diff --git a/Visualizations/Dashboard_Cancellations.ipynb b/Visualizations/Dashboard_Cancellations.ipynb
deleted file mode 100644
index d280c07..0000000
--- a/Visualizations/Dashboard_Cancellations.ipynb
+++ /dev/null
@@ -1,1164 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'geoviews'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-44-8e90ad76ea6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mgeoviews\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geoviews'"
-     ]
-    }
-   ],
-   "source": [
-    "# Imports for panel visualizations\n",
-    "\n",
-    "import panel as pn\n",
-    "import plotly.express as px\n",
-    "pn.extension('plotly')\n",
-    "import pandas as pd\n",
-    "import hvplot.pandas\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "from dotenv import load_dotenv\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "%matplotlib inline\n",
-    "import geoviews as gv\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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>hotel</th>\n",
-       "      <th>is_canceled</th>\n",
-       "      <th>lead_time</th>\n",
-       "      <th>arrival_date_year</th>\n",
-       "      <th>arrival_date_month</th>\n",
-       "      <th>arrival_date_week_number</th>\n",
-       "      <th>arrival_date_day_of_month</th>\n",
-       "      <th>stays_in_weekend_nights</th>\n",
-       "      <th>stays_in_week_nights</th>\n",
-       "      <th>adults</th>\n",
-       "      <th>...</th>\n",
-       "      <th>market_segment</th>\n",
-       "      <th>distribution_channel</th>\n",
-       "      <th>reserved_room_type</th>\n",
-       "      <th>assigned_room_type</th>\n",
-       "      <th>days_in_waiting_list</th>\n",
-       "      <th>customer_type</th>\n",
-       "      <th>adr</th>\n",
-       "      <th>required_car_parking_spaces</th>\n",
-       "      <th>total_of_special_requests</th>\n",
-       "      <th>reservation_status_date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Resort Hotel</td>\n",
-       "      <td>0</td>\n",
-       "      <td>342</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7/1/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Resort Hotel</td>\n",
-       "      <td>0</td>\n",
-       "      <td>737</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7/1/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Resort Hotel</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>A</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7/2/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Resort Hotel</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Corporate</td>\n",
-       "      <td>Corporate</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7/2/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Resort Hotel</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Online TA</td>\n",
-       "      <td>TA/TO</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7/3/2015</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
-       "0  Resort Hotel            0        342               2015               July   \n",
-       "1  Resort Hotel            0        737               2015               July   \n",
-       "2  Resort Hotel            0          7               2015               July   \n",
-       "3  Resort Hotel            0         13               2015               July   \n",
-       "4  Resort Hotel            0         14               2015               July   \n",
-       "\n",
-       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
-       "0                        27                          1   \n",
-       "1                        27                          1   \n",
-       "2                        27                          1   \n",
-       "3                        27                          1   \n",
-       "4                        27                          1   \n",
-       "\n",
-       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  market_segment  \\\n",
-       "0                        0                     0       2  ...          Direct   \n",
-       "1                        0                     0       2  ...          Direct   \n",
-       "2                        0                     1       1  ...          Direct   \n",
-       "3                        0                     1       1  ...       Corporate   \n",
-       "4                        0                     2       2  ...       Online TA   \n",
-       "\n",
-       "   distribution_channel reserved_room_type assigned_room_type  \\\n",
-       "0                Direct                  C                  C   \n",
-       "1                Direct                  C                  C   \n",
-       "2                Direct                  A                  C   \n",
-       "3             Corporate                  A                  A   \n",
-       "4                 TA/TO                  A                  A   \n",
-       "\n",
-       "  days_in_waiting_list customer_type   adr required_car_parking_spaces  \\\n",
-       "0                    0     Transient   0.0                           0   \n",
-       "1                    0     Transient   0.0                           0   \n",
-       "2                    0     Transient  75.0                           0   \n",
-       "3                    0     Transient  75.0                           0   \n",
-       "4                    0     Transient  98.0                           0   \n",
-       "\n",
-       "   total_of_special_requests reservation_status_date  \n",
-       "0                          0                7/1/2015  \n",
-       "1                          0                7/1/2015  \n",
-       "2                          0                7/2/2015  \n",
-       "3                          0                7/2/2015  \n",
-       "4                          1                7/3/2015  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file_path = Path(r'C:\\Users\\tonyh\\Desktop\\hotel_bookings1.csv')\n",
-    "hotel_data = pd.read_csv(file_path)\n",
-    "hotel_data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Group by hotel and year?\n",
-    "grouped = hotel_data.groupby([\"hotel\", \"arrival_date_year\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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></th>\n",
-       "      <th>is_canceled</th>\n",
-       "      <th>lead_time</th>\n",
-       "      <th>arrival_date_month</th>\n",
-       "      <th>arrival_date_week_number</th>\n",
-       "      <th>arrival_date_day_of_month</th>\n",
-       "      <th>stays_in_weekend_nights</th>\n",
-       "      <th>stays_in_week_nights</th>\n",
-       "      <th>adults</th>\n",
-       "      <th>children</th>\n",
-       "      <th>babies</th>\n",
-       "      <th>...</th>\n",
-       "      <th>market_segment</th>\n",
-       "      <th>distribution_channel</th>\n",
-       "      <th>reserved_room_type</th>\n",
-       "      <th>assigned_room_type</th>\n",
-       "      <th>days_in_waiting_list</th>\n",
-       "      <th>customer_type</th>\n",
-       "      <th>adr</th>\n",
-       "      <th>required_car_parking_spaces</th>\n",
-       "      <th>total_of_special_requests</th>\n",
-       "      <th>reservation_status_date</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>hotel</th>\n",
-       "      <th>arrival_date_year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">City Hotel</th>\n",
-       "      <th>2015</th>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Offline TA/TO</td>\n",
-       "      <td>TA/TO</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7/3/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>1</td>\n",
-       "      <td>119</td>\n",
-       "      <td>January</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>74.25</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12/31/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>1</td>\n",
-       "      <td>26</td>\n",
-       "      <td>January</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Online TA</td>\n",
-       "      <td>TA/TO</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>128.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12/17/2016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Resort Hotel</th>\n",
-       "      <th>2015</th>\n",
-       "      <td>0</td>\n",
-       "      <td>342</td>\n",
-       "      <td>July</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>Direct</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7/1/2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>0</td>\n",
-       "      <td>109</td>\n",
-       "      <td>January</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Online TA</td>\n",
-       "      <td>TA/TO</td>\n",
-       "      <td>A</td>\n",
-       "      <td>D</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Transient-Party</td>\n",
-       "      <td>59.94</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1/2/2016</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                is_canceled  lead_time arrival_date_month  \\\n",
-       "hotel        arrival_date_year                                              \n",
-       "City Hotel   2015                         0          6               July   \n",
-       "             2016                         1        119            January   \n",
-       "             2017                         1         26            January   \n",
-       "Resort Hotel 2015                         0        342               July   \n",
-       "             2016                         0        109            January   \n",
-       "\n",
-       "                                arrival_date_week_number  \\\n",
-       "hotel        arrival_date_year                             \n",
-       "City Hotel   2015                                     27   \n",
-       "             2016                                      1   \n",
-       "             2017                                      1   \n",
-       "Resort Hotel 2015                                     27   \n",
-       "             2016                                      1   \n",
-       "\n",
-       "                                arrival_date_day_of_month  \\\n",
-       "hotel        arrival_date_year                              \n",
-       "City Hotel   2015                                       1   \n",
-       "             2016                                       1   \n",
-       "             2017                                       1   \n",
-       "Resort Hotel 2015                                       1   \n",
-       "             2016                                       1   \n",
-       "\n",
-       "                                stays_in_weekend_nights  stays_in_week_nights  \\\n",
-       "hotel        arrival_date_year                                                  \n",
-       "City Hotel   2015                                     0                     2   \n",
-       "             2016                                     0                     1   \n",
-       "             2017                                     1                     0   \n",
-       "Resort Hotel 2015                                     0                     0   \n",
-       "             2016                                     0                     1   \n",
-       "\n",
-       "                                adults  children  babies  ... market_segment  \\\n",
-       "hotel        arrival_date_year                            ...                  \n",
-       "City Hotel   2015                    1       0.0       0  ...  Offline TA/TO   \n",
-       "             2016                    2       0.0       0  ...         Direct   \n",
-       "             2017                    1       0.0       0  ...      Online TA   \n",
-       "Resort Hotel 2015                    2       0.0       0  ...         Direct   \n",
-       "             2016                    2       0.0       0  ...      Online TA   \n",
-       "\n",
-       "                               distribution_channel reserved_room_type  \\\n",
-       "hotel        arrival_date_year                                           \n",
-       "City Hotel   2015                             TA/TO                  A   \n",
-       "             2016                            Direct                  A   \n",
-       "             2017                             TA/TO                  A   \n",
-       "Resort Hotel 2015                            Direct                  C   \n",
-       "             2016                             TA/TO                  A   \n",
-       "\n",
-       "                               assigned_room_type days_in_waiting_list  \\\n",
-       "hotel        arrival_date_year                                           \n",
-       "City Hotel   2015                               A                    0   \n",
-       "             2016                               A                    0   \n",
-       "             2017                               A                    0   \n",
-       "Resort Hotel 2015                               C                    0   \n",
-       "             2016                               D                    0   \n",
-       "\n",
-       "                                  customer_type     adr  \\\n",
-       "hotel        arrival_date_year                            \n",
-       "City Hotel   2015                     Transient    0.00   \n",
-       "             2016                     Transient   74.25   \n",
-       "             2017                     Transient  128.00   \n",
-       "Resort Hotel 2015                     Transient    0.00   \n",
-       "             2016               Transient-Party   59.94   \n",
-       "\n",
-       "                               required_car_parking_spaces  \\\n",
-       "hotel        arrival_date_year                               \n",
-       "City Hotel   2015                                        0   \n",
-       "             2016                                        0   \n",
-       "             2017                                        0   \n",
-       "Resort Hotel 2015                                        0   \n",
-       "             2016                                        0   \n",
-       "\n",
-       "                                total_of_special_requests  \\\n",
-       "hotel        arrival_date_year                              \n",
-       "City Hotel   2015                                       0   \n",
-       "             2016                                       0   \n",
-       "             2017                                       2   \n",
-       "Resort Hotel 2015                                       0   \n",
-       "             2016                                       1   \n",
-       "\n",
-       "                                reservation_status_date  \n",
-       "hotel        arrival_date_year                           \n",
-       "City Hotel   2015                              7/3/2015  \n",
-       "             2016                            12/31/2015  \n",
-       "             2017                            12/17/2016  \n",
-       "Resort Hotel 2015                              7/1/2015  \n",
-       "             2016                              1/2/2016  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "avg_grouped = grouped.first()\n",
-    "avg_grouped.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {},
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "    embed_document(root);\n",
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-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined && root['Plotly'] !== undefined) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 100) {\n",
-       "          clearInterval(timer);\n",
-       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
-       "        }\n",
-       "      }\n",
-       "    }, 10, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ],
-      "text/plain": [
-       ":DynamicMap   [hotel]\n",
-       "   :Bars   [arrival_date_year]   (lead_time)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "1001"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "avg_grouped.hvplot.bar(x=\"arrival_date_year\", y=\"lead_time\", groupby=\"hotel\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Map country of origin?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PRT    48590\n",
-       "GBR    12129\n",
-       "FRA    10415\n",
-       "ESP     8568\n",
-       "DEU     7287\n",
-       "       ...  \n",
-       "BWA        1\n",
-       "MMR        1\n",
-       "MDG        1\n",
-       "MRT        1\n",
-       "VGB        1\n",
-       "Name: country, Length: 177, dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hotel_data[\"country\"].value_counts().sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "load_dotenv()\n",
-    "mapbox_token = os.getenv(\"MAPBOX\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "px.set_mapbox_access_token(mapbox_token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "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>city</th>\n",
-       "      <th>city_ascii</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lng</th>\n",
-       "      <th>country</th>\n",
-       "      <th>iso2</th>\n",
-       "      <th>iso3</th>\n",
-       "      <th>admin_name</th>\n",
-       "      <th>capital</th>\n",
-       "      <th>population</th>\n",
-       "      <th>id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Tokyo</td>\n",
-       "      <td>Tokyo</td>\n",
-       "      <td>35.6897</td>\n",
-       "      <td>139.6922</td>\n",
-       "      <td>Japan</td>\n",
-       "      <td>JP</td>\n",
-       "      <td>JPN</td>\n",
-       "      <td>Tōkyō</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>37977000.0</td>\n",
-       "      <td>1392685764</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Jakarta</td>\n",
-       "      <td>Jakarta</td>\n",
-       "      <td>-6.2146</td>\n",
-       "      <td>106.8451</td>\n",
-       "      <td>Indonesia</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>IDN</td>\n",
-       "      <td>Jakarta</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>34540000.0</td>\n",
-       "      <td>1360771077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Delhi</td>\n",
-       "      <td>Delhi</td>\n",
-       "      <td>28.6600</td>\n",
-       "      <td>77.2300</td>\n",
-       "      <td>India</td>\n",
-       "      <td>IN</td>\n",
-       "      <td>IND</td>\n",
-       "      <td>Delhi</td>\n",
-       "      <td>admin</td>\n",
-       "      <td>29617000.0</td>\n",
-       "      <td>1356872604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Mumbai</td>\n",
-       "      <td>Mumbai</td>\n",
-       "      <td>18.9667</td>\n",
-       "      <td>72.8333</td>\n",
-       "      <td>India</td>\n",
-       "      <td>IN</td>\n",
-       "      <td>IND</td>\n",
-       "      <td>Mahārāshtra</td>\n",
-       "      <td>admin</td>\n",
-       "      <td>23355000.0</td>\n",
-       "      <td>1356226629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Manila</td>\n",
-       "      <td>Manila</td>\n",
-       "      <td>14.5958</td>\n",
-       "      <td>120.9772</td>\n",
-       "      <td>Philippines</td>\n",
-       "      <td>PH</td>\n",
-       "      <td>PHL</td>\n",
-       "      <td>Manila</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>23088000.0</td>\n",
-       "      <td>1608618140</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      city city_ascii      lat       lng      country iso2 iso3   admin_name  \\\n",
-       "0    Tokyo      Tokyo  35.6897  139.6922        Japan   JP  JPN        Tōkyō   \n",
-       "1  Jakarta    Jakarta  -6.2146  106.8451    Indonesia   ID  IDN      Jakarta   \n",
-       "2    Delhi      Delhi  28.6600   77.2300        India   IN  IND        Delhi   \n",
-       "3   Mumbai     Mumbai  18.9667   72.8333        India   IN  IND  Mahārāshtra   \n",
-       "4   Manila     Manila  14.5958  120.9772  Philippines   PH  PHL       Manila   \n",
-       "\n",
-       "   capital  population          id  \n",
-       "0  primary  37977000.0  1392685764  \n",
-       "1  primary  34540000.0  1360771077  \n",
-       "2    admin  29617000.0  1356872604  \n",
-       "3    admin  23355000.0  1356226629  \n",
-       "4  primary  23088000.0  1608618140  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file_path = Path(r\"C:\\Users\\tonyh\\Desktop\\worldcities.csv\")\n",
-    "world_data = pd.read_csv(file_path)\n",
-    "world_data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "world_data = world_data.drop(columns=[\"city_ascii\", \"iso2\", \"iso3\", \"iso3\", \"admin_name\", \"capital\", \"id\", \"population\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>city</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lng</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Tokyo</td>\n",
-       "      <td>35.6897</td>\n",
-       "      <td>139.6922</td>\n",
-       "      <td>Japan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Jakarta</td>\n",
-       "      <td>-6.2146</td>\n",
-       "      <td>106.8451</td>\n",
-       "      <td>Indonesia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Delhi</td>\n",
-       "      <td>28.6600</td>\n",
-       "      <td>77.2300</td>\n",
-       "      <td>India</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Mumbai</td>\n",
-       "      <td>18.9667</td>\n",
-       "      <td>72.8333</td>\n",
-       "      <td>India</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Manila</td>\n",
-       "      <td>14.5958</td>\n",
-       "      <td>120.9772</td>\n",
-       "      <td>Philippines</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      city      lat       lng      country\n",
-       "0    Tokyo  35.6897  139.6922        Japan\n",
-       "1  Jakarta  -6.2146  106.8451    Indonesia\n",
-       "2    Delhi  28.6600   77.2300        India\n",
-       "3   Mumbai  18.9667   72.8333        India\n",
-       "4   Manila  14.5958  120.9772  Philippines"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "world_data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Replace with hotel data and have functions that return figures, plots, etc.\n",
-    "\n",
-    "# avg_data = pn.Row(average_gross_rent(), average_sales_price())\n",
-    "# yearly_data = pn.Column(\"replace with relevant hotel information\")\n",
-    "# column_of_ML_model_1 = pn.Column(replace with ML model 1 information)\n",
-    "# neighborhood_data = pn.Column(column_of_neighborhood) \n",
-    "# world_map_data = pn.Column(world_map()) \n",
-    "\n",
-    "\n",
-    "# panel = pn.Tabs(\n",
-    "#    (\"Yearly Market\", yearly_data),   \n",
-    "#    (\"Country of Origin Map\", world_map_data),\n",
-    "#    (\"ML model 1 Analysis\", ML_model_1_data),\n",
-    "# )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Serve the Panel dashboard\n",
-    "\n",
-    "# panel.servable()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total bookings canceled: 44,224 (37 %)\n",
-      "Resort hotel bookings canceled: 11,122 (28 %)\n",
-      "City hotel bookings canceled: 33,102 (42 %)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Hotel Cancellations\n",
-    "total_cancelations = hotel_data[\"is_canceled\"].sum()\n",
-    "rh_cancelations = hotel_data.hotel_dataloc[hotel_data[\"hotel\"] == \"Resort Hotel\"][\"is_canceled\"].sum()\n",
-    "ch_cancelations = hotel_data.loc[hotel_data[\"hotel\"] == \"City Hotel\"][\"is_canceled\"].sum()\n",
-    "\n",
-    "# as percent:\n",
-    "rel_cancel = total_cancelations / hotel_data.shape[0] * 100\n",
-    "rh_rel_cancel = rh_cancelations / hotel_data.loc[hotel_data[\"hotel\"] == \"Resort Hotel\"].shape[0] * 100\n",
-    "ch_rel_cancel = ch_cancelations / hotel_data.loc[hotel_data[\"hotel\"] == \"City Hotel\"].shape[0] * 100\n",
-    "\n",
-    "print(f\"Total bookings canceled: {total_cancelations:,} ({rel_cancel:.0f} %)\")\n",
-    "print(f\"Resort hotel bookings canceled: {rh_cancelations:,} ({rh_rel_cancel:.0f} %)\")\n",
-    "print(f\"City hotel bookings canceled: {ch_cancelations:,} ({ch_rel_cancel:.0f} %)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "res_book_per_month = hotel_data.loc[(hotel_data[\"hotel\"] == \"Resort Hotel\")].groupby(\"arrival_date_month\")[\"hotel\"].count()\n",
-    "res_cancel_per_month = hotel_data.loc[(hotel_data[\"hotel\"] == \"Resort Hotel\")].groupby(\"arrival_date_month\")[\"is_canceled\"].sum()\n",
-    "\n",
-    "cty_book_per_month = hotel_data.loc[(hotel_data[\"hotel\"] == \"City Hotel\")].groupby(\"arrival_date_month\")[\"hotel\"].count()\n",
-    "cty_cancel_per_month = hotel_data.loc[(hotel_data[\"hotel\"] == \"City Hotel\")].groupby(\"arrival_date_month\")[\"is_canceled\"].sum()\n",
-    "\n",
-    "res_cancel_data = pd.DataFrame({\"Hotel\": \"Resort Hotel\",\n",
-    "                                \"Month\": list(res_book_per_month.index),\n",
-    "                                \"Bookings\": list(res_book_per_month.values),\n",
-    "                                \"Cancelations\": list(res_cancel_per_month.values)})\n",
-    "cty_cancel_data = pd.DataFrame({\"Hotel\": \"City Hotel\",\n",
-    "                                \"Month\": list(cty_book_per_month.index),\n",
-    "                                \"Bookings\": list(cty_book_per_month.values),\n",
-    "                                \"Cancelations\": list(cty_cancel_per_month.values)})\n",
-    "\n",
-    "full_cancel_data = pd.concat([res_cancel_data, cty_cancel_data], ignore_index=True)\n",
-    "full_cancel_data[\"cancel_percent\"] = full_cancel_data[\"Cancelations\"] / full_cancel_data[\"Bookings\"] * 100\n",
-    "\n",
-    "# order by month:\n",
-    "ordered_months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \n",
-    "          \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
-    "full_cancel_data[\"Month\"] = pd.Categorical(full_cancel_data[\"Month\"], categories=ordered_months, ordered=True)\n",
-    "\n",
-    "# show figure:\n",
-    "plt.figure(figsize=(20, 10))\n",
-    "sns.barplot(x = \"Month\", y = \"cancel_percent\" , hue=\"Hotel\",\n",
-    "            hue_order = [\"City Hotel\", \"Resort Hotel\"], data=full_cancel_data)\n",
-    "plt.title(\"Cancelations per month\", fontsize=16)\n",
-    "plt.xlabel(\"Month\", fontsize=16)\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.ylabel(\"Cancelations [%]\", fontsize=16)\n",
-    "plt.legend(loc=\"upper right\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "lead_time                      0.293123\n",
-       "total_of_special_requests      0.234658\n",
-       "required_car_parking_spaces    0.195498\n",
-       "adults                         0.060017\n",
-       "days_in_waiting_list           0.054186\n",
-       "adr                            0.047557\n",
-       "babies                         0.032491\n",
-       "stays_in_week_nights           0.024765\n",
-       "arrival_date_year              0.016660\n",
-       "arrival_date_week_number       0.008148\n",
-       "arrival_date_day_of_month      0.006130\n",
-       "children                       0.005048\n",
-       "stays_in_weekend_nights        0.001791\n",
-       "Name: is_canceled, dtype: float64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cancel_corr = hotel_data.corr()[\"is_canceled\"]\n",
-    "cancel_corr.abs().sort_values(ascending=False)[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "is_canceled  reservation_status_date\n",
-       "0            12/8/2015                  243\n",
-       "             6/26/2016                  228\n",
-       "             5/29/2016                  225\n",
-       "             2/14/2016                  207\n",
-       "             11/22/2015                 202\n",
-       "                                       ... \n",
-       "1            4/7/2015                     1\n",
-       "             5/15/2015                    1\n",
-       "             6/14/2015                    1\n",
-       "             8/23/2015                    1\n",
-       "             9/13/2015                    1\n",
-       "Name: reservation_status_date, Length: 1707, dtype: int64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hotel_data.groupby(\"is_canceled\")[\"reservation_status_date\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}