQ2zgwIGpst0AAABIZ4F38uTJbkY1Bcm08ttvv7kyBf3tM/HCrqd48eJWunRpN35wUjXJvXr1CmvhZYg1AACADBp4NeJBWrbseq27RYsWtRYtWqT4vkePHrW///7bChYsmOg6KnVIrNwBAAAAGWziiRtvvNF1HksrJ0+etDFjxti9996b4EgQffr0caUOcvz4cdepbceOHe66an67devmxgpu165dmm0zAAAA0nELr0LlfffdZ5MmTbIKFSq4YclCDR8+3CJJHc62bdtmnTp1SvD2Xbt2uc5pki1bNrdNdevWtSNHjrjSBP0+Z84chikDAADIgM4q8KrV9JZbbnG/e0EzNWk2N83qllhN7dChQ4MjLCh8P/DAA+6iCSjy5cvnQjAAAAAyprMKvFOmTLG0VKBAAXdJahiylCwHAABAxhH5qdEAAADOoEyfabxGPrT+uebmq8CrEgJN5LBx40bXqSxU+/btI7FtAAAAQHQC788//+yGB9u+fbub7ldT9u7bt8/dpqHDCLwAAABI18OSadgvzbJ24MABd33v3r22bt06q1+/vrsNAAAASNeBd8mSJW5mMs1oplERTpw4YZUqVbL333/fRowYEfmtBAAAANIy8KpFt3Dhwu53/dy6dav7XcOGabxcAAAAIF0H3lCNGjWygQMH2ooVK6xfv35WpUqVyGwZAAAAEK1Oa4888kjYpA8tW7Z05QzFihWzCRMmRGK7AAAAgOgF3hdffDH4e+XKlW3t2rVulAbNahZ3mmEAAAAgXZU0eMOPxaWhyfbv3x+JbQIAAACiE3inT59ud955Z6K333HHHfbtt99GYrsAAACAtA+8r776qvXq1SvR23XbK6+8EontAgAAANI+8Gokhho1aiR6u25buXJlJLYLAAAASPvAu2fPHtcxLTGq49U6AAAAQLoMvKVKlbKlS5cmOQNb6dKlI7FdAAAAQNoHXo2326dPHzt48GC82w4cOOBu0zoAAABAuhyHt2/fvlavXj2rUKGCdenSxY3BGwgE7Ndff7V33nnH8ubN60IvAAAAkC4Db8GCBW3hwoX26KOP2ksvvWRHjhxxy3PlymVt2rRxy84///zU2lYAAAAg9WdaK1q0qI0dO9ZGjRplmzZtcjOrlShRwrJnz57yvw4AAADE4tTCooBbvnz5yG4NAAAAEO2phQEAAID0hMALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8LavFuCFDhtikSZPCllWoUMHGjx+f5P3mzJljw4cPt+3bt1v16tWtf//+dtFFF6Xy1gIAACDWxHzg3bBhg+XPn9+GDh0aXJYrV64k7zNr1ixr1qyZ9evXz7p27WrDhg2zyy+/3FatWmX58uVLg60GAABArIj5wCvnn3++1a1bN9nr//e//7W2bdvagAED3PUmTZrYhRdeaG+99ZY99thjqbilAAAAiDXpooZ38eLFLrS2aNHCnn/+eTt27Fii6x46dMgWLVpkzZs3D2sRvuaaa1zLLwAAADKWmG/hPe+886xjx4525ZVX2pYtW+zpp5+2yZMn2/fff29Zs8bf/M2bN1sgELDixYuHLdf1pAKvQnRokN6/f3+EnwkAAACiIeYD7+DBgy179uzB66rFrVSpkk2YMMHuvPPOeOufOHHC/cyRI0fYcrXyercl1jlu4MCBEd12AAAARF/MlzSEhl0pV66clSlTxnVAS0ihQoXcz927d4ct37VrV/C2hPTt29f27dsXvGzatCki2w8AAIDoivnAG9fJkydt586drtQhIeqcpsuSJUvi1QHXrl070cdVi7BGcAi9AAAAIP2L6cB7/Phxe/bZZ+3o0aPuukoSHn30UTty5Ii1adMmuN6gQYPsjjvuCF7v1KmTvfvuu8FW2k8++cTWrl3rlgMAACBjieka3mzZsrkW3RIlStgFF1xg27ZtsyJFiti0adOscuXKwfXWr19vq1evDhuW7I8//rCKFSu6zmo7duywkSNHWp06daL0TAAAABAtMR14M2XKZE899ZSbQEIBVuPxKvDGpYB78ODBsLrfjz76yM2yprBbvnx5y507dxpvPQAAAGJBTAdej4YfC23Rjat06dIJLi9atKi7AAAAIOOK6RpeAAAA4FwReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK9ltRh3+PBhGzt2rC1cuNCyZs1qV1xxhXXo0MGyZMmS6H3efPNN++qrr8KWlS5d2t5444002GIAAADEkpgOvKdPn7Zq1apZs2bN7JprrnHhd+DAgTZhwgT78ssvLXPmhBuoV61aZXv37rU+ffoEl+XLly8NtxwAAACxIqYDb6ZMmWzx4sVWpEiR4LLatWtb/fr1benSpXbZZZclet9ixYrZzTffnEZbCgAAgFgV84E3NOx6QVb279+f5H1Xrlxpt99+u+XPn98aN25s7du3T7RFGAAAAP6V7hLgyy+/bAULFnStvInJli2bXX311dayZUurVKmS9e7d27X2BgKBRO9z7NgxF6JDLwAAAEj/YrqFNy51Xhs+fLhNnDjR8ubNm+h6zz77bFjNrmqAa9asaZ999pm1bt06wfsMGTLE1QcDAADAX9JNC686qnXq1Mneeecd13KblLgd1KpXr+5GaVi2bFmi9+nbt6/t27cveNm0aVPEth0AAADRky5aeCdNmuRqcEeOHGn33ntviu+vUgaN2qBSh8TkyJHDXQAAAOAvMd/CqzKEf/3rXzZixAjr2LFjguu8/vrr9sADD7jfT5w4YaNGjQqr1x08eLBrtb311lvTbLsBAAAQG2K6hVcdx9q1a+dGWpg8ebK7eHr06GHXX399cESGRYsWud81IYWGLHvqqaesYsWKrjThwIEDrv63Vq1aUXsuAAAAiI6YDry5cuUKC7mhqlatGhZ+NfuaaOgxtQYPGjTIVq9ebeeff74bqSFnzpxptt0AAACIHTEdeFVzm5zJI2rUqBFvWaFChezKK69MpS0DAABAehHzNbwAAADAuSDwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAX8tq6cD69evt/ffft+3bt1v16tWtU6dOljNnzojfBwAAAP4T8y28q1evtksvvdTWrFljFStWtJEjR1rTpk3txIkTEb0PAAAA/CnmW3gff/xxu+yyy2zChAnuevv27a106dI2ZswY12obqfsAAADAn2K6hff48eM2Y8YMa9euXXBZ0aJF7eqrr7YvvvgiYvcBAACAf8V0C+/GjRtdGUKZMmXCluv6/PnzI3YfOXbsmLt49u3b537u37/f0srpY4fT7G8h7aTlPhSK/cm/orFPsT/5F59RSM/7k/f3AoFA+g28R44ccT/z5MkTtjxv3rzB2yJxHxkyZIgNHDgw3vKSJUue1bYDnvyv8logstinwP6EWJU/St95Bw4csPz586fPwJsvXz73c+/evWHL9+zZk+iTOpv7SN++fa1Xr17B66dPn3b3KVSokGXKlOmcngfiH43pQGLTpk3B9ws4W+xPiDT2KbA/pR9q2VXYLV68eJLrxXTgVShSSP3555/txhtvDBuF4ZJLLonYfSRHjhzuEqpAgQIReR5ImMIugReRwv6ESGOfAvtT+pBUg2a66LSWOXNm1/nsvffes4MHD7plixcvdpd//etfwfU03m7//v1TdB8AAABkDDEdeL3aWk0YockjbrnlFrvuuuuse/fudsMNNwTXWbBggU2ZMiVF9wEAAEDGENMlDVKwYEH74YcfbN68eW7WtMGDB8crTejYsaO1aNEiRfdB9Kh05KmnnopXQgKwPyEW8BkF9if/yRQ40zgOAAAAQDoW8yUNAAAAwLkg8AIAAMDXCLwAAADwNQIvAAAAfI3ACwAAEINOnjzp5hHAuSPwAgAAxKDvvvvOrrjiCjfE6unTp91wqzg7BF7EpOXLl9vHH38c7c0AztnevXvt2LFjvJKIZ9OmTfbGG28k+cps3rzZ9u/fz6uXQV199dVWuHBhN1NsqVKlrFevXrZnz55ob1a6ROBFTNIpnB49etiJEyfClv/vf/+zBx54wCpVqmRly5a1Z555JmrbCCTl999/t8aNG7uJcAoVKmQTJkzgBUMYtdo9+OCD9scff4QtP378uC1atMhNmFS6dGkXeJ588klevQxIB0Q6aF65cqXNmDHDli5d6j5TkHIEXsSkdu3auVaNb775Jrjs3nvvdZcSJUrYuHHj7NVXX7Vhw4bZa6+9FtVtBUQHZ5999pm9/vrr9ueff1rLli2tW7dudvjwYXviiSfcjJA7duzgxUJQ3bp1rW/fvpYpU6bgMh3EX3PNNXbLLbfYs88+684O6GDppZdeshdeeIFXz8c0D5g+Qx566CF3gKPW/c6dO7sGoN27d7uSBpw9ZlpDzLrtttssS5Ys9umnn7rrCsD58uUL3q5Qcf/999uqVats48aNljVrzM+UDZ/RF5Bq7HTGQaceixUrZlu3bnWnqlu1amXjx4936506dcqto9Dbr1+/aG82orCfbNmyxUqWLBnvtl9++cVeeeUVe+utt9z1mTNn2nXXXWe33367ffLJJ8H1FHgHDRpkGzZsCPschH86p7Vp08bWrl3rzm7mz5/fihYt6vYFqVWrlt1www323HPPRXtT0y1aeBFTFF7//e9/W9OmTV1r2Oeff+5O54j3Ib9kyRIXLho1amQVKlRw6+lUD5DWdu3a5b6EtL8+/fTTrkOJTj3qNHS2bNmC6+nATfv12LFjeZMykDVr1rgW3DJlytgjjzwSdrB+33332Q8//GC5c+e2d955xxYuXOhu02fbRRddFC/UdunSxQ4cOMBnnU9pH9D+oNZcle21b98+GHZFnx8ffvghrbzngMCLmKH63AYNGtjFF19sAwYMcF8S3uk8j+ra9IWg1l+1og0fPtyqVq1KkECq04HXTz/95EoUPEWKFHGBV51I7rjjDrcsT5489vDDD7tgopZdj77A1q1b52rw4O+6XJVb1alTx6699lq3TKVZH330kTtNrc8z9bpXaZY+49QR6corr7QxY8a4dTNnzmx33XWX+zwMpQCs9dXCi/RJBywjRoxwBy86QN63b1/wtm+//dbtLwUKFEjwvuq09vfff9vs2bPTcIt9JgDEiBtuuCHQqVOneMsaN24cvP6f//wn0KpVq7B1ypQpE8iVK1dg//79abatyDi0X91///2B3LlzBwoUKBDIly9f4P333w/ePn78+ECWLFkCBw4cCC7btWtXIFu2bIGvvvoq7LEaNGgQ6NGjR5puP9LG//73v8CNN94YOP/88wN33313oEaNGu7zK1T16tXdfjFhwoSw5aNGjXL3O3bsmLv+008/BfT1PG3atOA6hw4dcvvepEmT0ugZ4UxOnjwZ2Lt3b7JeqJUrVwYuuuiiQJs2bQIvvvhioGnTpoEKFSoE73/77bcHbrrppiQfQ999DRs2DHz77beBxx57LN5+hKTRwos0NXXqVLvxxhutXr161rt3b3fEG3qaT3WOobTO999/b+vXr3fX1SqiU8a6n+ri1KlDPeBz5sxp8+fP591ExKlOXC2zqsFUCUOfPn1cLa5qd+XWW291rbratz3aJ2+66aZ4Zx50WpJB5P0pR44c7v3VfjJ69Gi336ged9u2bcF1dLs6N9avXz/svqrdPHr0qH355ZfuukZnqFmzpjsroJbhBQsWuDMIKuHS/obYoPfkscceS/A2jbzRtWtX9955773KW9TCr/IWlevpe2zo0KHudrXyz507N17HVn0vesPSqYO2WvoHDhzo9rfatWun+nP0lTMEYiBievfuHahYsWJg3LhxgTFjxgRKly7tWryOHz/ubr/22msD//73v8Puc/To0UDmzJkDzzzzjLv+zz//BKpUqeJa2ooUKRK46qqrAhs3bqR1F+fs999/D4wcOTLwwQcfBHbv3u2W7dy507W0zZ8/P2xd7atXX3118HrHjh0D119/fdg6EydOjHfmQfv66dOnebd82sLrtdDKnj17Ajly5Ai89NJLwWVbtmxxn2dxW/7ljjvuCLRs2TJ4/eWXXw5kzZo10KJFi0CjRo0C/fr1CzuLgOgbPXp0oG7dumEtvk888UTg448/DhQvXjzw7LPPBnbs2BHYtGmT+xxRa+6cOXMC9957r2vR1xmAKVOmuPvqc0L30ft95MgRt0z303fkzz//HLXn6CcEXqSKtWvXBkaMGBG8vm7dOvdBv2LFiuCy3377LZA9e/bAm2++6a6/9dZbgTx58rgPCI8+IPRBUalSpeAyfRgsWLAg8Oeff/LuISKGDBkSyJs3b6Bbt26B//73v8GShT/++MPtfz/88EPY+vpCUxnDwYMH3XV9ien61q1bww7W2rZtG/jrr794l9K5M5VLLV682O0n3v7gad26deDSSy8NW3bddde5A6S4VL6gz0PvYGvbtm1un5o1a1ZEngMiTwH377//Dvz666/BZXXq1HEHKp988klwmRpltH9ceOGFgZo1a7qDIN0vriVLlgRKlCjhwrAep3DhwoF3332Xty5CCLyIGIXToUOHBrp27epaK1SrplpGmTx5svuH1wdEqA4dOriaJK/1S8H2mmuucaH38OHDgc6dO7uWNNVPKnwAkaZAoZa4VatWxbvtxIkTgfz58wdeeOGFsOU//vij25+9MKtW21KlSsVbD+nb9u3bA82aNXMHQ17oVctt3IOY77//3u0PoS288tlnn7nlofuWzm6pFlefb3H3NQUi3cejv616YESHvq/mzp0bePLJJwN33XWX62Oig91Tp04F19Fytcp6Xn31Vfeex22QKVmypGvZjUutuDNnzgw7UNZ1nQXwDn4QGdTwIlnUM33OnDmJ3q4BsjVagnqgqz5XPdpVq+aNI3nhhRe6n6qFDNWkSRNbvXq1+13DOKnmTT3eixcv7sYhPHTokBuIW7WT5cqV491CxE2aNMnNiFa9evV4t2ls5zvvvNPVzoXWm2u0Bu2fGj5KNHHAqFGj3GQT8A99rqkGU59/EydOdMs0iYiGRNQoMaFjqErcscCbN2/u6rlDa7lbt27tRu9QDWco3Vd1n6H7UIcOHdz+GToyCFKf/r9VZ6txk1Wjq/4iDRs2dP/nqsXVGNveCCy6rlE49B0l+rzQe6nHCKXJJDSsWOgQmsuWLbObb745bKgx1eZq4hH1dWFGtQiLUHCGz+m0nHYXHb0mpEuXLq6uMfTIWC219evXD7ZeFCtWLNCzZ8+w+73++uvuyDeUWst0dKyWFOBcqUXmwQcfTPR27btXXnllkq186l192WWXubrcYcOGuVONKsGBP6xevTrQp0+fwC+//BK2fNGiRYGyZcu60RfUX8D7LFPt5SWXXBLsYa8WOZVsJUQjfKg2M7RVsH379u5yJmoF1lkzRqBJW7169XJnfeLuD/LFF18EMmXK5EZa8L7r9N2m7zKPRluI+/5qPbXwqkxF34u1atVy/Vi+/PLLNHhGEAIvkqV8+fIuGOhDIKFhcVRYry+MUOroo5Ds1TfpA0FlDgoNohIFdWIbMGAA7wIiSl8iTZo0cfutgolqIxM7gFKNnDqXhdaOi+oxvUCjGjzVXaqDSrt27dxpTviHOsLqs03hVgc4nnnz5rkyq48++siFHO0Hos5jCiw6qFcp1jfffOPun1hnNn0OzpgxI7hMnW9DAzBii/qaxH3PQmkIsaJFi7qDHy8g64A49CBb/VHi1nR7Q85pf1IplcoXkHYIvEgWtcLqyFa9UhUOFi5cGHb7rbfeGrjtttviHdGq9k2dgDx9+/Z1obdgwYKujq1///588COiNDal9i196eig6tFHH3VfXhqBISEKL/ryuvPOO4M15goxGjFE413C/9SbXh2FtA/Uq1fPjXkrCiXVqlVzLa3ap9S50aNORxoD/J577nEHWOedd16ij6++CgrPSD80ZrLet4Toc0GfKd57unz5cnfdaxH29pexY8em6TYjaQReJItO2XhHu/py0Cnd0J6p77zzTrwRFkTDh6nVJHQoJrW0aZgVjm5xppE+vCF7EqOe7N6wdh71btbQQHEPyC6//PJEH0ejLOgLSuFGpyI1ILzOaCBj2LBhg2vB1SQiarW95ZZb3IH4119/7XrVi05HX3zxxfH2UR28a9gwfdbBP55//nl3EOMd/IRSy60CbmhZk84kaeg4j/YXHUAhdtBpDcly/PhxN7mD12lDg+qrqH7nzp1u2d133+2mWdWUiVrXmxu8aNGi9tdff4VNp3r++ee76YNVnA8kRhM7aH/yOgR51MFDnUQ0SYk6Q2q/C51uUwO3FytWLOw+mupXnY80iLtH99H0ntK0aVP79ddf7cEHH3QD/o8bN87efvtt3hwfWrFihetMu2rVquAyb3rf8ePHu05i2k/UyUgdb9WZ1utAtmbNGtfRyFOlShU34ciPP/4YXA/+oOmd1VlQnabj0vTPkjt37uAydV7T54YaEkXTS+s6Yki0EzfSB5UmqG5RtUfqwKHWMLV2qG7JOwJWxw91zrjgggtc3VvVqlVdC4haeL2xdoHk0pA8mko6tPb2X//6l7tUrlzZnU7UqUOto05l3mDtGsYu7gQmuk0fd6H14hoLUx2NNBkA0re3337b1cqeqRVXrfz6PFKnM52RUolV6PS+qvXWUIpaV0OEafpXtd6GDj330EMPxXts7Uv67IO/aMzkuNNDe0PLxa37V4mLzhDEHXoTsYPAi2RRh4ycOXO6kRg0E5XqHnVRqNXpYq8Dhpapnk0d1rRMF5VDMHg24lKHMJUPbN68OdEXR/uReql7VEOp4KovHI/2udB6uffee8/tq6GTQCjU6n4qVQgth3jllVeCHdOQfmkcVI2HGkoH4gqxOmDS55BKE9SfwPusUsck9Sf47rvv3PV9+/a5EPPGG2+468uWLXOntEPrOBWQNcOj11nJo4lKQifHgT/ocybuhDJq9NHY3JoxFOkLgRfJopawhFpQVIursOHNTCVei69+du/e3bX4aopWIJRazNTpR7VyoTRIvzeVtAbhV3gNHS1BtZaayCSU6uW8qX3VwlKjRg3X+Uj7p2b00zBB6lSp0KszEfCXTz/91E1OEzr1rjoraqYzbwYr1dgq7GqEBHVg1IQ3Cq+hB+OaMMcbSlHiDgemfUn7a+ikEdqPNYJH6OQD8AfV6npnAnRwrBp/HTjFnWoc6QOBF2ekAKGgsHTp0gRv904le3r37u1a0vQF07x587DObUAodfJQb+jQfU1DRGk0ENHMVeoUFBpKNB7qfffdF/Y4s2fPDmuJ0alGraf9Vr3v9YWlzm1qsUtoWD2kb/oMUqub1/KvFn0FFe/gZvr06a71tk2bNu5zScNKadSZuC21mt43dCjF5NCBlEIQB1L+pPIor6VfnVuRfhF4cUZqzdCXQEr+2XV6MO6XCRCXhvHRvrVy5Up3XWcKFHhD6+BUoxs6MYRKFhRiQ6dxVSubhs7zBoP36CyDdwp7/fr17m+dqdYT6ZOmIVfNpbfPhA78r+lb9d5rAhJ9NsWtFVd5i+gzS0PUaSrZ5PKmT4c/qTY3oZEakP4wSgPOSL3i77jjjhRN7ZsvX75402wCcVWuXNlNRa2pV9UjfuDAgW7khCxZsoT1fp43b55t2LDBXW/Tpo0dPXrUvvzyy+A6mvJTvaqXL18e9vhLlixxU8B+8cUXblQRTQnaoEED3ogYd+TIEVu7dm285fv27XP7Su/evW3kyJF27Nix4G0aRWHWrFluNI4PPvjABgwYELytRIkSdu2119rcuXPdvuLZvXu32yc0+oLoM0v7laaTTS5NHQz/0ogvoaMxIP3KpNQb7Y0A4G8a5unFF1+0devWuSHp+vbta8WLF3e3DR8+3IYMGWJ9+vSx9957zw3xFBpKpGLFinbPPfdYv379gvPVKxRNmTIluI7mtg8NyvL666+78KPh8Vq3bm333ntvcEghxKZp06a5gxcNOeeF3l27dtkDDzxg06dPtyZNmrj9QQcxpUuXtm+//dbtL/oq00H5/v373VB2gwYNss6dO1uuXLncY/z222/WuHFjNyyi9h895oQJE9w+peHoAPgbn/wAUpVa3erWrWsXXHCBa4X73//+51p1t27d6m7X2QON56zxnf/++2976aWX7MCBA2GP0b59e9ey5+nUqZMbfzdU3LAr3bt3d2NAK0TpPoTd2Pf7779b3rx57ZdffnEt9FKgQAG77rrr3IHT559/7vaROXPmuJZ/jZsrCr3aT9Ti+vzzz9uwYcNcIH722Wdt7969LiT/9NNP7oyBzhYULlzYtQYTdoGMgRZeAKmqfPnydt999wVPEx88eNBN7nD55Ze71le55ZZb3GlDTWiisLJlyxYXRDT4v4KJgo4G+V+9erWbcALp26FDh1xr/ldffeUmqFHJgUdnAlauXGnz5893+4WCayjtC1qmSSJUBqODJ01EIpo8RGUyKm2pUaOGC8NDhw51yzX5yFNPPZXggREA/6OFF8A506lmtaCqBVetqR61rCmg1KlTJ7jsvPPOc+H3008/dTMZiVrdVDupsgO1wo0ZM8a14KmFTqFYp6o1gxph1z/9AlTCsmjRImvevLkrQ/BopkbNwqiyBoXa0Jn2dL1WrVqWPXt2d6ZAdbwzZ860bdu2udu1f1x22WVu/1Frftu2bV0Lv84yPProo4RdIAMj8AJIklrHRo8e7cKGOovFpel/e/To4Wpz8+fPb7feeqs7jSw6Na0pqXWaOtTVV1/tHkuPLWrJ09SsapHTqekWLVrY999/76al1rTV3mlt+IP2C+0nav3XtL7qiOgFW286Xx08qdTFa71VjbbOFCjk6ixA2bJlXVmLln/00UfBx9b94nZeVCuwOtICyLgIvADiUX2tTi3rtLB6t0+ePNmVGFxxxRVhLW4KIx9//LE7/fzII4+4TmKvvfaaO3WsDmo6fazaSwXmUN5j5MmTx/1Ui55aiNVrPlTcOl34h4LpsmXL3Mgce/bssa5duwZbeBV4vRE81FrrtQrrjIC3z4gOwtQJLbS+W6FYZwcAIBQ1vADCqLe7airVyUwBVj3bRaeGFUDUUchb1r9/f1eHqZEVPGpxq1ChgrVr186ee+45d9pa9brqNa/T2Ao0HTt2tD/++MPdhoxJ+4n2M42koBEYNFyc9gvVeOs2dUzTCB4qf1HJgs4e6HaNyqDRNrTv6ABJNcCq+dZZAgBIDC28AMKopEBhVWFCQdWjYcRUF6lOZB61rm3cuNGFZI9ada+55hrXeicKMu+++64LvAo4Gm5KQ0Kp1RgZl/YTDQ82btw4N2ycDpxeeeUVN9ScWni9ETwUfjV8mIwYMcJ1PtP+o1KXxYsXu/pwwi6AMyHwAohHncg2b95ss2fPdjWVn332mV1//fWuQ5A6lSmEiJYpGKvTWtgHS+bMrkzBoxY5tcIpuKxYscKt743Di4y9n61fv96VxKgGXAdB2rd0JkF0cNWsWbNgWYP2KXU+U/DVEGReMAaAM6GkAUCCLrnkEhc+FGg1JJg6kqn1V5NEqAVXoyyIhhJTeYJKHdRSpwCjcXdVA6zJIoCkVK9e3erXr+/OAiREB0iq361duzYvJICzRuAFkCCNX6oZ0VatWuXCr0fDhCnIalIABVsNF6Zpe9VJTeFFEwZowgdNEQyciUZcGDx4sKvT1YgeAJAaCLwAEqQSBA0ZpYCr08cezVJVpkwZ++STT+z22293y9QCp1mrNJ6qanY12xWQ3P1M+5NG/NCZAwBIDQReAInSkGRZs2YNjoUqr776qht2bO3atdThIiI03q5GBQGA1JI11R4ZgC86FWl8XE0AoR7xqrPUzGmaTY1OZ4gUwi6A1EYLL4BEaUxUdUTTRBFNmzZ1kwWojIHe8QCA9ITACyBJGh+1Vq1abvxcAADSIwIvAAAAfI2JJwAAAOBrBF4AAAD4GoEXAAAAvkbgBQAAgK8ReAEAAOBrBF4AAAD4GoEXAAAAvkbgBYAYp2mdFy1aZOnJjz/+aN9//320NwMAnKz/9wMAEC2BQMB++ukn++uvvyx//vxWtWpVN6WzZ8SIEW565wYNGqSbN+m9996zzZs32xVXXBHtTQEAAi8ARNO8efOsa9eutn//fqtbt64dOnTIfv31V2vcuLG98847lidPHt4gADhHtPACQJQsW7bMrr/+env44Ydt0KBBljXr/30knz592iZMmGBHjhwJC7yHDx+25cuX28GDB61+/fpWoECB4G1r1qyxVatWud/VSly9enUrUaJEvNIItSbXqFEj0cdJzjoetUr/8ccfVrJkSatZs6ZlyZIlVV4nADhXBF4AiJJ+/fpZxYoVbfDgwZY58//rUqHf27VrF7buzz//bHXq1LHSpUvbtm3b7O+//7a5c+dalSpV3O3r1q2zKVOmuN/37NljCxYssCeffNIef/zxsNKIlStX2tGjRxN9nOSsc+DAAbv99ttt9erVVqtWLdcinTdvXvviiy+sWLFiafLaAUBKEHgBIApOnDhhc+bMsd69e4eF3cQo0KpFWPW9aoG99tpr7YUXXrBRo0a521u1auUuHrXOqua3bdu2Vq5cuWQ/TnLWefTRRy1TpkyudTd79uyuRVp/R8vHjRsX4VcKAM4dgRcAokCtsMePH7dSpUola/2mTZu6ACoKm02aNLEZM2aEraM6YAXdHTt22KlTpyxfvnwuuIYG3uQ8TlLrqPOcQu39999vn3/+uQvEuqh8YuLEief4qgBA6iDwAkAUnHfeee7n7t27k7V+wYIFw67nyJHDlR14Jk2aZJ06dbLy5cu7EK3bjx075sJvSh7nTOvo8VRLvGLFCtu0aVO8oAwAsYjACwBRoM5o1apVi9j4ut27d7dnnnnG/fQULlzYtb5Gkmp11erbpUsXV8cLAOkBE08AQJT06tXLpk6d6mp541LLr0ZpSA6VL+zatcsqV64cXPbdd98lu/U4pYG3UaNG9tZbb8UL01u2bIn43wOASKCFFwCipGPHjvbLL7/YTTfdZB06dHCdzDQOr0Y/+Pbbb13rb65cuc74OBoO7Oabb7aHHnrIevbsaTt37rTXXnvNcufOnSrb/eabb9o111xjV199teuspnKHWbNmuVrh119/PVX+JgCcCwIvAETR888/b//+979t8uTJbugvjaGrCSheeuml4Bi8GgdXIyGEuvjii+2GG24IXldHMg0ptnDhQitUqJBNnz7dLatUqVJwneQ8TnLW0Ri9GiZt9OjR9sMPP7jSiR49eoStoyHUypYtG5HXCADOVaZApAu8AAAAgBhCDS8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAAPA1Ai8AAAB8jcALAAAAXyPwAgAAwNcIvAAAADA/+/8AVndzPhgx1vUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "channel_sorted = channel.sort_values(\"cancellation_rate\", ascending=False)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "ax.bar(channel_sorted[\"channel\"], channel_sorted[\"cancellation_rate\"])\n",
    "ax.set_title(\"Cancellation Rate by Booking Channel\")\n",
    "ax.set_xlabel(\"Channel\")\n",
    "ax.set_ylabel(\"Cancellation Rate %\")\n",
    "\n",
    "plt.xticks(rotation=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7190a3f2",
   "metadata": {},
   "source": [
    "### Booking Lead Time by Channel\n",
    "\n",
    "Lead time measures the number of days between booking creation and check-in. Differences between channels can help with forecasting and inventory planning.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0763bcd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channel\n",
       "Booking.com    19.82\n",
       "Agoda          19.63\n",
       "MakeMyTrip     19.51\n",
       "Direct         11.54\n",
       "Name: lead_time_days, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_time = (\n",
    "    bookings.groupby(\"channel\")[\"lead_time_days\"]\n",
    "    .mean()\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "\n",
    "lead_time.round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e8a45d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V3/O///3P9DfV/dUewa49b4cNG2Yu0erv0lmgNKulQbzW7+klc9cPfE/sl2a0rQBXPww1qNJZ1po3b26OlWX48OHy+++/S926deWDDz4wZRuandVLtxrMWuUTsV0vYkZcA8bMmTObTKn2sI1YjhJfcTmesT1OEctkNEDTyTH0qoFmebW2Uusntdeu/g7tCavraZCl+6NZQy0d0TIIndVLv2S5BnEa9Ovx1wBPAzgNmrWP9Isvvmj23ZfpPupxat++vSkb0Npt/VKjVzL0/zs9TzUoi0m9evWcf7t+adPAVM8NnR0uIq2Lt845LWPS7OqD6Guu/29oDb+eB/ocer7qrGqaidVzUXs7a2lPXGmNtgabbdu2lQ4dOpirJF999ZV5X/FEzXhiHH/A0wh4AQ/TbE2tWrWcA6SiorMnaUCoA4w0AFH6waQZGM2o6YdfRBoAaxZp0aJFJtOkWVXN5OhAHQ0QrcvQGsRozaZV2+hKPzz1cr8+h9bx6iA5DcS07lCDVIsGiDq4afz48SZo1Zmy3njjDROMjh071mRJ47pfUdH1dF812LYCbv3A1+zijz/+GKkOUvdDg2sNxjRQ00BE65w1qHMdzBTb9aI6VmPGjDF1hvpFQOsstT4zqqmFozvOuv+63HVQUFyOZ2yPkysdoKTBmQa1eoVAX1MNUPVSu26nteEWDfo1o6vHRYMQveytQZ1OZ6uD2Cz6hU2Pl37x0JseCx1sqeeoVRoSW1rLrVc1IrKOlesXEP0yoMsi1opqCYYut0pdLFpeoMv1mLrSGla9SjJv3jwT7Olrp2VGmrWOOMWxK+v361UCPaa6rWZ3NSOrpQpanhKRvta6vgaZDxqs5kq/ZPzyyy/m/xl9XfR81asi+rpp0KpXG6zjEN1xsQbYudbFauCs54leRdJzTv9uPZ+13EL/v7BKJOLynHFZN7bHP6rXH0goftqqIcGeHYCt6IdYnz59zOXRqAIYcDyTKw3c9MuuzsBnXbUB4DsIeAFESbOirhlNrQfU0gT9jhxVvS5ixvG0L/0CqBlOzcp6uv4bgGdQ0gAgSg0bNjT1sxrk6iAnrSnUGlK9XI6443jajw5C1LrUjz76yHw5HDJkiLd3CUA0yPACiJIOeNJsldb6anZSW4/paPTYDMgBxzM50EFnWuajdfE6ECvipAwAfAcBLwAAAGyNPrwAAACwNQJeAAAA2BqD1qKhc43rgATt8RjbpvAAAABIPNo56Pr166Y3uk4gFB0C3mhosKvNvwEAAODbdEbBmCY6IuCNhmZ2rQOoM/wAAADAt+iMlZqgtOK26BDwRsMqY9Bgl4AXAADAdz2o/JRBawAAALA1Al4AAADYGgEvAAAAbI2AFwAAALZGwAsAAABbI+AFAACArRHwAgAAwNa82of33r17smTJEtm8ebOkSpVKatSoIc2aNYu03okTJ+Sbb76RCxcuSJkyZaRz586SNm3aGJ87PtsAAADAfryW4b1//76UKlVK5s2bJ3nz5jUzZHTp0kVeeeUVt/UOHjwoTzzxhBw6dEiKFSsmkydPljp16sidO3eife74bAMAAAB78nM4HA5v/GL9tf/88488+uijzmXr1q0zgenu3bulfPnyZlmTJk0kPDxcVq1aZe5rxrZgwYLy1VdfmaxtVOKzTVRT1QUEBEhISAgzrQEAAPig2MZrKbw5BZxrsKuKFi1q/v3vv//Mv1bQ2rp1a+c6OXPmlLp168qyZcuifN74bAMAAAD78qlBa1OmTJEMGTJI5cqVzf1Tp06ZMoRChQq5raf3jx07FuVzxGcbFRYWZr4luN4AAACQ9PlMwKvZ11GjRpmyg8DAQLPs9u3b5t/06dO7rav1vtZjEcVnGzV69GiTErdu+fPnf+i/CQAAAN7nEwGvliC8+OKLMnbsWGnfvr1zuVWLcfXqVbf1g4ODTVAalfhso4YMGWLqP6zb6dOnH+pvAgAAgG/wesC7evVqadGihQwfPlwGDBjg9phmWTVIDQoKitSFoXTp0lE+X3y2Uf7+/iZYdr0BAAAg6fNqH94//vhDmjdvLsOGDZOBAwdGejxFihRm8NmMGTOke/fupr5327Zt5vbBBx8419N+u1qfO2LEiFhv44sKDf7Z27uABHBiTBOOKwAAybEt2fXr1yVXrlwmIG3UqJHbY506dZJatWo5SxHq168vV65cMRNIrF27Vjp27CifffaZc33t37t161aTxY3tNr7YloyA154IeAEASBixjde8luFNkyaNGaAWldy5czt/zpIli2zfvl3Wr19v+unqwLaIpQkaILvO0BabbQAAAJA8eC3D6+vI8MJTyPACAJBMM7wAEhYlMvbFlygASGJdGgAAAICERIYXAPBAXDGwL64YIDkgwwsAAABbI+AFAACArRHwAgAAwNYIeAEAAGBrBLwAAACwNQJeAAAA2BoBLwAAAGyNgBcAAAC2RsALAAAAWyPgBQAAgK0R8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFsj4AUAAICtEfACAADA1gh4AQAAYGsEvAAAALA1Al4AAADYGgEvAAAAbI2AFwAAALZGwAsAAABbI+AFAACArRHwAgAAwNYIeAEAAGBrBLwAAACwNQJeAAAA2BoBLwAAAGwtlfiArVu3ypEjR+SZZ56RXLlyuT02d+5cuXPnTqRtihcvLtWrV4/y+bZs2SJ//vmn27LMmTNLixYtPLznAAAA8HVeDXhXrFghgwcPlnv37snBgwdlzZo1kQLeDRs2SGhoqPP+9evXZfHixTJixIhoA95Zs2aZ565Tp45zWb58+Qh4AQAAkiGvBrwa6M6cOVOyZcsm+fPnj3KdyZMnu92fMmWKLF26VDp06BDjc1eqVMk8NwAAAJI3rwa8TZo0Mf+eOXMm1ttMnz5dGjVqZDK2Mbl8+bL88MMPEhAQIBUqVJDs2bM/9P4CAAAg6fGJGt7YOnDggOzYscNkeB9Ea3jnzZsnZ8+elaCgIBk/frx069Yt2vXDwsLMzXLt2jWP7TcAAAC8J0l1adDsbp48eZyZ4ei0a9dOTpw4YWp9t23bJuPGjZNevXrJ3r17o91m9OjRJhts3aIrsQAAAEDSkmQC3vDwcJkzZ4507NhRUqZMGeO6Tz31lKRJk8Z5v0ePHqZLgw5ki86QIUMkJCTEeTt9+rRH9x8AAADekWRKGpYsWSLBwcHSuXPneG2fLl06U9cbHX9/f3MDAACAvaRISuUM9evXl8KFC0d6bPPmzc663vv375u6XVda1qAZ26pVqyba/gIAAMA3eDXDe/ToUdm4caNcuXLF3NeSA629LVeunLlZTp48KatXrzaD0KIyY8YMM3mFTiyhrc4aNGhgevCWKlVKTp06ZVqbtWzZUp577rlE+9sAAADgG7wa8J4/f17Wrl1rfta+unpfb4GBgW4B799//21qd6ObKU1rdq0JK1KnTi27d++W7777Tvbs2WNqdxcuXGiCYAAAACQ/Xg14a9SoYW4PoqUMeouOBsOu0qZNK506dfLIPgIAACBpSzI1vAAAAEB8EPACAADA1gh4AQAAYGsEvAAAALA1Al4AAADYGgEvAAAAbI2AFwAAALZGwAsAAABbI+AFAACArRHwAgAAwNYIeAEAAGBrBLwAAACwNQJeAAAA2BoBLwAAAGyNgBcAAAC2RsALAAAAWyPgBQAAgK0R8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFsj4AUAAICtEfACAADA1gh4AQAAYGsEvAAAALA1Al4AAADYGgEvAAAAbI2AFwAAALZGwAsAAABbSxXXDfbv3y/z5s2T9evXy5kzZ8yy/PnzS61ataRNmzZSpkyZhNhPAAAAIGEzvBro1q9fX5588knZuHGjVKlSRXr27GlulStXNgFwxYoVpUGDBnLgwIH47Q0AAADgrQxvo0aNpH///ia7my1btijXuXTpksyaNUueeeYZOXv2bKx34vbt23LhwgXJlSuXpE2b1u2xy5cvy/Xr192WpUmTRvLkyfPA5w0NDZWrV69Kjhw5JEUKqjcAAACSo1hHgX/99ZcMGDAg2mBX6WO6jq4bG8eOHZO+fftKwYIFpXDhwrJ169ZI6wwdOlRKlSolderUcd46duwY4/Pev39f+vTpI4GBgVKiRAkTHC9cuDBW+wQAAIBkGvCmT58+2sfu3bsnO3fulPPnzz9wXVfLli0zwe7vv//+wOzyiRMnnLeVK1fGuP64ceNk7ty5Zp80w/vee+/JSy+9JEFBQbHaLwAAANhHvK7za71uly5dnPdbtGhhansLFSokv/32W6yfR7O7/fr1k8yZMz9w3YsXL5rSh9iYOHGi2b/SpUuLn5+fqTPWfZs6dWqs9w0AAADJOOAdPHiwdO3a1fy8a9cuU4pw9OhR+fjjj+X999/39D7K4sWLpWTJkhIQEGAGyO3YsSPGwPjUqVNSvXp1t+VPPfVUjNsBAADAnuIV8O7bt0/Kli1rfl69erU8//zz8uijj0qnTp3k4MGDHt1B7Qahvy84ONjcHnvsMWnYsKGcO3cuyvX/++8/82/WrFkj1Rdbj0UlLCxMrl275nYDAABAMg14s2TJIocPHzY/L126VOrWrWt+1oBSH/MkHaBm9fbNkCGDTJkyRcLDw2XRokVRrm91Y7h7967b8jt37kjKlCmj/T2jR482GWTrpr2FAQAAkEwD3nbt2pnWY9WqVZN//vlHmjRpYpYvX77c1PMmJG1bljt3bjN4LSr58uUz/1oD6Cx6P2/evNE+75AhQyQkJMR5O336tIf3HAAAAEkm4B05cqR88skn0qxZMzMJRcaMGc3yK1euyDvvvOPRHdQWY67+/fdfOXnypCmhcO3Vq8uV7kuFChXcOjlodlc7QdSuXTva3+Pv7y+ZMmVyuwEAACAZTi2sFixYIK1atTJBoqu4Brs3btwwk1VY2Vj9VzO32j9Xb1pXq1MWa29f7cWrg9G0L2+BAgWkbdu2zucZNGiQGThn1Q9rGzLdP535TbPQ48ePN+UMPXr0iM+fCwAAgOSW4dXBaTqZwxtvvGEGlMXXihUrzEQSbdq0Mf14tfuD3p85c6Z5XAPqadOmmVIJXUfrbLUnr3aGsLLK1oA013IFLavQGeHmz59v+u9q4Kyt1GKaNAMAAAD25OdwOBxx3UgzsxpMzpgxQzZv3mxKCDQIfvnll2PVUzcp0C4NOnhN63kTq7yh0OCfE+X3IHGdGPN/Ne6JjfPJvrxxTnE+2Ze33qOAxIzX4pXh1W4JnTt3lk2bNsmRI0ekfv36pq5Xs76aUdVWZQAAAIAviFfA66pEiRLSvXt30z5MB5j9/PPP0rhxY9OnV0sPAAAAgCQZ8Oo0v3PmzDE9eLVjwrp160yPXB14pi29dLCZljgAAAAASa5Lg2Z0dVBYmjRpTE/eiRMnmql/LenSpZMJEyaYxwEAAIAkF/DqZBNff/21PPvss9EGtalSpTKdEQAAAIAkF/D+9ttvsVqvRo0a8Xl6AAAAwHcGrQEAAAC+LF4Bb2hoqLz//vvy+OOPmxZladOmdbsBAAAASTrg1al7Fy9eLG+//bbcvHnTdGvQWddSpEghAwcO9PxeAgAAAIlZw6uzrC1btsz02tUuDS1btpRWrVpJxYoV5csvv4zvvgAAAAC+keHVPrulSpUyP6dPn95M56aaNGkiO3fu9OweAgAAAIkd8DocDkmZMqX5uWjRorJq1Srzs86sFtM8xgAAAECSCHgDAgKcP/fr10/at28vlSpVkkaNGknXrl09uX8AAABA4tfwXr161flzhw4dzNTCW7duNbOtNW3a9OH2CAAAAPB2wBvVBBNMMgEAAADbBLzHjh2Tn376SY4fPy5+fn5SuHBhad68uRQpUsTzewgAAAAkZsA7duxYGTp0qAl08+bNa5adPXvW9N8dM2aMDBgw4GH2BwAAAPDeoLW1a9fKu+++K+PGjTN1vCdOnDA3/VmD3SFDhsiGDRs8u4cAAABAYmV4J0+eLIMGDTKdGVxpL17N7F6+fFkmTZokNWvWfJh9AgAAALyT4dVODG3bto32cW1PpusAAAAASTLgPX/+vBmgFh0dtKbrAAAAAEky4A0LC5M0adJE+7g+dvv2bU/sFwAAAOCdLg1axwsAAADYMuD19/eXvn37PnAdAAAAIEkGvKGhoQm3JwAAAIC3a3gBAAAA2wa8DRo0kG3btj1wvS1btph1AQAAgCRV0tCmTRtp1qyZmU5Y/61YsaLkzJlTHA6HaUW2Y8cO+emnn+TixYsyatSohN1rAAAAwNMBb+fOnU3QO2vWLJk3b56MHTtWwsPDnQPVqlSpIq+//rq0a9fOzLwGAAAAJLlBaxrI9uzZ09zu3r1rsrl+fn6SPXt2SZUqzh3OAAAAgAQX7yhVA9w8efJ4dm8AAAAAD6NLAwAAAGyNgBcAAAC2RsALAAAAWyPgBQAAgK3FO+C9dOmSTJ8+XYYOHepctnXrVrl3716sn0PXXbJkiTzzzDNSqFAhs31Ex44dk969e5u+v9r6bMCAAeZ3x0T3SZ/P9dawYcM4/oUAAABItl0a9u7da2ZT03Zkhw8flpEjR5rlM2fOlEOHDkmnTp1i9TyDBw+Wv/76S1q3bm22CQ0NjRQQN2nSxAS8HTt2lFu3bslbb70l9erVk+3bt5v+v1G5fPmyPP744zJx4kTnsjRp0sTnTwUAAEByDHg1y9q/f38ZMmSI6cNr6d69u5l4IrYB7+jRo017szNnzkT5eMqUKSUoKMj8a9GgukSJEiYbXLt27WifO126dCazCwAAgOQtXgHvzp07TSmCcg14ixUrZjK2sf7lsZiswjXYVVbJRMTlEW3YsMFkeQMCAqRmzZqmzEF/BgAAQPISr4BXg83r169LxowZ3ZYfOXJEsmTJIgnpnXfekSJFikjlypWjXSdr1qxmPc0Anz171gS7v/zyi+zatSvaMoiwsDBzs1y7di1B9h8AAABJYNBa48aN5YMPPpD79+87M7wnT56UHj16SLNmzSShaBC7cuVKmT9/fow1uR9++KGp+y1btqw0atRIli9fbjLP8+bNi7G8QjPA1i1//vwJ9FcAAADA5wPe8ePHy5YtWyR37twm6C1TpowULVrUDDrTwDEhjBgxQiZMmGAytZUqVYpx3RQp3P8snQK5YMGCZkBddLQeOSQkxHk7ffq0x/YdAAAASaykIWfOnKY8YOnSpaaeV4Pet99+W1q2bJkg3RC0C4QG0j///LPUqlUrzttrIH7u3LkYyy201CG6cgcAAAAks4BXaWD7wgsvmFtCGjNmjAl2NbMbXVcGbW+2Z88eU+4QHh4ugwYNMhnbHDlyyI0bN6RXr17icDhM+zMAAAAkLw8109rNmzdNS7GIt9hauHChaR1WrVo1c79NmzbmvpYuqODgYBO4qg4dOrhNJKF1vBadiEIHp6nUqVOb8gote9A+wTqA7ejRo7JmzRralAEAACRD8Z54QieC0H+jotnU2NAZ1qKqxw0MDHT+e/z48Si3zZYtm/PnsWPHOjss6CA6zejqTSegyJQpkwmCAQAAkDzFK+DViSVKly4tkyZNcgan8ZEhQwZzi2nwWWwmj9AsblyWAwAAIPmIV8Cr3Q7WrVsXqQ8vAAAAYIsaXp1R7dSpU57fGwAAAMAXMrxaM/vqq6/K+++/L48++qjb9MKqZMmSnto/AAAAIPED3nv37snhw4ejnVUttoPWAAAAAJ8MePv27SuvvPKKmb73YQatAQAAAD4Z8J4/f95MLxxThwUAAAAgyQ5a0xpdncwBAAAAsGWGt2XLlvLSSy/J8OHDzaxmEQetlStXzlP7BwAAACR+wDt06FDz74svvhjl4wxaAwAAQJIOeP/77z/P7wkAAADgKwFvtmzZPL8nAAAAgDcD3o0bN5p/a9So4fw5OroOAABAdAoN/pmDY0MnxjSRJB3w1qxZ01mfa/0cHWp4AQAAkOQC3tu3b0f5MwAAAGCLPrxp06aVtm3bOn+O6QYAAAAkyYknFi1alHB7AgAAAPjKTGsAAACAbduS3bhx44HrZMiQIb77AwAAAHg34M2YMeMD16FLAwAAAJJswPv9998nzJ4AAAAAvhDwtmnTJiH2AwAAAEgQDFoDAACArcUp4PX390+4PQEAAAC8HfCGhoYmxD4AAAAACYaSBgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFuLdR/eZ599NtZPumTJkvjuDwAAAOCdDG+hQoWct0yZMsnSpUvl2LFjkjlzZnM7evSoWaaPAQAAAEkuwzthwgTnzy+//LJ88MEH8v7777utM2zYMPnrr788u4cAAABAYk4trFavXi0TJ06MtLxPnz5SokSJh9kfAAAAwPuD1sLDw+XQoUORlgcFBZnHAAAAgCSd4e3QoYO0atVK3nnnHXnyySfF4XDIzp07ZcSIEfLqq696fi8BAACAxAx4x48fL9mzZ5d3331XgoODzbKsWbNKv379ZNCgQXF6rgsXLsg333wjR44ckcGDB0vJkiUjrXPixAmzjq5bpkwZ6dy5s6RNmzbG543PNgAAALCfeJU0pEqVymR3L1++LOfPnze3S5cuydChQ81jsfXFF19IxYoV5Z9//pFZs2aZ54no4MGD8sQTT5gSimLFisnkyZOlTp06cufOnWifNz7bAAAAwJ4eeuKJnDlzmlt8NGzY0AS77733XrTraMa4cuXKsmDBAhkwYIAZMLd3716ZPXu2R7cBAACAPcWrpEHdunVLtm7dKqdOnZK7d++6PdalS5dYPUfx4sVjfFwHwK1atcqtI4QG13Xr1pVly5aZMgVPbAMAAAD7ilfAu2/fPmnatKncuHFDrl69agJKrZVVBQoUiHXA+yAaTGsZgk524Urvb9iwwWPbqLCwMHOzXLt27aH3HwAAAEm0pKF///7Srl07uXLlirmvtbc6SKxGjRoeC3bV7du3zb/p06d3W54xY0bnY57YRo0ePVoCAgKct/z583vgLwAAAECSDHh37dolb775pvnZz8/PlBEULFhQpk+fLlOnTvXYzlnTFGsW2ZV2htCg1FPbqCFDhkhISIjzdvr0aQ/8BQAAAEiSAa8GhFmyZDE/a3uys2fPmp9z584tFy9e9NjOaZZVg1Sd0CJiF4bSpUt7bBvl7+9vgmXXGwAAAJK+h+7SoGUM2o9XB7C99dZbUqpUKc/tXIoU0rp1a5kxY4apF1bbtm0zt5dfftm5nvbb1TZpcdkGAAAAyUO8Al7tt2sZO3asyaZWq1ZNfv31V/nqq69i/TwbN240M7NpTbAaM2aMub9kyRK32lqdMEInj2jevLk0aNBAevfubVqaWTZt2hTnbQAAAJA8xKtLg04hbClatKjs2bNHQkND4zyTWa5cucyEEKpx48bO5a4dFrR0Yvv27bJ+/XrTCWLUqFGRShM6deokzZo1i9M2AAAASB7i3Yc3ovhM26vBst4eRGdv0z660alevXqctwEAAEDyEK+Shvv378uXX34p5cuXd+t8oDOcnTlzxpP7BwAAACR+wDthwgT5+OOP5bXXXnOboKFkyZLy4YcfPtweAQAAAN4OeCdPniw//PCD9OzZ0225Dg778ccfPbVvAAAAgHcC3pMnT5oOCNbEE5Z06dIxJS8AAACSfsCrXRR0trWIAe+iRYtMWQMAAACQpLs0aN/c9u3bm3Zfat26dbJixQpT2ztlyhRP7yMAAACQuAFvt27dJDw8XPr27Ws6Nmgv3WzZssm4ceNMIAwAAAAk+T68OnPZ66+/btqQadCbP39+M62vTuebIUMGz+4lAAAA4I2JJ7R+VwNdVxkzZhSHw/EwTwsAAAB4d9AaAAAAkFQQ8AIAAMDWCHgBAABga3Gq4b106VLC7QkAAADg7YA3e/bsCbEPAAAAgG8EvMuWLUu4PQEAAAC8HfA2bdo0IfYBAAAASDAMWgMAAICtEfACAADA1gh4AQAAYGsEvAAAALC1hwp4Q0JCZM+ePZ7bGwAAAMAXAl4NdFu1aiWBgYFSoUIF5/Lnn39etm3b5sn9AwAAABI/4B08eLBcu3ZNgoKC3JZ37dpVhg8f/nB7BAAAAHirD69l6dKlsmXLFilYsKDb8ipVqpjMLwAAAJCkM7zBwcGSNWtW87Ofn59z+c2bN93uAwAAAEky4K1YsaLJ8irXAHfcuHFStWpVz+0dAAAA4I2ShlGjRplphjds2CAOh0OGDRsmK1eulN27d8vatWsfdp8AAAAA72Z4a9eubQLbK1euSOHChWXmzJmSO3du2bRpExleAAAAJP0Mr1XWMH/+fM/uDQAAAOBhzLQGAAAAW4tXhlcnnIiOv7+/FClSRDp27Gj68gIAAADeFK+Ad8CAATJ27Fjp3LmzmWlNOzXs3LlTpk+fLr169ZL79+/Lm2++aQa0devWzfN7DQAAACRkwLt69Wr5/vvvpVmzZs5l7du3l3r16smnn35qBrRVqlRJ3n//fQJeAAAAJL0a3j179kidOnUiLf/f//5nWpOpp59+Wk6cOPHwewgAAAAkdoY3U6ZMZuKJtm3bui1fsmSJBAQEmJ9Pnz4tRYsWlYc1d+5cuXPnTqTlxYsXl+rVq0e5jU57/Oeff7oty5w5s7Ro0eKh9wcAAADJIOB99913zaC0ZcuWmdIFrdXdtWuXLF68WL788kuzjpY2vPXWWw+9gzq5RWhoqPP+9evXze8ZMWJEtAHvrFmzZMWKFW5Z6Hz58hHwAgAAJEPxCnh1IFqJEiXks88+MwPVdNBayZIlZdWqVc4gc+rUqR7ZwcmTJ7vdnzJliskud+jQIcbtNBDXCTEAAACQvMV74gkNbKOq401oGmA3atTIZGxjcvnyZfnhhx9MiYV2ksiePXui7SMAAABsEPB6w4EDB2THjh0mw/sgWsM7b948OXv2rAQFBcn48eNj7BgRFhZmbpZr1655bL8BAACQBAPevXv3mlraU6dOyd27d90emzNnjiRUdjdPnjzSpEmTGNdr166dfP7555ImTRpzf9KkSaY/cJUqVaRcuXJRbjN69GgZNmxYguw3AAAAklhbskWLFkm1atVk//79ZoCYBrzbtm0zHRVu3Ljh+b0UkfDwcBNI62C5lClTxrjuU0895Qx2VY8ePUyXBh3IFp0hQ4ZISEiI86ZdJgAAAJBMM7zDhw+X2bNnywsvvGAGrGnpwL1796RPnz6mi0JC0JZnwcHBZna3+EiXLp2p641pSmS9AQAAwF7ileHV+lirrCBVqlRy69Ytk3V97733ZPny5ZJQ5Qz169eXwoULR3ps8+bNzrpendZY63ZdafZZM7ZVq1ZNkH0DAACAzTK8OrhLM6ZKa2o1AC5fvrwpO3Ad+OUpJ0+eNNMZayY5KjNmzJCtW7eaPruaaW7QoIHpIFGqVClTY6ytzVq2bCnPPfecx/cNAAAANu/S8Oyzz5q62jZt2piyg4RoVfb333+b3xHdTGlas5srVy7zc+rUqc30xt99952ZAllrdxcuXGiCYAAAACQ/8Qp4dYIJ1+4GQ4cONcGuTj4xZswY8TQtZdBbdDQYdpU2bVrp1KmTx/cDAAAAySTgdW3tpaUNOo0wAAAAYJtBazly5PD8ngAAAAC+EvDmzp1b/v33X8/vDQAAAOALAa/22+3Xr59cvXrV0/sDAAAAeL+Gd8qUKfLPP/+YGde0LZnrrGbq6NGjnto/AAAAIPED3oEDBz7cbwUAAAB8OeDt3r275/cEAAAA8JUaXktISIiZ3AEAAACwVcCrgW6rVq0kMDBQKlSo4Fz+/PPPy7Zt2zy5fwAAAEDiB7yDBw+Wa9euSVBQkNvyrl27yvDhwx9ujwAAAABv1/AuXbpUtmzZIgULFnRbXqVKFZP5BQAAAJJ0hjc4OFiyZs1qfvbz83Muv3nzptt9AAAAIEkGvBUrVjRZXuUa4I4bN06qVq3qub0DAAAAvFHSMGrUKGnatKls2LBBHA6HDBs2TFauXCm7d++WtWvXPuw+AQAAAN7N8NauXdsEtleuXJHChQvLzJkzJXfu3LJp0yYyvAAAAEj6GV7t0KBlDfPnz/f8HgEAAADezvDmzJlTWrduLcuWLZM7d+54cn8AAAAA7we8c+fOlbt378oLL7xgShl69epl2pQBAAAAtgh4dUa1RYsWyYULF2Ts2LFy+PBhqVGjhhQtWlTef/99z+8lAAAAkJgBryUgIEA6d+4sf/zxh+zfv18yZMjATGsAAACwT8AbFhYmixcvlpYtW5pBbOfOnZPevXt7bu8AAAAAb3RpWLNmjanjXbhwoanlbdGihfz444/SoEEDSZUqXk8JAAAAJIh4RadPP/201K9fX7788kt57rnnJH369J7fMwAAAMBbAe/Zs2clR44ckZaHhITId999Jz169PDEvgEAAADeqeGNGOyuW7dO2rdvb1qUDRw48OH3CgAAAPD2oLXz58+blmTFixeXOnXqSJo0aczMa5cuXfLUvgEAAACJG/Deu3fPzK727LPPSv78+U0v3rfeess8Nm3aNGnWrJmkTZv24fcKAAAA8EYNb4ECBeTGjRvyyiuvyM6dO+WJJ54wy1977TVP7Q8AAADgvQyvlivkyZNHSpYsaYJfAAAAwFYBr3Zn0GzulClTTODbrl07Wb9+fcLtHQAAAJCYAW+2bNmkf//+EhQUZKYT1oFqTZo0MY+NHDnSLAcAAABs0aWhWrVqMn36dDOd8NSpU2X58uVSunRpKVasmGf3EAAAAPBGwGvJkCGDdOnSRbZs2SIHDx6Upk2bPuxTAgAAAL4T8LoqVaqUfPrpp558SgAAACDxpxZOTJcvX5br16+7LdPaYR009yChoaFy9epVMzNcihQeje0BAACQRPh8FDh06FCTOdbZ3Kxbx44dY9zm/v370qdPHwkMDJQSJUqY4HjhwoWJts8AAADwHT6f4VWNGjWKU8A6btw4mTt3rpkcQ4PlSZMmyUsvvSSPPfaYuQ8AAIDkw+czvJaLFy/K7du3Y7XuxIkTzUA67Rrh5+cnPXv2lEKFCpluEgAAAEhekkTAu3jxYjO7W0BAgFSuXFl27NgRY2B86tQpqV69utvyp556KsbtAAAAYE8+H/BWqVJF9u3bJ8HBweamZQkNGzY0/X+j8t9//5l/s2bNGmnSDOuxqISFhcm1a9fcbgAAAEj6fD7g1QFqZcqUcfb81WmNw8PDZdGiRVGub3VjuHv3rtvyO3fuSMqUKaP9PaNHjzYZZOuWP39+j/4dAAAA8A6fD3gjSps2reTOnVtOnDgR5eP58uUz/54/f95tud7PmzdvtM87ZMgQCQkJcd5Onz7t4T0HAACAN/h8wKstxlz9+++/cvLkSXn00UfdevXqcpUxY0apUKGCrFy50i27+/vvv0vt2rWj/T3+/v6SKVMmtxsAAACSPp8OeLWutlq1avLDDz9IUFCQ/Prrr2bq4gIFCkjbtm2d6w0aNEiefvpp5/333ntPvv32W/nqq69k9+7d8uqrr5pyhh49enjpLwEAAIC3+HTAq1nXadOmyfLly6VNmzamzlZ78u7atctkcl0HpLmWK7Ro0ULmzZsn8+fPN/13NXBev369WQ8AAADJi89PPKED1mbPnh3jOmPGjIm0rGXLluYGAACA5M2nM7wAAADAwyLgBQAAgK0R8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFsj4AUAAICtEfACAADA1gh4AQAAYGsEvAAAALA1Al4AAADYGgEvAAAAbI2AFwAAALZGwAsAAABbI+AFAACArRHwAgAAwNYIeAEAAGBrBLwAAACwNQJeAAAA2BoBLwAAAGyNgBcAAAC2RsALAAAAWyPgBQAAgK0R8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFsj4AUAAICtEfACAADA1nw+4D127Jj07t1bKlasKFWqVJEBAwbIpUuXYtxm6NChUqhQIbdbw4YNE22fAQAA4DtSiQ+7d++eNGnSxAS8HTt2lFu3bslbb70l9erVk+3bt4u/v3+U212+fFkef/xxmThxonNZmjRpEnHPAQAA4Ct8OuBNmTKlBAUFmX8tM2fOlBIlSsjWrVuldu3a0W6bLl06k9kFAABA8ubzJQ2uwa6V9Y1qeUQbNmwwWd5q1aqZrHBISEiC7icAAAB8k09neKPyzjvvSJEiRaRy5crRrpM1a1aznmaAz549a2p6f/nlF9m1a1e0ZRBhYWHmZrl27VqC7D8AAAASV5IKeDWIXblypaxduzbGmtwPP/xQUqT4v+R12bJl5YknnjDlDfPmzZMOHTpEuc3o0aNl2LBhCbbvAAAA8A6fL2mwjBgxQiZMmGAytZUqVYpxXSvYteTJk0cKFiwohw4dinabIUOGmLIH63b69GmP7TsAAAC8J0lkeEeOHGkysD///LPUqlUrztuHhobKuXPnJEuWLNGuo6UO0ZU7AAAAIOny+QzvmDFjTLCrmd06depEuc7gwYOdfXbDw8OlX79+cvHiRXP/xo0b0q1bN3E4HNK6detE3XcAAAB4n08HvMHBwabUQGntretEEvPnz3eupxNR6OA0lTp1ailatKgpe8iePbsZwHb06FFZs2YNbcoAAACSIZ8uaQgMDJTjx49H+Vi2bNmcP48dO9bZYcHPz0969eplbjoBRaZMmUwQDAAAgOTJpwNeHXwWm8kjNIsbl+UAAABIPny6pAEAAAB4WAS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFsj4AUAAICtEfACAADA1gh4AQAAYGsEvAAAALA1Al4AAADYGgEvAAAAbI2AFwAAALZGwAsAAABbI+AFAACArRHwAgAAwNYIeAEAAGBrBLwAAACwNQJeAAAA2BoBLwAAAGyNgBcAAAC2RsALAAAAWyPgBQAAgK0R8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAtkbACwAAAFsj4AUAAICtEfACAADA1gh4AQAAYGsEvAAAALC1VJIEnDhxQr755hu5cOGClClTRjp37ixp06b1+DYAAACwH5/P8B48eFCeeOIJOXTokBQrVkwmT54sderUkTt37nh0GwAAANiTz2d4Bw0aJJUrV5YFCxaY+23btpWCBQvK7NmzTdbWU9sAAADAnnw6wxseHi6rVq2S1q1bO5flzJlT6tatK8uWLfPYNgAAALAvn87wnjp1ypQhFCpUyG253t+wYYPHtlFhYWHmZgkJCTH/Xrt2TRLL/bBbifa7kHgS8xxyxflkX944pzif7Iv3KCTl88n6fQ6HI+kGvLdv3zb/pk+f3m15xowZnY95Yhs1evRoGTZsWKTl+fPnj9e+A5aACRwLeBbnFDif4KsCvPSZd/36dQkICEiaAW+mTJnMv1evXnVbHhwcHO0fFZ9t1JAhQ6R///7O+/fv3zfbZM2aVfz8/B7q70Dkb2P6ReL06dPO1wuIL84neBrnFDifkg7N7GqwmydPnhjX8+mAV4MiDVKDgoKkUaNGbl0YSpcu7bFtlL+/v7m5CgwM9MjfgahpsEvAC0/hfIKncU6B8ylpiCmhmSQGraVIkcIMPpsxY4bcuHHDLNu2bZu5vfzyy871tN/uO++8E6dtAAAAkDz4dMBr1dbqhBE6eUTz5s2lQYMG0rt3b2nYsKFznU2bNsmSJUvitA0AAACSB58uaVBZsmSR7du3y/r1682saaNGjYpUmtCpUydp1qxZnLaB92jpyPvvvx+phATgfIIv4D0KnE/24+d4UB8HAAAAIAnz+ZIGAAAA4GEQ8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAAB909+5dM48AHh4BLwAAgA9au3at1KhRw7RYvX//vmm3ivgh4AUiuHjxIsfEZi5duiTjxo0zc64DvuL06dPy1VdfxbjOmTNn5Nq1a4m2T/AtdevWlWzZspmZYgsUKCD9+/eX4OBgb+9WkkTAC/w/ixYtkvz580uuXLnksccek+PHj3NsbCI8PFwGDx4s69atixRwjB07VqpWrSp58uSRVq1a8YUHiUazdq+//rocO3Ys0vm6detWM2FSwYIFTcDz3nvv8cokQ/qF6OrVq7Jv3z5ZtWqV7Ny500yuhbgj4EWy/rCZNm2azJw503y4vPvuu/LTTz/JlStXJHfu3NKtWzdv7yI8RIPZevXqybfffutctmzZMilXrpz8888/Mnz4cDM9ub72LVu25LgjUVSqVEmGDBkifn5+zmUffvihOVebN28uI0eOlLCwMFmwYIGMHz/eXKWAfekVqB9//FH69OljvuBodr9Lly6mhvfy5cumpAEPQWdaA5KLkJAQx+bNmx1Lly515MiRw/H00087MmXK5EiTJo3j+++/d663e/duvfbtOHjwoFf3F54ze/Zs81rfunXL3A8NDTU3y7Vr1xyjRo0yr/u6des49PCYe/fuOU6dOhXlY4cPH3Z07drVeX/VqlXmHHzxxRfd1vv4448dgYGB5j0M9nPnzh1HixYtHMWLF3d8+eWXjm+//dbx22+/OR8vV66cY9CgQV7dx6SODC+SlYULF0rjxo1l0KBB5tLQypUrzWWiO3fuSOrUqZ3rlS9fXp544gm3jCCSHr0UqBm0OnXqmAyu1kIuXbrUPObv729uWg/Xs2dPU86yfft2KVu2rMyaNcvbuw4bOHTokDn/ChUqJAMGDHAu16sK3bt3N+dbunTpZOrUqbJ582ZnzWbevHklU6ZMbs/12muvyfXr1837FexHzwE9HzSb26tXL2nbtq00aNDA+Xi7du1k7ty5ZHkfAgEvbOvkyZNy5MgRt4FKL7zwgrlEWK1aNRPgqMqVK0v9+vVlxYoVbtvrG853333HG0wSdfv2bXnyySfl77//lg8++EBq1qwpadKkcfsSo+eCjoAOCQkx54peTnz++efNFyPdHohPqdSECROkYsWK5n1F6XuLvpfoZWotT9BzLl++fCYQ1oFItWvXltmzZ5t1U6RIIa+88ops2bLF7Xk1ANb19X0NSZN+YZk0aZL58qJlVPq+Y/ntt9/M+RIYGBjltjpo7dy5c/LHH38k4h7bjLdTzICn7d+/31G9enVHQECAw9/f31GqVCnHoUOHnI+3adPG0bBhw0iXu3X927dvO5edPXvWkSJFCscff/zBi5QETZo0yZE7d25HeHi4c9nEiRMdqVKlcly4cMHc//333839sLAw5zoDBgwwl5TnzZvnlf1G0rRlyxZHo0aNHJkzZ3Z06NDBUbZs2UjvM2XKlHGkTp3asWDBArfl06dPN9tZ5+GBAwfMOfjzzz8717l586YpyVm0aFEi/UV4kLt37zquXr0aqwO1b98+R968eR2tWrUy5Sl16tRxFC1a1Lm9lrA0btw4xud47rnnHNWqVTOlDgMHDox0HiFmZHhhKzro6JlnnjEDPvTnf//9V3LkyGHKGG7duuW8NLRmzRq31i6a1dMG3zpozXWgk37j1gFt8E1Hjx6Vjh07mmxa69at5cCBA26XjTUj5lqqogNAAgIC5Pvvvzf3Ncumr7uVTdPXWssZNAPHpWPEhZbH6HvL2bNnzUBYLZNZvXq1nD9/3rmOPq7lU1WqVHHbVruDhIaGyvLly8197c6gAyqtq0ybNm2SNm3aSNGiRaVFixa8MD5CX5OBAwdG+Zh23ujatat57azXXstbNMOv5S36WaMZX+0SozTLr11kIrbF1Pcxqy3dZ599ZjL9w4YNM+dbhQoVEvxvtJUHBMSAz1qzZo1jwoQJjl9//dUMClGTJ0925MqVy3H//n3neufOnXM88sgjJrtnDQ7QAWvWfUvbtm0dTZo0cVvmmvmDb9m4caPJyo8cOdLx008/mexIhgwZTIZf6cAPfdw6Nyy6XsWKFZ33X3/9dZN1K1SokCN//vyOZcuWmQFsQFwzvK7vF8HBweYK0/jx4yNdNfrll18iba9Xnp599lnn/U8++cRcfWjWrJm5YjV06FDH9evXeVF8yMyZMx2VKlVyy/i+/fbbZgB0njx5zHvTxYsXHadPnzYZe83m6udWx44dTUZfrwAsWbLEbKvvObqNvt7WlUbdrmrVqo6goCCv/Y12QsCLJEff9OvXr28uB+mo1b59+zpLFsaOHesoUKBApG1atmzpFsz26dPHXBpytXbtWkfv3r0T4S9AXN24ccMxZswYtxHqTzzxhOOdd95x+7DR17Ru3brmvo6K1+4b3333ndtz6aVE/fBx/RDRS8g7duwwX4aAiB70BWjbtm3mnNLz1NXzzz9vzlNXDRo0cHTq1CnSc2j5gp6vly9fNvfPnz/vSJkypSm7gW/S9xxNqPz111/OZfplWr+ozJ8/37lM34v0/NASK+22oF+CdLuI9D0oX758JhjW58mWLZtj2rRpifb32B0BL5KcHj16mG/VUWVfNXOibyzHjx93W67ful2zejt37jTr/f3334myz4ifH374wbze+sVGP0RmzJjhfEwzZa4fKkozvfq6njlzxtzv16+fI3v27I6tW7earL8+n2ZRtK47YoYfiEhrvZ955hlHxowZnUGvZm4jvr/o1QY97yK+J/34449muXXVIar2eBb9sqUBkW5j0d+t9cDwXkCrLQrfe+89xyuvvOLo3Lmzyd66XjXS5ZqVtehVR33N//nnH7fn0qtHmtmNSLO4q1evdt7XVol6Xz/LrC8/8AwCXiQ5Wo6gpQtR0QFKGtBEzKDoG1L79u3dls2dO5eell6iweeGDRtiHPChAzQee+wxU5pgBbz/+9//nI/rAJBhw4a5baPPpx82K1eudH5gaY9TPz8/c3lZsyu7du2KFGwAUdFzRYNdzbRaX7a6d+9uAlPXvrp6dUjPu4jlM/p+lDVrVjPAyKJZ4PTp00c5KDLiealXJ7RMRwesIfHoF5T+/fub17ly5crmSpK+D3Xp0sW8DzVv3ty8t6gVK1aYkqj//vvP+SVJ19Fe7650oJpm8F1762riRa8AuC5DwiHghc/RAGXhwoXRPq6Xe7744otoH1+8eLEJcLp162bqMXv16mUuDbledoJ3ffjhhyZA0PID1y4KFg1Y06ZNay7rWvQDR1/XkydPOgOPIkWKuJUh6GVCfV69xOxKP4Ss7YCIdIKZwYMHO44cOeK2XK8MFC5c2HRfsL5s6fmmtZelS5d2fmHTjJxecYhKz549zZdw12BYxwvo7UE0ANYyLWrKE5cGu/oFOeL5oPQzRd+HNIBVGvjquBHXzyQdJxDx9dX1NMOrX56qVKniKF++vKNgwYKO5cuXJ8JfBEXAC6/TSzgaAGnLHr3UrN+q9QMmOnqZL2K7H2tAiGvApM+hbyz65mVd4oZv0Iyt1mHrrEJRffDrh4nWaLvSQEOzujobmjp27JgjXbp0jtdee80M8tCbXv7VjEnETBsQk5IlS5oAR4Nbq2WdWr9+vTlHNdOqQY6V1dVxBBqw1KtXz3xh0yyfbh/dYDb9EqYzqFmuXLnCOerD9u7dG+k1c6UtxHLmzOn8sq2fMZoJtmjZg2bxI9Z0W+MF9HzS2mzXmR6R8Ah44XWtW7d2PPnkkyabsmfPHjMiWb8Fu2b3oqrTjTgooEKFCs7LTPBtWlurwanWuekHh45Ad6XTamoAEfEDQctStMzB9YuNZlf0sq924mjatClfbhBnOpperxy99NJL5r3IKiHQoES/hGumVetuR48e7XY1QTt7vPrqqyZLp+dgdPS81eAZSYcmYCKWwVm0BEE/g6zXVD+39L6VEbbOF30fg+8g4IXH6KUevTT4oBGtrnTwh75R7N6927lMC/X1zeLTTz+N9rmGDBligmLN9moHBs0KaykDkgZtBaYlJ1adpAYLX3/9tfNx/bKjAe+sWbPcttMaOj1ftPbNohk27dJh1dABcaXlLprB1bpazdpqjaZeJdCWh1r3rfRy9OOPP+623eHDhx1ZsmQxX9IDAwM58Dby0UcfRVs/rZlbfR+aMmWKc5mWuLh+cdfzRb9AwXcw8QQ8RqfT1MbYru7fv2+m9tUpNTNnzmwmc9Cm65cuXTKPW022c+XK5dwmS5Yspkm36xSw6vPPPzdTv6pRo0aZOcfr1atn5hvfsWOHPPfcc7yaSUR4eLikTZvW/KzN03WOeG3U/8svv5hlOXPmlO7du5tG7Tq5hNJ/tVn7o48+Kj/88IPzuXRiiccee0yyZcvmpb8GScnevXtl/vz5sn//fucya3rfefPmyaJFi0yz/z59+phJIqyJS9q3by+HDh2SXbt2ObcrWbKkLF26VHbv3u02wQmSPp3eWScrsj5zXOn0zypdunTOZfqZNWfOHOdU9vp5qPfhQ7wdccNeGV7Xwn3N1GrmVVu5aMZE269oLa1ektaBaerSpUsmsxJxRKteCtLT0zVjXLNmTdOEG0mfZj+0jnfTpk1m8JlmybRtnGZUNOOrtCZXSxR0BLRO06oDD7XRu454t3rtAkqvDmit7IOyuE899ZSp09UxAFpjqVeKXKf31VH0+p6k6+p7l/Zs1uyt1VlEe3xrD++ItK+qDkyDvWjP5KjGi2hrOS2h0kklLHr1Uq8QUFbnuwh4EWsanOil6Jh89tlnbi1WNIjR0gPXUcY6F7xertaBG9aIVr2MGPF5NODVoMiiAwiYR94etHelBrIlSpRwDB8+3NmzUicR0Zpc1z6n2uNUayStEfHal1dLWQDXtoN6TrnSS9EaxGrfXC1P0NKEd9991zlYTAcm6TmoLcWUTmqiQcxXX33lVmrjWsepAbK2RYw4Qck333xjBrfBXjSw1c+vf//917lMB5zpDI5z5szx6r4h7gh4EWvbt283QaiOjnelg8cWLFgQbRNuzZq4tp6yelNaNZta8K8BsA5k0jeWP/74wzTp1mk2dfQ07OeFF15wDBgwINJyPTc0o6ZXBCyuNXTalF/blcXUtg7Jj04ooh07XKfeffPNN81MZ9YMVlpjq8GuftHWPt46K58Gr64zWen0vtrZxRKxHZhOVKO1na79cjXzq32/Xd/3YA9Wz2T9oqNjSnQAo35x0h7iSHoIeBEnmpFzbfavWRLNglitfKJrwu06k4zVm1JLFFyztzqlogbUWvKg84vrc2lvS/1Agb3olxntexoVax55i849ry3KNDjRDxzryxXges5o1k0zclaLQg1UrJIo7eah2dtWrVqZwFfbSmkJVsRMrU7vq+9BcenZrYNmNQiKacAukq527do5M/36XoSky0//4+06YiQdI0eOlFmzZslff/1l7jdt2lTKli1rBpGpe/fuSb58+WTo0KHy+uuvm2VNmjQxA4p0O8vWrVulevXqZnBIoUKFnMtv3LghGTJkMD+PHz9ePv30Uzlz5kwi/5VIaHpOFC5cWL788stYD3LTm3VuABG99tprcvLkSfntt9/MAMjr1687B77qe0j+/PnNe5K+h2XKlMm5XXBwsBmcpgMl7969a96/unXrJsOGDYvVQb58+bJkzZqVF8Smzp8/b84X1wFqSKK8HXEjaTlx4oQZZKY9czdv3myyJVoj5yqqJtxRtXfRbJ1rL10dLKJlE5pd+eSTT8w29DG0J71EyAxDiI6WDGiruYi0jluzuDpwcdKkSW59mrUnql4R0ppvLW84evSo27Y60Yn2VnUtU9D3nFq1apkyKouWP7iWRgCwBzK8iJK2h/rmm2/k2rVr0qhRI5MZSZUqlXmsTp06Urp0adOip27duvLOO+9EavtTvnx5OXLkiJQoUUJu375t2o599dVX0rZtW+d6mg1OmTKl875mWbT1j2aPH3/8cfM7tYUZgOTj559/Ni2hcufOLYcPHzbLtI1hr169ZOXKlVKrVi0pVqyYLFu2TAoWLGgyun5+fqYdVJEiRcx7lmZqR4wYIV26dJFHHnnEPMfff/8tNWvWNO0RX3rpJfOcCxYscLsaBcC+6MOLSPr162d6UGp/26efftpc2tMPCNd+g9OmTZMNGzaYDyf94HGtjClXrpwJiK3LifqBo88XkWuwq7SP5ffff2/6XOq2BLtA8qP9ljNmzGi+MGt/bRUYGGjej7QESnsxa7nTmjVrZP369aZvrtKgV79Qa3nBRx99ZPp2a0CsJQxXr141QfKBAwfM+5eWPmiZ1aZNmwh2geTC2ylm+BZtxaOXBV0vB+qlQj1VfvrpJ2f7Hh0pr7PM6KVpLWvQWWa0/MAaBDJ27Fgz1zwARDX6/fPPPzft5bQ/t6tx48Y52rZt6yhYsKCjd+/ekbbVLjHaC1enpNb+za59Uv/880/zXqWdX7Qjg3Zv0NaIGTNmNC3J6JEKJF+UNCRDYWFh8vXXX5vshpYa6OW8okWLmsc0c/vWW2+ZgRyuNLuiWZfFixeb+61btzaXDTW7ooNDpkyZYgaY6To6M41mVrQEIk2aNF75GwH4Ln3P0PcefX/QWc70apE1kEwHwGomVweRTZ06Vf79919nOZXOhKYDyvTWo0cP2blzp7n6pIPSrNka9crQU089JZ988onz92mmWMurXAerAUheKGmwGa2D3bhxowlo165dG+lxrVvTcoM//vjDdEnYt2+fqbe1psvMnj27ufynI49daa2u61Scellw+fLlJjDWIPfNN9+U48ePm+4LOs2rjmgl2AUQFX3PaNGihZkmWgPeVq1amS/Q1nuYljdpPf9///0nK1ascNb863TTkydPNiUL2uUjR44cZvl3333nfG7dbs+ePW6/78knnyTYBZI5Al6b2L59u/Tu3du01NEaXM281qtXzwzccDV48GBTy6ZZ2DfeeENWr14tVatWNYM7rAFpadOmdWshpvTDKH369M77zzzzjFSsWNFkYiwa4GqtHQA8iAam+kVbxwjoF+euXbua5dp+TgNezchqoDp79myz/P79+3Lr1i239yHN+OogNGu8gNKgWOt7AcAVAa8NbN682VzG01IFDXz18p2OZh44cKB88cUXbutq9rdGjRpuA8c0KNbOCnp5MCAgwJQ4DB8+3DyXOnHihEyfPt2tw4JeYtTfW6lSpUT8SwHYhZZJaRmCfvnWq0V61Unfd6wMrxUU66DYkJAQs0zfg7TH7rvvvmu237Ztm6xatcqtZ27EwbAAoAh4baBatWqmZlYzs/qvJU+ePKYOzpVmQ3SEsqvKlSubzKxV1qABsE4MoJlfre3Vrgsvv/yyKVsAAE/QwFTrb+fMmWPep7QVoo4DWLJkiTPgbdOmjSlZ0PZhatKkSdK3b19TmtWhQwcT8OqVpubNm/OiAIgRAa8NaDse7Vupl/c0O6IfBlrjpv0lNWjVzK1F24zpB4peGnTdPkWKFOLv7+8sTZg7d64cO3bMXE48ffq0GUii6wGAp+hYAL2CpIPWtPe2DorVAFdLGZS2DtPyKausQd+j9Iu3Br6a7bUCYwB4ELo02IT2rNTBYlra8Oeff5oMrZYu6EAz7aCgWRTNppw7d05KliwpL774ohnYpkGsTjCh9b/a/9Ia6QwAiaFMmTLmfUs7xERFy620frdChQq8IADijYDXRjQrogGs1sJlyJDBubxZs2am4bpmUpTW92pbMS1j0DY9OmBEB6npIDcASEx6NUqvIJ0/f96UZQFAQqCkwUZ0gIdmdPWSoCvtvHDq1Cln25+GDRuaOt6JEyfKl19+adqJEewC8AYtx7p586bpCw4ACYUMr41oz0odqKb1bVabMa3p1QBX63KtfpYA4GvvXdoDHAASChleG9EPDB3gofW6Z8+elbFjx0rx4sXNZUId0AYAvohgF0BCI+C1YVnD+vXrpWzZsqbLgga62u6HCSEAAEByRUmDzYSGhpom7jpQzWozBgAAkJwR8AIAAMDWKGkAAACArRHwAgAAwNYIeAEAAGBrBLwAAACwNQJeAAAA2BoBLwAAAGyNgBcAAAC2RsALAD5u27ZtsnXrVklKdu/eLRs3bvT2bgCAker//gEAeIvD4ZADBw7I8ePHJSAgQB577DHJmTOn8/FJkybJ3bt3pWrVqknmRZoxY4acOXNGatSo4e1dAQACXgDwpvXr10vXrl3l2rVrUqlSJbl586b89ddfUrNmTZk6daqkT5+eFwgAHhIZXgDwkl27dsnTTz8tffv2lREjRkiqVP/3lnz//n1ZsGCB3L592y3gvXXrluzZs0du3LghVapUkcDAQOdjhw4dkv3795ufNUtcpkwZyZcvX6TSCM0mly1bNtrnic06Fs1KHzt2TPLnzy/lypWTlClTJshxAoCHRcALAF4ydOhQKVasmIwaNUpSpPj/h1Toz61bt3ZbNygoSCpWrCgFCxaU8+fPy7lz52TdunVSsmRJ8/iff/4pS5YsMT8HBwfLpk2b5L333pNBgwa5lUbs27dPQkNDo32e2Kxz/fp1efHFF+XgwYNSvnx5k5HOmDGjLFu2THLlypUoxw4A4oKAFwC84M6dO7JmzRp566233ILd6GhAqxlhre/VDGz9+vVl3LhxMn36dPP4c889Z24Wzc5qze8LL7wgRYoUifXzxGadN998U/z8/Ex2N02aNCYjrb9Hl8+ZM8fDRwoAHh4BLwB4gWZhw8PDpUCBArFav06dOiYAVRps1qpVS1atWuW2jtYBa6B78eJFuXfvnmTKlMkErq4Bb2yeJ6Z1dPCcBrU9e/aUn376yQTEetPyiYULFz7kUQGAhEHACwBekCFDBvPv5cuXY7V+lixZ3O77+/ubsgPLokWLpHPnzvLoo4+aIFofDwsLM8FvXJ7nQevo82kt8d69e+X06dORAmUA8EUEvADgBToYrVSpUh7rr9u7d2/58MMPzb+WbNmymeyrJ2mtrmZ9X3vtNVPHCwBJARNPAICX9O/fX5YuXWpqeSPSzK92aYgNLV+4dOmSlChRwrls7dq1sc4exzXgrV69ukyZMiVSMH327FmP/z4A8AQyvADgJZ06dZIjR45I48aNpX379maQmfbh1e4Hv/32m8n+PvLIIw98Hm0H1rRpU+nTp4/069dP/vvvP/nss88kXbp0CbLfEydOlHr16kndunXNYDUtd/j9999NrfAXX3yRIL8TAB4GAS8AeNFHH30k7dq1k8WLF5vWX9pDVyegGD9+vLMHr/bB1U4Irh5//HFp2LCh874OJNOWYps3b5asWbPKypUrzbLixYs714nN88RmHe3Rq23SZs6cKdu3bzelE2+88YbbOtpCrXDhwh45RgDwsPwcni7wAgAAAHwINbwAAACwNQJeAAAA2BoBLwAAAGyNgBcAAAC2RsALAAAAWyPgBQAAgK0R8AIAAMDWCHgBAABgawS8AAAAsDUCXgAAANgaAS8AAABsjYAXAAAAYmf/H6IwHiPw6zB3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "ax.bar(lead_time.index, lead_time.values)\n",
    "ax.set_title(\"Average Booking Lead Time by Channel\")\n",
    "ax.set_xlabel(\"Channel\")\n",
    "ax.set_ylabel(\"Average Lead Time (Days)\")\n",
    "\n",
    "plt.xticks(rotation=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6e712f",
   "metadata": {},
   "source": [
    "## 7. Room-Type Performance\n",
    "\n",
    "Room categories are compared using realised booking volume, room nights, revenue, ADR, and average booking value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89ff6728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>bookings</th>\n",
       "      <th>room_nights</th>\n",
       "      <th>revenue</th>\n",
       "      <th>adr</th>\n",
       "      <th>avg_booking_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cottage</td>\n",
       "      <td>2563</td>\n",
       "      <td>4985</td>\n",
       "      <td>29194850.21</td>\n",
       "      <td>5860.34</td>\n",
       "      <td>11390.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Villa</td>\n",
       "      <td>273</td>\n",
       "      <td>540</td>\n",
       "      <td>6402933.19</td>\n",
       "      <td>11868.95</td>\n",
       "      <td>23453.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Camping Tent</td>\n",
       "      <td>523</td>\n",
       "      <td>1013</td>\n",
       "      <td>3240659.87</td>\n",
       "      <td>3200.11</td>\n",
       "      <td>6196.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dormitory</td>\n",
       "      <td>586</td>\n",
       "      <td>1137</td>\n",
       "      <td>2813232.50</td>\n",
       "      <td>2486.81</td>\n",
       "      <td>4800.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      room_type  bookings  room_nights      revenue       adr  \\\n",
       "1       Cottage      2563         4985  29194850.21   5860.34   \n",
       "3         Villa       273          540   6402933.19  11868.95   \n",
       "0  Camping Tent       523         1013   3240659.87   3200.11   \n",
       "2     Dormitory       586         1137   2813232.50   2486.81   \n",
       "\n",
       "   avg_booking_value  \n",
       "1           11390.89  \n",
       "3           23453.97  \n",
       "0            6196.29  \n",
       "2            4800.74  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_performance = (\n",
    "    active.groupby(\"room_type\")\n",
    "    .agg(\n",
    "        bookings=(\"booking_id\", \"count\"),\n",
    "        room_nights=(\"nights\", \"sum\"),\n",
    "        revenue=(\"revenue\", \"sum\"),\n",
    "        adr=(\"nightly_rate\", \"mean\"),\n",
    "        avg_booking_value=(\"booking_value\", \"mean\")\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "room_performance.round(2).sort_values(\"revenue\", ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c455bce",
   "metadata": {},
   "source": [
    "## 8. Key Findings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4db33cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest monthly occupancy: 145.4% in 2025-12.\n",
      "Lowest monthly occupancy: 71.3% in 2026-04.\n",
      "Highest cancellation rate: Agoda at 18.7%.\n",
      "Direct booking cancellation rate: 7.2%.\n"
     ]
    }
   ],
   "source": [
    "best_month = monthly.loc[monthly[\"occupancy_pct\"].idxmax()]\n",
    "lowest_month = monthly.loc[monthly[\"occupancy_pct\"].idxmin()]\n",
    "highest_cancel = channel.loc[channel[\"cancellation_rate\"].idxmax()]\n",
    "direct = channel.loc[channel[\"channel\"] == \"Direct\"].iloc[0]\n",
    "\n",
    "print(\n",
    "    f\"Highest monthly occupancy: {best_month['occupancy_pct']:.1f}% \"\n",
    "    f\"in {best_month['month']}.\"\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Lowest monthly occupancy: {lowest_month['occupancy_pct']:.1f}% \"\n",
    "    f\"in {lowest_month['month']}.\"\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Highest cancellation rate: {highest_cancel['channel']} \"\n",
    "    f\"at {highest_cancel['cancellation_rate']:.1f}%.\"\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Direct booking cancellation rate: \"\n",
    "    f\"{direct['cancellation_rate']:.1f}%.\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248bbf74",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "The analysis highlights three operational themes:\n",
    "\n",
    "1. **Inventory utilisation is seasonal.** Occupancy varies meaningfully across the analysis period, so booking count alone does not fully describe property performance.\n",
    "\n",
    "2. **Booking channels behave differently.** OTA channels show different cancellation and lead-time patterns from Direct bookings, which matters for forecasting and inventory control.\n",
    "\n",
    "3. **Occupancy, ADR, and RevPAR should be interpreted together.** A high nightly rate does not automatically mean strong inventory performance, while occupancy alone does not capture pricing quality.\n",
    "\n",
    "The dataset is synthetic and does not contain operational variables such as marketing campaigns, local events, competitor pricing, or channel commissions. The analysis therefore identifies patterns in booking behaviour without claiming unsupported causes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd67504",
   "metadata": {},
   "source": [
    "## 9. Project Summary\n",
    "\n",
    "This project demonstrates an end-to-end Python analytics workflow:\n",
    "\n",
    "**CSV data → Pandas validation and preparation → aggregation → hotel KPIs → exploratory analysis → Matplotlib visualisation → business interpretation**\n",
    "\n",
    "The complete source datasets are available in the repository's `data/` directory.\n"
   ]
  }
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