market analysis in dublin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d4e766cc",
   "metadata": {},
   "source": [
    "Assignment\n",
    "\n",
    "Airbnb wants to understand\n",
    "\n",
    "What guests are searching in Dublin\n",
    "Which inquiries do hosts tend to accept\n",
    "What gaps exist between guest demand and host supply\n",
    "Any other information that deepens the understanding of the data\n",
    "The goal is to analyze, understand, visualize, and communicate the demand/supply of the market in Dublin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed9cd5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries/dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "contacts_file = (\"contacts.tsv\")\n",
    "contacts = pd.read_csv(contacts_file, sep=\"\\t\")\n",
    "\n",
    "searches_file = (\"searches.tsv\")\n",
    "searches = pd.read_csv(searches_file, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df375b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contacts\n",
      "id_guest          0.000000\n",
      "id_host           0.000000\n",
      "id_listing        0.000000\n",
      "ts_contact_at     0.000000\n",
      "ts_reply_at       0.077208\n",
      "ts_accepted_at    0.536367\n",
      "ts_booking_at     0.722101\n",
      "ds_checkin        0.000000\n",
      "ds_checkout       0.000000\n",
      "n_guests          0.000000\n",
      "n_messages        0.000000\n",
      "dtype: float64 \n",
      "\n",
      "Searches\n",
      "ds                      0.000000\n",
      "id_user                 0.000000\n",
      "ds_checkin              0.331561\n",
      "ds_checkout             0.331561\n",
      "n_searches              0.000000\n",
      "n_nights                0.331561\n",
      "n_guests_min            0.000000\n",
      "n_guests_max            0.000000\n",
      "origin_country          0.000000\n",
      "filter_price_min        0.627221\n",
      "filter_price_max        0.627221\n",
      "filter_room_types       0.546940\n",
      "filter_neighborhoods    0.962336\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Find % of null values in datasets \n",
    "print('Contacts')\n",
    "print(contacts.isna().sum()/len(contacts), '\\n')\n",
    "print('Searches')\n",
    "print(searches.isna().sum()/len(searches))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9251676a",
   "metadata": {},
   "source": [
    "The neighborhood column in searches has 96.2336% of null values. This could lead to inaccurate assumptions about the demand from people. When looking through the column, 'City Centre' was a common choice, so this should be investigated further with more data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3ed173",
   "metadata": {},
   "source": [
    "SEarches dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "797e3350",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop filter_neighborhoods column\n",
    "\n",
    "searches = searches.drop(columns=['filter_neighborhoods'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "468218e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Manipulation of searches dataset\n",
    "\n",
    "#Convert date column to datetime data type for easier analysis\n",
    "searches['ds'] = pd.to_datetime(searches['ds'])\n",
    "searches['ds_checkin'] = pd.to_datetime(searches['ds_checkin'])\n",
    "searches['ds_checkout'] = pd.to_datetime(searches['ds_checkout'])\n",
    "\n",
    "#How soon they want the room\n",
    "searches['length_preperation'] = searches['ds_checkin'] - searches['ds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "811a14bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>n_searches</th>\n",
       "      <th>n_nights</th>\n",
       "      <th>n_guests_min</th>\n",
       "      <th>n_guests_max</th>\n",
       "      <th>filter_price_min</th>\n",
       "      <th>filter_price_max</th>\n",
       "      <th>length_preperation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35737.000000</td>\n",
       "      <td>23888.000000</td>\n",
       "      <td>35737.000000</td>\n",
       "      <td>35737.000000</td>\n",
       "      <td>13322.000000</td>\n",
       "      <td>1.332200e+04</td>\n",
       "      <td>23888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.206565</td>\n",
       "      <td>7.672765</td>\n",
       "      <td>1.742955</td>\n",
       "      <td>2.105857</td>\n",
       "      <td>8.470200</td>\n",
       "      <td>9.019063e+07</td>\n",
       "      <td>51 days 08:11:53.730743469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.348746</td>\n",
       "      <td>21.557614</td>\n",
       "      <td>1.460440</td>\n",
       "      <td>1.817358</td>\n",
       "      <td>53.987679</td>\n",
       "      <td>2.978482e+08</td>\n",
       "      <td>65 days 18:56:19.491940518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>-1 days +00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>10 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.390000e+02</td>\n",
       "      <td>26 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.010000e+02</td>\n",
       "      <td>67 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>448.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1.073742e+09</td>\n",
       "      <td>604 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         n_searches      n_nights  n_guests_min  n_guests_max  \\\n",
       "count  35737.000000  23888.000000  35737.000000  35737.000000   \n",
       "mean       9.206565      7.672765      1.742955      2.105857   \n",
       "std       17.348746     21.557614      1.460440      1.817358   \n",
       "min        1.000000      0.000000      1.000000      1.000000   \n",
       "25%        1.000000      2.000000      1.000000      1.000000   \n",
       "50%        4.000000      3.000000      1.000000      2.000000   \n",
       "75%       10.000000      5.000000      2.000000      2.000000   \n",
       "max      448.000000    399.000000     16.000000     16.000000   \n",
       "\n",
       "       filter_price_min  filter_price_max          length_preperation  \n",
       "count      13322.000000      1.332200e+04                       23888  \n",
       "mean           8.470200      9.019063e+07  51 days 08:11:53.730743469  \n",
       "std           53.987679      2.978482e+08  65 days 18:56:19.491940518  \n",
       "min            0.000000      9.000000e+00           -1 days +00:00:00  \n",
       "25%            0.000000      8.600000e+01            10 days 00:00:00  \n",
       "50%            0.000000      1.390000e+02            26 days 00:00:00  \n",
       "75%            0.000000      3.010000e+02            67 days 00:00:00  \n",
       "max         1250.000000      1.073742e+09           604 days 00:00:00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Describe searches dataset\n",
    "\n",
    "#Helps understand the dataset and its distribution of values within columns better\n",
    "display(searches.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfeaceb2",
   "metadata": {},
   "source": [
    "This shows that the number of guests is usually 1 or 2. This can be understood since even at 75% the n_guests_min and n_guests_max are 2 and at 25% is 1. Leads to believe that smaller accommodations are preferred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e52539a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_searches           7.509258\n",
       "n_nights             9.942364\n",
       "n_guests_min         3.583798\n",
       "n_guests_max         3.148548\n",
       "filter_price_min    11.087745\n",
       "filter_price_max     2.999833\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculate skewness in searches dataset\n",
    "\n",
    "display(searches.skew(axis = 0, numeric_only = True, skipna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee864281",
   "metadata": {},
   "source": [
    "All numeric columns have a Fisher-Pearson coefficient value greater than 1. This results in a positive skewness. With more time, I would have used a transformation method such as log transformation to reduce the skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4b2259",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "216e2b2b",
   "metadata": {},
   "source": [
    "Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a384eb15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution plot of n_guests_min and n_guests_max\n",
    "sns.displot(searches, x = 'n_guests_min', color = 'brown')\n",
    "sns.displot(searches, x = 'n_guests_max', color = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f7961f",
   "metadata": {},
   "source": [
    "Both have similar distributions with 1 being the most popular option and 2 being the next popular option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f61eee7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None,\n",
       "  None]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#When were searches conducted\n",
    "\n",
    "ax = sns.displot(searches, x = 'ds', color = 'brown')\n",
    "[plt.setp(ax.get_xticklabels(), rotation=90) for ax in ax.axes.flat]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5233725",
   "metadata": {},
   "source": [
    "Noticed all date searches were between October 1st to October 14th. No major variation in when search was conducted between these dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6e35ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.311022189887232 %\n"
     ]
    }
   ],
   "source": [
    "#Percentage of dataset with a filter_price_max above 600\n",
    "\n",
    "print(len(searches[searches['filter_price_max'] > 600])/len(searches['filter_price_max'])*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8dfa5206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdwUlEQVR4nO3df5TVd33n8edLYjJjJjZMJSzhx4IR4xJRjCMlyR4djTXoupLuEYu7tmhhWU9To+22DdSzdXu27ImLx+pxtVlKVbqNRoyxQXebiGjS7TlOkOg1hkloRonJLBTGJN04HYEC7/3j+5nkMnMHLsz93s+9M6/HOZz7ve/7/d77nhzy4juf+/18vooIzMys+V6QuwEzs+nKAWxmlokD2MwsEwewmVkmDmAzs0wuyN3AZKxcuTLuueee3G2YmZ2NahXb+gz4pz/9ae4WzMzOW1sHsJlZO3MAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWSVuvhtYujh49Sl9f37j6ihUr6OjoyNCRmbWCUgNY0m8D64EAfgi8D3gR8CVgIfA48K6IeCbtvwlYB5wEbo6Ie8vsr1n6+vpYv75CV9ey52rDwxW2bYPe3t5sfZlZXqUFsKS5wM3Akoj4uaQdwBpgCbA7Im6VtBHYCNwiaUl6/SrgcuCbkl4eESfL6rGZurqWMXNmb+42zKyFlD0GfAHQKekCijPfg8AqYHt6fTtwY9peBdwREcci4gAwACwvuT8zs2xKC+CI+L/Ax4AngEPA/4uIbwCzI+JQ2ucQcFk6ZC7wZNVbDKbaaSRtkLRX0t6hoaGy2jczK11pASxpJsVZ7SKKIYWLJb3nTIfUqMW4QsTWiOiJiJ5Zs2Y1plkzswzKHIJ4M3AgIoYi4p+Au4BrgcOS5gCkxyNp/0FgftXx8yiGLMzMpqQyA/gJYIWkF0kScD3wCLATWJv2WQvcnbZ3AmskXSRpEbAY2FNif2ZmWZV2FUREPCDpTuB7wAng+8BWoAvYIWkdRUivTvvvS1dK9Kf9b5oqV0CYmdVS6nXAEfER4CNjyscozoZr7b8Z2FxmT2ZmrcJTkc3MMnEAm5ll4gA2M8vEi/FkcurUcSqV/nF1L9BjNn04gDMZGelny5YRqueSeIEes+nFAZxRZ+dSL9BjNo15DNjMLBMHsJlZJh6CaLBad7+oVCpELM3UkZm1Kgdwg9W6+8XQ0ACdnVfQ3Z2vLzNrPQ7gEoy9+8XwcCVXK2bWwjwGbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8uktACWdKWkStWfZyV9SFK3pF2SHkuPM6uO2SRpQNJ+STeU1ZuZWSsoLYAjYn9ELIuIZcBrgRHgq8BGYHdELAZ2p+dIWgKsAa4CVgKfkTSjrP7MzHJr1hDE9cCPIuInwCpge6pvB25M26uAOyLiWEQcAAaA5U3qz8ys6Zp1T7g1wBfT9uyIOAQQEYckXZbqc4Hq2wkPptppJG0ANgAsWLCgtIZbQa07LAOsWLGCjo6ODB2ZWSOVHsCSLgTeAWw62641ajGuELEV2ArQ09Mz7vWppNYdloeHK2zbBr29vdn6MrPGaMYZ8FuB70XE4fT8sKQ56ex3DnAk1QeB+VXHzQMONqG/ljb2DstmNnU0Ywz43Tw//ACwE1ibttcCd1fV10i6SNIiYDGwpwn9mZllUeoZsKQXAb8M/Ieq8q3ADknrgCeA1QARsU/SDqAfOAHcFBEny+yv1Zw6dZxKpf+555VKhYilGTsyszKVGsARMQL84pjaUxRXRdTafzOwucyeWtnISD9btowwa1bxfGhogM7OK+juztuXmZWjWVdBWJ06O5c+N+Y7PFzJ2YqZlcxTkc3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5llUmoAS7pU0p2SHpX0iKRrJHVL2iXpsfQ4s2r/TZIGJO2XdEOZvZmZ5Vb2GfAngXsi4hXAq4FHgI3A7ohYDOxOz5G0BFgDXAWsBD4jaUbJ/ZmZZXNBWW8s6cXA64H3AkTEceC4pFVAb9ptO3AfcAuwCrgjIo4BByQNAMuB75TVYzs6deo4lUr/uPqKFSvo6OjI0JGZna/SAhh4KTAEfE7Sq4EHgQ8CsyPiEEBEHJJ0Wdp/LtBXdfxgqp1G0gZgA8CCBQvK636Mo0eP0tfXN67e7OAbGelny5YRZs16vjY8XGHbNujt7W1aH2Y2eWUG8AXA1cAHIuIBSZ8kDTdMQDVqMa4QsRXYCtDT0zPu9bL09fWxfn2Frq5lz9VyBV9n51JmzmzuZ5pZ45UZwIPAYEQ8kJ7fSRHAhyXNSWe/c4AjVfvPrzp+HnCwxP7OWVfXMgefmTVMaV/CRcTfA09KujKVrgf6gZ3A2lRbC9ydtncCayRdJGkRsBjYU1Z/Zma5lXkGDPAB4HZJFwI/Bt5HEfo7JK0DngBWA0TEPkk7KEL6BHBTRJwsuT8zs2xKDeCIqAA9NV66foL9NwOby+zJzKxVlH0GbE3gS9PM2pMDeArwpWlm7ckBPEX40jSz9uMAnoRav/pXKhUilmbqyMzaiQN4Emr96j80NEBn5xV0d+fry8zagwN4ksb+6j88XMnVipm1Ga8HbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmZQawJIel/RDSRVJe1OtW9IuSY+lx5lV+2+SNCBpv6QbyuzNzCy3ZpwBvzEilkVET3q+EdgdEYuB3ek5kpYAa4CrgJXAZyTNaEJ/ZmZZ5BiCWAVsT9vbgRur6ndExLGIOAAMAMub356ZWXOUHcABfEPSg5I2pNrsiDgEkB4vS/W5wJNVxw6m2mkkbZC0V9LeoaGhEls3MyvXBSW//3URcVDSZcAuSY+eYV/VqMW4QsRWYCtAT0/PuNfNzNpFqWfAEXEwPR4BvkoxpHBY0hyA9Hgk7T4IzK86fB5wsMz+zMxyKi2AJV0s6ZLRbeAtwMPATmBt2m0tcHfa3gmskXSRpEXAYmBPWf2ZmeVW5hDEbOCrkkY/5wsRcY+k7wI7JK0DngBWA0TEPkk7gH7gBHBTRJwssT8zs6xKC+CI+DHw6hr1p4DrJzhmM7C5rJ7MzFqJZ8KZmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZVJXAEu6rp6amZnVr94z4E/VWTMzszqdcS0ISdcA1wKzJP1O1UsvBny7IDOzSTjbYjwXAl1pv0uq6s8C7yyrKTOz6eCMARwR9wP3S/p8RPykST2ZmU0L9S5HeZGkrcDC6mMi4k1lNGVmNh3UG8BfBm4DtgFeJN3MrAHqDeATEfGnpXZiDXXq1HEqlf5x9RUrVtDR0ZGhIzMbq94A/pqk36S4seax0WJEPF1KVzZpIyP9bNkywqxZz9eGhyts2wa9vb3Z+jKz59UbwKM30fy9qloAL21sO9ZInZ1LmTmzN3cbZjaBugI4IhaV3YiZ2XRTVwBL+vVa9Yj4i8a2Y2Y2fdQ7BPG6qu0Oirsafw9wAJuZnad6hyA+UP1c0i8A/7OUjszMponzXY5yBFjcyEbMzKabeseAv0Zx1QMUi/D8C2BHWU2ZmU0H9Y4Bf6xq+wTwk4gYLKEfK5EnZ5i1lnrHgO+XNJvnv4x7rLyWrCyenGHWWuq9I8a7gD3AauBdwAOS6lqOUtIMSd+X9PX0vFvSLkmPpceZVftukjQgab+kG879x7GzGZ2cMfqnq2tZ3obMprF6v4T7MPC6iFgbEb8OLAf+U53HfhB4pOr5RmB3RCwGdqfnSFoCrAGuAlYCn5HkRd/NbMqqN4BfEBFHqp4/Vc+xkuYB/4piFbVRq4DtaXs7cGNV/Y6IOBYRB4ABiqA3M5uS6v0S7h5J9wJfTM9/FfjfdRz3CeD3Of1uGrMj4hBARBySdFmqzwX6qvYbTLXTSNoAbABYsGBBne2bmbWeM57FSnqZpOsi4veA/wG8Cng18B1g61mOfTtwJCIerLMX1ajFuELE1ojoiYieWdXfJpmZtZmznQF/AvgDgIi4C7gLQFJPeu1fn+HY64B3SHobxfTlF0v6S+CwpDnp7HcOMDq0MQjMrzp+HnDwnH4aM7M2crZx3IUR8dDYYkTspbg90YQiYlNEzIuIhRRfrn0rIt4D7OT55S3XAnen7Z3AGkkXSVpEMdNuT70/iJlZuznbGfCZrs7vPM/PvBXYIWkd8ATFpW1ExD5JO4B+iskeN0WEb39kZlPW2QL4u5L+fUT8WXUxhWe9Y7tExH3AfWn7KYrV1GrttxnYXO/7mpm1s7MF8IeAr0r6dzwfuD3AhcCvlNiXmdmUd8YAjojDwLWS3gi8MpX/V0R8q/TOzMymuHrXgvg28O2SezEzm1bOdz1gMzObJAewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSb13pLIpqhTp45TqfSPq69YsYKOjjOtRmpmk+UAnuZGRvrZsmWE6rs7DQ9X2LYNent7s/VlNh04gI3OzqXMnNmbuw2zacdjwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSa+DtjGqTU7zjPjzBrPAWzjjJ0d55lxZuVwAFtNnh1nVj6PAZuZZVJaAEvqkLRH0g8k7ZP0R6neLWmXpMfS48yqYzZJGpC0X9INZfVmZtYKyhyCOAa8KSKGJb0Q+FtJfw38G2B3RNwqaSOwEbhF0hJgDXAVcDnwTUkvj4iTJfZodfCSlWblKC2AIyKA4fT0helPAKuA3lTfDtwH3JLqd0TEMeCApAFgOfCdsnq0+njJSrNylPolnKQZwIPAy4BPR8QDkmZHxCGAiDgk6bK0+1ygr+rwwVSzFuAv5cwar9Qv4SLiZEQsA+YByyW98gy7q9ZbjNtJ2iBpr6S9Q0NDDerUzKz5mnIVRET8A8VQw0rgsKQ5AOnxSNptEJhfddg84GCN99oaET0R0TOr+ndiM7M2U+ZVELMkXZq2O4E3A48CO4G1abe1wN1peyewRtJFkhYBi4E9ZfVnZpZbmWPAc4DtaRz4BcCOiPi6pO8AOyStA54AVgNExD5JO4B+4ARwk6+AMLOprMyrIB4CXlOj/hRw/QTHbAY2l9WTmVkr8Uw4M7NMHMBmZpk4gM3MMnEAm5ll4uUoJ3D06FH6+p6fmFepVIhYmrEjM5tqHMAT6OvrY/36Cl1dywAYGhqgs/MKurvz9mVmU4cD+Ay6upY9t/7B8HAlZyttYexvDaO8appZbQ5ga5ixvzWAV00zOxMHsDVU9W8NZnZmvgrCzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmpd2SSNJ84C+AfwacArZGxCcldQNfAhYCjwPviohn0jGbgHXASeDmiLi3rP5sck6dOk6l0n9arVKpELE0U0dm7afMe8KdAP5jRHxP0iXAg5J2Ae8FdkfErZI2AhuBWyQtAdYAVwGXA9+U9PKIOFlij3aeRkb62bJlhFmznq8NDQ3Q2XkF3d35+jJrJ6UFcEQcAg6l7Z9JegSYC6wCetNu24H7gFtS/Y6IOAYckDQALAe+U1aPNjmdnUtPuwHn8HAlVytmbakpd0WWtBB4DfAAMDuFMxFxSNJlabe5QF/VYYOpZgbA0aNH6evrG1dfsWIFHR0dGToym5zSA1hSF/AV4EMR8aykCXetUYsa77cB2ACwYMGCRrVpbaCvr4/16yt0dS17rjY8XGHbNujt7c3Wl9n5KjWAJb2QInxvj4i7UvmwpDnp7HcOcCTVB4H5VYfPAw6Ofc+I2ApsBejp6RkX0Da1dXUtO23Yw6ydlXYZmopT3T8HHomIj1e9tBNYm7bXAndX1ddIukjSImAxsKes/szMcivzDPg64NeAH0qqpNofALcCOyStA54AVgNExD5JO4B+iisobvIVEO2v1uVq4HFbMyj3Koi/pfa4LsD1ExyzGdhcVk/WfLUuV/O4rVmhKVdB2PQ29nI1Myt4KrKZWSYOYDOzTBzAZmaZeAzYWlKtWW9e7MemGgewtaRas9682I9NNQ5ga1ljZ715sR+bajwGbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxBMxrCWMnXrsacc2HTiA8boDrWDs1GNPO7bpwAGM1x1oFdVTj+udduxbHlk7cwAnXnegPfmWR9bOHMDW9nzLI2tXDmBrulrDBh5zt+nIAWxNV2vYoJFj7h4XtnbhALYsxg4bNHLM3ePC1i4cwDYleVzY2oED2OwMal0jDh7OsMZwAJudQa1rxD2cYY3iADY7i7HXiJs1ihfjMTPLpLQAlvRZSUckPVxV65a0S9Jj6XFm1WubJA1I2i/phrL6MjNrFWWeAX8eWDmmthHYHRGLgd3pOZKWAGuAq9Ixn5E0o8TezMyyKy2AI+JvgKfHlFcB29P2duDGqvodEXEsIg4AA8DysnozM2sFzR4Dnh0RhwDS42WpPhd4smq/wVQbR9IGSXsl7R0aGiq1WTOzMrXKVRCqUYtaO0bEVmArQE9PT819zOox9hrf48ePA3DhhRc+V/MaFVamZgfwYUlzIuKQpDnAkVQfBOZX7TcPONjk3myaGb8I/E7gcmbNuva5fbwutJWp2QG8E1gL3Joe766qf0HSx4HLgcXAnib3ZtPQ+EXgr/C60NY0pQWwpC8CvcBLJA0CH6EI3h2S1gFPAKsBImKfpB1AP3ACuCkiTpbVm5lZKygtgCPi3RO8dP0E+28GNpfVj5lZq2mVL+HM2obXG7ZGcQCbnaN61xs+35XUvALb9OEANjsP9aw3fL4rqXkFtunDAWxWovNdSa2e43ym3P4cwGaZjQ3Seid/+Ey5/TmAzTIbPyFk/OSPie4kffHFvvVSO3MAmzVRPUFaa/JH2XeStjwcwGYNMFGwjh1KmEyQlnknacvDAWzWAOcSrA5SG+UAtmmh3jPUyXCw2rlyANu04DFUa0UOYJs2fIY6sUZfU+xrlOvjADazhl9TXOv9nn32u9x8c4Vly4parQXwYXqFtAPYzIDzn7VX7/sND1dOGwaqtQD+dJtI4gA2m+JqDQeMPfus9wvJyQ4tVA8D1VoAf7pxAJtNIRNd7fGpT53ikkuufq429uyz3i8kc0x/nsrjyQ5gsylk4qs9bqjxBeQVZ5x9N5FGD1WczVRe88IBbDbFtPPVHtNtzQsHsJnVVO/klUZOcmmV67WbNezhADazmuoNw0aH5vmewTfyDiS1xs3LGPZwAJvZhOoNw1YY9qjn2uNR1aFc67ha4+ZlcACbWdupd6x47LXHo7WxZ7K1rlluBgewmbWdyaw+V4R35bnnjV6U6Vw4gM2sLZ3vsMfY8M65KNO0C+CJBtxz/QtoZs03fkZeHtMugCcecPeyhGbWXNMugCHfgLuZWbUX5G5gLEkrJe2XNCBpY+5+zMzK0lIBLGkG8GngrcAS4N2SluTtysysHK02BLEcGIiIHwNIugNYBfSf8ahzNHbI4ec/HwBGeOaZSyas1bNPo2s5PrNV+pgun9kqfUyXz5xMH0VuLKORFBENfcPJkPROYGVErE/Pfw34pYj4rap9NgAb0tMrgf11vPVLgJ82uN1mcN/N0449g/tutvPt+6cRsXJssdXOgFWjdtq/EBGxFdh6Tm8q7Y2Insk0loP7bp527Bncd7M1uu+WGgMGBoH5Vc/nAQcz9WJmVqpWC+DvAoslLZJ0IbAG2Jm5JzOzUrTUEEREnJD0W8C9wAzgsxGxrwFvfU5DFi3EfTdPO/YM7rvZGtp3S30JZ2Y2nbTaEISZ2bThADYzy2RKB3ArT2uW9FlJRyQ9XFXrlrRL0mPpcWbVa5vSz7Ff0g15ugZJ8yV9W9IjkvZJ+mA79C6pQ9IeST9Iff9RO/Sd+pgh6fuSvt5GPT8u6YeSKpL2tlHfl0q6U9Kj6e/4NaX2HRFT8g/Fl3g/Al4KXAj8AFiSu6+q/l4PXA08XFX7b8DGtL0R+GjaXpL6vwhYlH6uGZn6ngNcnbYvAf4u9dfSvVNcY96Vtl8IPACsaPW+Uy+/A3wB+Hob/T15HHjJmFo79L0dWJ+2LwQuLbPvpv+ATfwPeQ1wb9XzTcCm3H2N6XHhmADeD8xJ23OA/bV6p7hK5Jrc/ade7gZ+uZ16B14EfA/4pVbvm+Ja+N3Am6oCuKV7Tp9dK4Bbum/gxcAB0sUJzeh7Kg9BzAWerHo+mGqtbHZEHAJIj5elekv+LJIWAq+hOJts+d7Tr/IV4AiwKyLaoe9PAL8PnKqqtXrPUMxg/YakB9PyAdD6fb8UGAI+l4Z8tkm6mBL7nsoBfNZpzW2k5X4WSV3AV4APRcSzZ9q1Ri1L7xFxMiKWUZxVLpf0yjPsnr1vSW8HjkTEg/UeUqOW6+/JdRFxNcXKhjdJev0Z9m2Vvi+gGBb804h4DfCPFEMOE5l031M5gNtxWvNhSXMA0uORVG+pn0XSCynC9/aIuCuV26J3gIj4B+A+YCWt3fd1wDskPQ7cAbxJ0l/S2j0DEBEH0+MR4KsUKx22et+DwGD6zQjgTopALq3vqRzA7TiteSewNm2vpRhfHa2vkXSRpEXAYmBPhv6QJODPgUci4uNVL7V075JmSbo0bXcCbwYepYX7johNETEvIhZS/P39VkS8p5V7BpB0saRLRreBtwAP0+J9R8TfA09KujKVrqdYCre8vnMM0DdxUP1tFN/S/wj4cO5+xvT2ReAQ8E8U/5KuA36R4guXx9Jjd9X+H04/x37grRn7/pcUv2Y9BFTSn7e1eu/Aq4Dvp74fBv4w1Vu676peenn+S7iW7pliLPUH6c++0f/3Wr3v1McyYG/6e/JXwMwy+/ZUZDOzTKbyEISZWUtzAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gy0LSzWm5v2eUlgqV9J8l/W7afq+ky5vc0+WS7mzmZ9r01lL3hLNp5TcpLlw/MMHr76WYMFH31E5JF0TEifNpJh17EHjn+Rxvdj58BmxNJ+k2itlSOyX9tqT/Pub1dwI9wO1pQe9OSa+VdH9aXeveqrn590n6r5LuBz44wed9XtJtkv6PpL9Li9yMnmV/WdLXKFbuWqi0QH5aOe1jaVHxhyR9INVr9jHB594n6U8k/U0623+dpLvSwt5/XLXfX6X32ze6cpikf572e4mkF6Te33L+/9WtFfkM2JouIt4vaSXwRuDtNV6/U8XdsX83IvamxX8+BayKiCFJvwpsBn4jHXJpRLzhLB+7EHgDcAXwbUkvS/VrgFdFxNNpec1RGygW2X5NFHfr7q6jj1qOR8TrVdw55G7gtcDTwI8k/UlEPAX8Rvr8TuC7kr4SET+R9FHgNorlPvsj4htn+RmtzTiArR1cCbwS2FWsBcQMinU0Rn2pjvfYERGngMck/Rh4Rarvioina+z/ZuC20SGNFJCvPEsftYwuAPVDYF+kdWVTD/OBp4CbJf1K2m8+xaIuT0XENkmrgfdTrFFgU4wD2NqBKMLrmgle/8c63mPsoiejzyc6VjWOOVsftRxLj6eqtkefXyCplyLsr4mIEUn3AR0Akl5EscQhQBfws3P4XGsDHgO2VvUzinvOQbHS1CxJ10CxHrGkq87x/VansdQrKMaf959l/28A75d0QfrM7gb1MdYvAM+k8H0FxX3qRn0UuB34Q+DPJvk51oIcwNaqPg/cpuIWQjMork74qKQfUCyBee05vt9+4H7gr4H3R8TRs+y/DXgCeCh95r+NiOMN6GOseyjOhB8C/gvQByDpDcDrKG4AeTtwXNL7JvlZ1mK8HKVNeZI+T7GWrq/xtZbiM2Azs0z8JZxNGZI+DKweU/5yRLy35M/9NMX926p9MiI+V+bnWvvzEISZWSYegjAzy8QBbGaWiQPYzCwTB7CZWSb/HwNxfkwS6nW7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of filter_price_max of searches\n",
    "\n",
    "#Removing the set upper limit\n",
    "searches_maxprice_removed = searches[searches['filter_price_max'] <= 600]\n",
    "\n",
    "#Distribution plot of filter_price_max column\n",
    "sns.displot(x=searches_maxprice_removed[\"filter_price_max\"], color = 'blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae75f3e6",
   "metadata": {},
   "source": [
    "Filter_price_max was chosen instead of filter_price_min due to the min usually being set at $0\n",
    "\n",
    "To further help better visualize the trend we set the filter price max as less or equal to 600. 600 was chosen as the limit since only 14.25% of the dataset has values greater than 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e786c5c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.396423874415872 % \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of length_preperation of searches\n",
    "\n",
    "#Percentage of dataset beyond 100 days\n",
    "distribution = searches[\"length_preperation\"] / np.timedelta64(1, 'D')\n",
    "print(len(distribution[distribution > 100])/len(distribution)*100, '% \\n')\n",
    "\n",
    "#Remove values beyond 100 days\n",
    "distribution = distribution[distribution < 100]\n",
    "\n",
    "#Distribution plot of length_preperation column\n",
    "sns.displot(x=distribution, color = 'purple')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3059b0a4",
   "metadata": {},
   "source": [
    "100 days was chosen as the limit since only 14.06% of the dataset exists beyond that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f6ba05bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.737387021854101 % \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXm0lEQVR4nO3df7DldX3f8edrF11pFIW60Ju7pGC7kwkw9deGopiMCalubGYhVs1mbNhMaXZisSNJmxbqTExmyoy2acaQESwxliW1kk0iZWOFSNcfmXRQXCmKgMgaVPZyyq6mCWTSYuG++8f5LjlzOffu2d37PZ/74/mYOXO+38/38z3f937v3df9ns/5fr8nVYUkafo2tC5AktYrA1iSGjGAJakRA1iSGjGAJamRU1oX0Jft27fXHXfc0boMSQLIuMY1ewT87W9/u3UJkrSkNRvAkrTSGcCS1IgBLEmNGMCS1IgBLEmNGMCS1IgBLEmNGMCS1IgBLEmNGMCS1IgBLEmNGMCS1MiavRvaSjc/P89gMABgZmaGDRv8WyitNwZwI4PBgMGOHcOZffuYnZ1tW5CkqTOAG5rZtKl1CZIa8n2vJDViAEtSI70GcJJvJLkvyb1JDnRtZyS5M8nD3fPpI/2vSXIwyUNJ3jjS/urudQ4muS7J2K/3kKTVZBpHwD9SVa+oqm3d/NXA/qraCuzv5klyHrATOB/YDlyfZGO3zg3AbmBr99g+hbolqVcthiAuBfZ003uAy0bab6mqp6rqEeAgcGGSGeC0qrqrqgq4eWQdSVq1+g7gAj6Z5ItJdndtZ1XVAKB7PrNrnwUeHVn3UNc2200vbH+OJLuTHEhy4MiRI8v4z5Ck5df3aWgXV9VjSc4E7kzy1SX6jhvXrSXan9tYdSNwI8C2bdvG9pGklaLXI+Cqeqx7PgzcClwIPN4NK9A9H+66HwLOHll9C/BY175lTPu6ND8/z9zcHHNzc8zPz7cuR9JJ6C2Ak3xPkhcdnQbeAHwF2Afs6rrtAm7rpvcBO5NsSnIuww/b7u6GKZ5MclF39sPlI+usO0evoBvs2PHspcySVqc+hyDOAm7tzhg7BfgvVXVHki8Ae5NcAXwLeCtAVd2fZC/wAPA0cGVVPdO91juAm4BTgdu7x7rlFXTS2tBbAFfVnwIvH9P+HeCSRda5Frh2TPsB4ILlrlGSWvJKOElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEZ6D+AkG5P8zyQf7+bPSHJnkoe759NH+l6T5GCSh5K8caT91Unu65ZdlyR91y1JfZvGEfC7gAdH5q8G9lfVVmB/N0+S84CdwPnAduD6JBu7dW4AdgNbu8f2KdQtSb3qNYCTbAH+IfChkeZLgT3d9B7gspH2W6rqqap6BDgIXJhkBjitqu6qqgJuHllHklatvo+A3w/8K2B+pO2sqhoAdM9ndu2zwKMj/Q51bbPd9ML250iyO8mBJAeOHDmyLP8ASepLbwGc5CeAw1X1xUlXGdNWS7Q/t7HqxqraVlXbNm/ePOFmJamNU3p87YuBHUneBLwAOC3JfwYeTzJTVYNueOFw1/8QcPbI+luAx7r2LWPaJWlV6+0IuKquqaotVXUOww/XPlVV/xjYB+zquu0Cbuum9wE7k2xKci7DD9vu7oYpnkxyUXf2w+Uj60jSqtXnEfBi3gvsTXIF8C3grQBVdX+SvcADwNPAlVX1TLfOO4CbgFOB27uHJK1qUwngqvoM8Jlu+jvAJYv0uxa4dkz7AeCC/iqUpOnzSjhJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGWnwn3JoyPz/PYDAAYGZmhg0b/JsmaTIG8EkaDAYMduwYzuzbx+zsbNuCJK0aBvAymNm0qXUJklYh3y9LUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1ckrrAtS/+fl5BoMBADMzM2zY4N9daSUwgNeBwWDAYMeO4cy+fczOzrYtSBJgAK8bM5s2tS5B0gK9vRdN8oIkdyf5UpL7k/xq135GkjuTPNw9nz6yzjVJDiZ5KMkbR9pfneS+btl1SdJX3ZI0LX0OBj4F/GhVvRx4BbA9yUXA1cD+qtoK7O/mSXIesBM4H9gOXJ9kY/daNwC7ga3dY3uPdUvSVPQWwDX0l93s87pHAZcCe7r2PcBl3fSlwC1V9VRVPQIcBC5MMgOcVlV3VVUBN4+sI0mrVq8fhyfZmORe4DBwZ1V9HjirqgYA3fOZXfdZ4NGR1Q91bbPd9ML2cdvbneRAkgNHjhxZ1n+LJC23XgO4qp6pqlcAWxgezV6wRPdx47q1RPu47d1YVduqatvmzZuPu15JmqapnBBaVX8OfIbh2O3j3bAC3fPhrtsh4OyR1bYAj3XtW8a0S9Kq1udZEJuTvKSbPhX4MeCrwD5gV9dtF3BbN70P2JlkU5JzGX7Ydnc3TPFkkou6sx8uH1lHklatPs8DngH2dGcybAD2VtXHk9wF7E1yBfAt4K0AVXV/kr3AA8DTwJVV9Uz3Wu8AbgJOBW7vHpK0qk0UwEkurqr/cay2UVX1ZeCVY9q/A1yyyDrXAteOaT8ALDV+LEmrzqRDEL85YZskaUJLHgEneQ3wWmBzkl8cWXQasHH8WpKkSRxrCOL5wAu7fi8aaX8CeEtfRUnSerBkAFfVZ4HPJrmpqr45pZokaV2Y9CyITUluBM4ZXaeqfrSPoiRpPZg0gH8P+CDwIeCZY/SVJE1g0gB+uqpu6LUSSVpnJj0N7Q+T/LMkM939fM9IckavlUnSGjfpEfDRS4d/aaStgJctbzmStH5MFMBVdW7fhUjSejPppciXj2uvqpuXtxxJWj8mHYL4wZHpFzC8l8M9DL+dQpJ0AiYdgvjno/NJXgz8Ti8VSdI6caL3A/4rhvfrlSSdoEnHgP+Qv/4aoI3ADwB7+ypKktaDSceAf21k+mngm1V1aLHOkqRjm2gIorspz1cZ3hHtdOC7fRYlSevBRAGc5G3A3Qy/PuhtwOeTeDtKSToJkw5BvBv4wao6DMMv3AT+O/D7fRUmSWvdpGdBbDgavp3vHMe6kqQxJj0CviPJHwEf7eZ/CvhEPyVJ0vpwrO+E+7vAWVX1S0neDLwOCHAX8JEp1CdJa9axhhHeDzwJUFUfq6pfrKpfYHj0+/5+S5Okte1YAXxOVX15YWNVHWD49USSpBN0rAB+wRLLTl3OQiRpvTlWAH8hyc8tbExyBfDFfkqSpPXhWGdBXAXcmuTt/HXgbgOeD/xkj3VJ0pq3ZABX1ePAa5P8CHBB1/zfqupTvVcmSWvcpPcD/jTw6Z5rkaR1xavZJKkRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJamR3gI4ydlJPp3kwST3J3lX135GkjuTPNw9nz6yzjVJDiZ5KMkbR9pfneS+btl1SdJX3ZI0LX0eAT8N/Iuq+gHgIuDKJOcBVwP7q2orsL+bp1u2Ezgf2A5cn2Rj91o3ALuBrd1je491S9JU9BbAVTWoqnu66SeBB4FZ4FJgT9dtD3BZN30pcEtVPVVVjwAHgQuTzACnVdVdVVXAzSPrSNKqNZUx4CTnAK8EPg+cVVUDGIY0cGbXbRZ4dGS1Q13bbDe9sH3cdnYnOZDkwJEjR5b13yBJy633AE7yQuAPgKuq6omluo5pqyXan9tYdWNVbauqbZs3bz7+YiVpinoN4CTPYxi+H6mqj3XNj3fDCnTPh7v2Q8DZI6tvAR7r2reMaZekVa3PsyAC/DbwYFX9+siifcCubnoXcNtI+84km5Kcy/DDtru7YYonk1zUveblI+tI0qp1So+vfTHwM8B9Se7t2v4N8F5gb5IrgG8BbwWoqvuT7AUeYHgGxZVV9Uy33juAm4BTgdu7hyStar0FcFX9CePHbwEuWWSda4Frx7QfAC5YvuokqT2vhJOkRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRk5pXYBWrvn5eQaDAQAzMzNs2ODfa2k5GcBa1GAwYLBjx3Bm3z5mZ2fbFiStMQawljSzaVPrEqQ1y/eUktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSId0PTiuH9h7XeGMBaMbz/sNYbA1grivcf1nriezxJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGegvgJB9OcjjJV0bazkhyZ5KHu+fTR5Zdk+RgkoeSvHGk/dVJ7uuWXZckfdUsSdPU5xHwTcD2BW1XA/uraiuwv5snyXnATuD8bp3rk2zs1rkB2A1s7R4LX1OSVqXeAriq/hj4swXNlwJ7uuk9wGUj7bdU1VNV9QhwELgwyQxwWlXdVVUF3DyyjiStatMeAz6rqgYA3fOZXfss8OhIv0Nd22w3vbB9rCS7kxxIcuDIkSPLWrgkLbeV8iHcuHHdWqJ9rKq6saq2VdW2zZs3L1txktSHaQfw492wAt3z4a79EHD2SL8twGNd+5Yx7ZK06k07gPcBu7rpXcBtI+07k2xKci7DD9vu7oYpnkxyUXf2w+Uj62iFmp+fZ25ujrm5Oebn51uXI61Yvd0NLclHgdcDL01yCHgP8F5gb5IrgG8BbwWoqvuT7AUeAJ4GrqyqZ7qXegfDMypOBW7vHlrBvK2kNJneAriqfnqRRZcs0v9a4Nox7QeAC5axNE2Bt5WUjm2lfAgnSeuOASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktTIKa0LkE7W/Pw8g8EAgJmZGTZs8LhCq4MBrFVvMBgw2LFjOLNvH7Ozs20LkiZkAGtNmNm0qXUJ0nHzvZokNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNeKVcJKe5X01pssA1rpl2DyX99WYLgNY65ZhM5731ZgeA1jrmmGjlnzPJUmNeAQsHSfHjrVcDGDpODl2rOViAEsnwLFjLQcDWJqCaQ9brPXtrRUGsDQFJzpscaLBNu1hEodlTowBLE3JiQxbnEywTXuY5ES2t96PnA1gaYVby+PN035nsNIYwJKamtY7g5UY2u0rmFCS7UkeSnIwydWt65HU1symTccV3kdDe7Bjx7NBPIn5+Xnm5uaYm5tjfn7+REpd1Ko4Ak6yEfgA8A+AQ8AXkuyrqgfaViZpNZn2OPyxrIoABi4EDlbVnwIkuQW4FFjWAJ6bmzvudQaDATz11NGZFbneaqhxra+3Gmpc6+ud1LZ6kqrq7cWXS5K3ANur6p928z8D/P2qeueCfruB3d3s9wMPHeemXgp8+yTLXS7WMp61jLdSalkpdcDKquXbVbV9YeNqOQLOmLbn/OWoqhuBG094I8mBqtp2ousvJ2sZz1rGWym1rJQ6YGXVspjV8iHcIeDskfktwGONapGkZbFaAvgLwNYk5yZ5PrAT2Ne4Jkk6KatiCKKqnk7yTuCPgI3Ah6vq/h42dcLDFz2wlvGsZbyVUstKqQNWVi1jrYoP4SRpLVotQxCStOYYwJLUyLoM4GNd1pyh67rlX07yqp7qODvJp5M8mOT+JO8a0+f1Sf4iyb3d45f7qKXb1jeS3Ndt58CY5dPaL98/8u+9N8kTSa5a0Ke3/ZLkw0kOJ/nKSNsZSe5M8nD3fPoi6y7bJfOL1PHvk3y12/+3JnnJIusu+bNcplp+JcncyM/gTYusu6y3EViklt8dqeMbSe5dZN1l3S8nrarW1YPhh3hfB14GPB/4EnDegj5vAm5neP7xRcDne6plBnhVN/0i4Gtjank98PEp7ZtvAC9dYvlU9suYn9f/Av72tPYL8MPAq4CvjLT9O+Dqbvpq4H0n8ru1DHW8ATilm37fuDom+VkuUy2/AvzLCX5+y7ZPFqtlwfL/APzyNPbLyT7W4xHws5c1V9V3gaOXNY+6FLi5hj4HvCTJzHIXUlWDqrqnm34SeBBYyXeynsp+WeAS4OtV9c2et/Osqvpj4M8WNF8K7Omm9wCXjVl1kt+tk6qjqj5ZVU93s59jeE587xbZJ5NY1n1yrFqSBHgb8NGT2ca0rMcAngUeHZk/xHNDb5I+yyrJOcArgc+PWfyaJF9KcnuS83sso4BPJvlid1n3QlPfLwzP+V7sP9O09gvAWVU1gOEfTuDMMX2mvX/+CcN3JOMc62e5XN7ZDYd8eJFhmWnvkx8CHq+qhxdZPq39MpH1GMCTXNY80aXPyyXJC4E/AK6qqicWLL6H4dvvlwO/CfzXvuoALq6qVwE/DlyZ5IcXljpmnT73y/OBHcDvjVk8zf0yqantnyTvBp4GPrJIl2P9LJfDDcDfAV4BDBi+9X9OqWPa+jz39adZ+uh3GvtlYusxgCe5rHlqlz4neR7D8P1IVX1s4fKqeqKq/rKb/gTwvCQv7aOWqnqsez4M3Mrw7eOoaV8S/uPAPVX1+MIF09wvncePDrd0z4fH9JnK/kmyC/gJ4O3VDWwuNMHP8qRV1eNV9UxVzQO/tcg2pvl/6RTgzcDvLtZnGvvleKzHAJ7ksuZ9wOXdp/4XAX9x9O3ncurGq34beLCqfn2RPn+r60eSCxn+zL7TQy3fk+RFR6cZftjzlQXdprJfRix6NDOt/TJiH7Crm94F3DamT++XzCfZDvxrYEdV/dUifSb5WS5HLaPj/z+5yDameRuBHwO+WlWHxi2c1n45Lq0/BWzxYPhp/tcYfjr77q7t54Gf76bD8AbwXwfuA7b1VMfrGL4d+zJwb/d404Ja3gncz/DT488Br+2plpd12/hSt71m+6Xb1t9gGKgvHmmbyn5hGPoD4P8xPIK7AvibwH7g4e75jK7v9wKfWOp3a5nrOMhwTPXo78sHF9ax2M+yh1p+p/s9+DLDUJ3pe58sVkvXftPR34+Rvr3ul5N9eCmyJDWyHocgJGlFMIAlqREDWJIaMYAlqREDWJIaMYAlqREDWBqRZFuS647R55zRWyEuWPazSb63n+q01qyK74STpqWqDgAnc5/Yn2V4dZXf2q1j8ghYa0Z3ZPpgkt/K8Ab3n0xy6iJ9P5PkfUnuTvK1JD/Utb8+yce76c0Z3nz9niT/Mck3R+43sXHhdpK8BdgGfKS74fepSd6b5IHujmG/NpUdoVXDANZasxX4QFWdD/w58I+W6HtKVV0IXAW8Z8zy9wCfquHds24Fvm+p7VTV7zM8en57Vb0COJXhPRLOr6q/B/zbE/9naS0ygLXWPFJV93bTXwTOWaLvx47R73UMbyBOVd0B/O/j3M4TwP8FPpTkzcDYm+do/TKAtdY8NTL9DEt/zvHUMfqNu5ftxNup4TdXXMjwdqOXAXcs8XpahwxgaXF/wvDrbUjyBmDsF3Eu8CTD7/c7eqP9F9fwfsVXMbxxufQsz4KQFverwEeT/BTwWYa3QHwSeOES69wEfDDJ/2F4Q/nbkryA4dH0L/RbrlYbb0cpLSLJJuCZqno6yWuAG7oP16Rl4RGwtLjvA/Ym2QB8F/i5xvVojfEIWGtakg8AFy9o/o2q+k8t6pFGGcCS1IhnQUhSIwawJDViAEtSIwawJDXy/wHHcai28MnVEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of n_nights of searches\n",
    "\n",
    "#Percentage of dataset beyond 20 nights\n",
    "print(len(searches[searches['n_nights'] > 20])/len(searches['n_nights'])*100, '% \\n')\n",
    "\n",
    "#Remove n_nights beyond 20 days\n",
    "searches_within_twenty = searches[searches['n_nights'] < 20]\n",
    "\n",
    "#Distribution plot of length_preperation column\n",
    "sns.displot(searches_within_twenty, x='n_nights', color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9a6af5",
   "metadata": {},
   "source": [
    "Removing n_nights beyond 20 days since only 7.3% of the dataset exists beyond 20 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f596d188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of months of ds_checkin of searches\n",
    "\n",
    "checkin_month = pd.DatetimeIndex(searches['ds_checkin']).month\n",
    "\n",
    "#Distribution plot of length_preperation column\n",
    "sns.displot(checkin_month, color = 'yellow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795a5767",
   "metadata": {},
   "source": [
    "Used only the check-in month, cause checkout is usually within 5/6 days. The mean of n_nights after removing the upper outlier limit is 5.6, so assumed 5 or 6 days after the check-in date people usually checkout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "92a988a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([',Entire home/apt,Entire home/apt,Private room,Private room', nan,\n",
       "       ',Entire home/apt',\n",
       "       'Entire home/apt,Entire home/apt,Private room,Private room',\n",
       "       'Entire home/apt', ',Shared room,Private room',\n",
       "       'Entire home/apt,Private room,Shared room,Private room,Shared room',\n",
       "       'Private room', 'Entire home/apt,Private room', ',Private room',\n",
       "       ',Entire home/apt,Private room',\n",
       "       ',Entire home/apt,Private room,Private room',\n",
       "       'Entire home/apt,Private room,Shared room',\n",
       "       ',Entire home/apt,Entire home/apt,Private room',\n",
       "       ',Entire home/apt,Entire home/apt,Shared room,Shared room'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Types of rooms searched for\n",
    "\n",
    "searches['filter_room_types'].unique()[0:15] #Display first 15 unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57e9ede",
   "metadata": {},
   "source": [
    "Most of the room types requested were entire home/apt and private rooms sometimes shared rooms. If given more time, I would have cleaned this column since most filter values are repeated within the same cell. On the Airbnb website, there are only 4 values in the type of place:\n",
    "\n",
    "1.Entire Place\n",
    "2.Private Room\n",
    "3.Hotel Room\n",
    "4.Shared Room\n",
    "So searching how often these 4 strings occur would be how I go about it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b02de290",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>6608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>5811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>4832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>3444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>2170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>1759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>1085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR</th>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count\n",
       "origin_country       \n",
       "IE               6608\n",
       "US               5811\n",
       "GB               4832\n",
       "FR               3444\n",
       "IT               2333\n",
       "DE               2170\n",
       "ES               1759\n",
       "CA               1085\n",
       "AU                962\n",
       "NL                843\n",
       "BR                636\n",
       "CH                535\n",
       "BE                386\n",
       "AT                320\n",
       "RU                274"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find top 15 countries where searches originate from\n",
    "\n",
    "#Group by origin country and finding the count of each country\n",
    "search_origin = searches.groupby(\"origin_country\").agg({'origin_country' : 'count'})\n",
    "search_origin.columns = ['count']\n",
    "\n",
    "search_origin = search_origin.sort_values('count', ascending = False) #Sort count in descending order\n",
    "search_origin.nlargest(15, 'count') #Find the 15 largest values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c981c95",
   "metadata": {},
   "source": [
    "Contacts Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ead9ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_guest                   object\n",
       "id_host                    object\n",
       "id_listing                 object\n",
       "ts_contact_at      datetime64[ns]\n",
       "ts_reply_at        datetime64[ns]\n",
       "ts_accepted_at     datetime64[ns]\n",
       "ts_booking_at      datetime64[ns]\n",
       "ds_checkin         datetime64[ns]\n",
       "ds_checkout        datetime64[ns]\n",
       "n_guests                    int64\n",
       "n_messages                  int64\n",
       "accepted                     bool\n",
       "length_stay       timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>n_guests</th>\n",
       "      <th>n_messages</th>\n",
       "      <th>length_stay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7823.000000</td>\n",
       "      <td>7823.000000</td>\n",
       "      <td>7823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.422600</td>\n",
       "      <td>6.319954</td>\n",
       "      <td>5 days 19:25:32.864629937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.617347</td>\n",
       "      <td>6.472827</td>\n",
       "      <td>14 days 23:45:24.447710564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>334 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          n_guests   n_messages                 length_stay\n",
       "count  7823.000000  7823.000000                        7823\n",
       "mean      2.422600     6.319954   5 days 19:25:32.864629937\n",
       "std       1.617347     6.472827  14 days 23:45:24.447710564\n",
       "min       1.000000     1.000000             1 days 00:00:00\n",
       "25%       1.000000     2.000000             2 days 00:00:00\n",
       "50%       2.000000     4.000000             3 days 00:00:00\n",
       "75%       3.000000     8.000000             5 days 00:00:00\n",
       "max      16.000000   102.000000           334 days 00:00:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Manipulation of contacts dataset\n",
    "\n",
    "#Convert date columns to datetime data type \n",
    "contacts['ts_contact_at'] = pd.to_datetime(contacts['ts_contact_at'])\n",
    "contacts['ts_reply_at'] = pd.to_datetime(contacts['ts_reply_at'])\n",
    "contacts['ts_accepted_at'] = pd.to_datetime(contacts['ts_accepted_at'])\n",
    "contacts['ts_booking_at'] = pd.to_datetime(contacts['ts_booking_at'])\n",
    "contacts['ds_checkin'] = pd.to_datetime(contacts['ds_checkin'])\n",
    "contacts['ds_checkout'] = pd.to_datetime(contacts['ds_checkout'])\n",
    "contacts['accepted'] = np.where(np.isnan(contacts['ts_accepted_at']), False, True)\n",
    "\n",
    "contacts['length_stay'] = contacts['ds_checkout'] - contacts['ds_checkin']\n",
    "\n",
    "#Understand dataset\n",
    "display(contacts.dtypes)\n",
    "display(contacts.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ec29242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_guests      2.441468\n",
       "n_messages    3.696440\n",
       "accepted      0.145883\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculate skewness in contacts dataset\n",
    "\n",
    "display(contacts.skew(axis = 0, numeric_only = True, skipna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48830afb",
   "metadata": {},
   "source": [
    "All columns have a Fisher-Pearson coefficient value greater than 1. Except for accepted, which could be due to it being derived from an existing column. With more time, I would have used a transformation method such as box-cox to reduce the skewness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "485a2827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of guests stayed\n",
    "\n",
    "contacts_less8 = contacts[contacts['n_guests'] < 8]\n",
    "sns.displot(contacts_less8, x = 'n_guests', hue = 'accepted', multiple=\"dodge\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff15f655",
   "metadata": {},
   "source": [
    "Choosing less than 8 guests, since only 1.46% (114 values) of the contacts dataset has 8 or more guests. To better visualize the majority distribution we removed rows with 8 or more guests.\n",
    "\n",
    "2 guests is the most popular option to book, but 1 guest is the most popularly searched option. This leads me to believe there is a lack of supply of viable single guest rooms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b094b49a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5993934381031155"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conversion rate from accepting to booking\n",
    "\n",
    "contacts['ts_booking_at'].count()/contacts['ts_accepted_at'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "25288dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 421.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Timeframe of when guests or accepted vs rejected\n",
    "\n",
    "contacts['month_checkin'] = contacts['ds_checkin'].dt.month #Extract month from checkin date\n",
    "contacts_checkin = contacts[contacts['month_checkin'] > 9] #Use only peak season months (Oct, Nov, Dec)\n",
    "\n",
    "#Distribution of checkin among October, November, and December and split by acceptance\n",
    "sns.displot(contacts_checkin, x='month_checkin', hue = 'accepted', multiple=\"dodge\")\n",
    "plt.xticks([10, 11, 12])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a7875094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Merge datasets for more analysis\n",
    "\n",
    "merged_datasets = contacts.merge(searches, left_on='id_guest', right_on='id_user')\n",
    "\n",
    "#Check difference between prices searched between accepted/rejected applicants\n",
    "\n",
    "merged_pricemax_filter = merged_datasets.loc[(merged_datasets['filter_price_max'] <= 600)]\n",
    "\n",
    "sns.displot(merged_pricemax_filter, x=\"filter_price_max\", hue=\"accepted\", multiple=\"dodge\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d77580",
   "metadata": {},
   "source": [
    "To further help better visualize the trend we set the filter price max as less or equal to 600. 600 was chosen as the limit since only 14.25% of the dataset has values greater than 600.\n",
    "\n",
    "As seen, more people are rejected compared than accepted with an average acceptance rate of 43%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9556fae1",
   "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>accepted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classification_max_price</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-100</th>\n",
       "      <td>0.411160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100-200</th>\n",
       "      <td>0.430308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200-300</th>\n",
       "      <td>0.431149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300-400</th>\n",
       "      <td>0.450488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400-500</th>\n",
       "      <td>0.485549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500-600</th>\n",
       "      <td>0.422297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600+</th>\n",
       "      <td>0.433122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          accepted\n",
       "classification_max_price          \n",
       "0-100                     0.411160\n",
       "100-200                   0.430308\n",
       "200-300                   0.431149\n",
       "300-400                   0.450488\n",
       "400-500                   0.485549\n",
       "500-600                   0.422297\n",
       "600+                      0.433122"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Classify dataset based on filter_price_max\n",
    "\n",
    "def label_price (row):\n",
    "    if (row['filter_price_max'] >= 0) & (row['filter_price_max'] < 100):\n",
    "        return '0-100'\n",
    "    \n",
    "    elif (row['filter_price_max'] >= 100) & (row['filter_price_max'] < 200):\n",
    "        return '100-200'\n",
    "\n",
    "    elif (row['filter_price_max'] >= 200) & (row['filter_price_max'] < 300):\n",
    "        return '200-300'\n",
    "    \n",
    "    elif (row['filter_price_max'] >= 300) & (row['filter_price_max'] < 400):\n",
    "        return '300-400'\n",
    "\n",
    "    elif (row['filter_price_max'] >= 400) & (row['filter_price_max'] < 500):\n",
    "        return '400-500'\n",
    "    \n",
    "    elif (row['filter_price_max'] >= 500) & (row['filter_price_max'] < 600):\n",
    "        return '500-600'\n",
    "    \n",
    "    else:\n",
    "        return '600+'\n",
    "\n",
    "merged_datasets['classification_max_price'] = merged_datasets.apply(lambda row: label_price(row), axis=1)\n",
    "\n",
    "merged_datasets.groupby('classification_max_price').agg({'accepted': 'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c7c961",
   "metadata": {},
   "source": [
    "Based on this table, it can be seen that regardless of max_filter_price, people are rejected at similar rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "febc036f",
   "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>origin_country</th>\n",
       "      <th>accepted</th>\n",
       "      <th>count_accepted</th>\n",
       "      <th>count_country</th>\n",
       "      <th>acceptance_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>IN</td>\n",
       "      <td>True</td>\n",
       "      <td>138</td>\n",
       "      <td>874</td>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>HR</td>\n",
       "      <td>True</td>\n",
       "      <td>159</td>\n",
       "      <td>530</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>AT</td>\n",
       "      <td>True</td>\n",
       "      <td>83</td>\n",
       "      <td>239</td>\n",
       "      <td>0.347280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>RU</td>\n",
       "      <td>True</td>\n",
       "      <td>83</td>\n",
       "      <td>239</td>\n",
       "      <td>0.347280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IT</td>\n",
       "      <td>True</td>\n",
       "      <td>1183</td>\n",
       "      <td>3137</td>\n",
       "      <td>0.377112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>AE</td>\n",
       "      <td>True</td>\n",
       "      <td>59</td>\n",
       "      <td>154</td>\n",
       "      <td>0.383117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>True</td>\n",
       "      <td>407</td>\n",
       "      <td>993</td>\n",
       "      <td>0.409869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IE</td>\n",
       "      <td>True</td>\n",
       "      <td>1217</td>\n",
       "      <td>2951</td>\n",
       "      <td>0.412403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ES</td>\n",
       "      <td>True</td>\n",
       "      <td>794</td>\n",
       "      <td>1914</td>\n",
       "      <td>0.414838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>RO</td>\n",
       "      <td>True</td>\n",
       "      <td>50</td>\n",
       "      <td>118</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>CR</td>\n",
       "      <td>True</td>\n",
       "      <td>82</td>\n",
       "      <td>188</td>\n",
       "      <td>0.436170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GB</td>\n",
       "      <td>True</td>\n",
       "      <td>1610</td>\n",
       "      <td>3667</td>\n",
       "      <td>0.439051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BE</td>\n",
       "      <td>True</td>\n",
       "      <td>134</td>\n",
       "      <td>304</td>\n",
       "      <td>0.440789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>BR</td>\n",
       "      <td>True</td>\n",
       "      <td>215</td>\n",
       "      <td>482</td>\n",
       "      <td>0.446058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AU</td>\n",
       "      <td>True</td>\n",
       "      <td>268</td>\n",
       "      <td>590</td>\n",
       "      <td>0.454237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FR</td>\n",
       "      <td>True</td>\n",
       "      <td>1526</td>\n",
       "      <td>3232</td>\n",
       "      <td>0.472153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CH</td>\n",
       "      <td>True</td>\n",
       "      <td>279</td>\n",
       "      <td>585</td>\n",
       "      <td>0.476923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US</td>\n",
       "      <td>True</td>\n",
       "      <td>2050</td>\n",
       "      <td>4298</td>\n",
       "      <td>0.476966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DE</td>\n",
       "      <td>True</td>\n",
       "      <td>745</td>\n",
       "      <td>1535</td>\n",
       "      <td>0.485342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NL</td>\n",
       "      <td>True</td>\n",
       "      <td>212</td>\n",
       "      <td>433</td>\n",
       "      <td>0.489607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>SG</td>\n",
       "      <td>True</td>\n",
       "      <td>115</td>\n",
       "      <td>232</td>\n",
       "      <td>0.495690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PT</td>\n",
       "      <td>True</td>\n",
       "      <td>101</td>\n",
       "      <td>203</td>\n",
       "      <td>0.497537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DK</td>\n",
       "      <td>True</td>\n",
       "      <td>86</td>\n",
       "      <td>125</td>\n",
       "      <td>0.688000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    origin_country accepted  count_accepted  count_country  acceptance_rate\n",
       "73              IN     True             138            874         0.157895\n",
       "55              HR     True             159            530         0.300000\n",
       "72              AT     True              83            239         0.347280\n",
       "54              RU     True              83            239         0.347280\n",
       "11              IT     True            1183           3137         0.377112\n",
       "100             AE     True              59            154         0.383117\n",
       "0               CA     True             407            993         0.409869\n",
       "13              IE     True            1217           2951         0.412403\n",
       "24              ES     True             794           1914         0.414838\n",
       "49              RO     True              50            118         0.423729\n",
       "78              CR     True              82            188         0.436170\n",
       "6               GB     True            1610           3667         0.439051\n",
       "25              BE     True             134            304         0.440789\n",
       "38              BR     True             215            482         0.446058\n",
       "27              AU     True             268            590         0.454237\n",
       "17              FR     True            1526           3232         0.472153\n",
       "12              CH     True             279            585         0.476923\n",
       "7               US     True            2050           4298         0.476966\n",
       "14              DE     True             745           1535         0.485342\n",
       "31              NL     True             212            433         0.489607\n",
       "46              SG     True             115            232         0.495690\n",
       "65              PT     True             101            203         0.497537\n",
       "1               DK     True              86            125         0.688000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the acceptance rate by country\n",
    "\n",
    "dataset_country = merged_datasets[['origin_country', 'accepted']]\n",
    "\n",
    "#Find acceptance count by country and accepted\n",
    "accepted_count = dataset_country.groupby(['origin_country', 'accepted']).agg({'origin_country':'count'})\n",
    "accepted_count.columns = ['count_accepted']\n",
    "\n",
    "#Find acceptance count by country\n",
    "country_count = dataset_country.groupby(['origin_country']).agg({'origin_country':'count'})\n",
    "country_count.columns = ['count_country']\n",
    "\n",
    "#Merge datasets for easier manipulation \n",
    "acceptance_country = pd.merge(dataset_country, accepted_count,  how='left', on=['origin_country','accepted']) #Merge accepted count\n",
    "acceptance_country = acceptance_country.drop_duplicates()\n",
    "\n",
    "acceptance_country = pd.merge(acceptance_country, country_count, how='left', on=['origin_country']) #Merge total country count\n",
    "acceptance_country = acceptance_country.sort_values(['count_country', 'accepted'], ascending = [False, True])\n",
    "acceptance_country = acceptance_country[acceptance_country['count_country'] >= 100] #100 is used so there is a good amount of data to make assumptions\n",
    "acceptance_country = acceptance_country[acceptance_country['accepted'] == True]\n",
    "\n",
    "#Divide count_accepted column by count_country column to find acceptance rate by country\n",
    "acceptance_country['acceptance_rate'] = acceptance_country['count_accepted']/acceptance_country['count_country']\n",
    "acceptance_country.sort_values(['acceptance_rate'], ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9601e2f2",
   "metadata": {},
   "source": [
    "An interesting point is that India only has the lowest acceptance rate of 15%, which is half of the acceptance rate compared to the second lowest accepted country. Needs to be investigated further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "645208f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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