From 17ed860c6498416805659d9887373cf966baecf2 Mon Sep 17 00:00:00 2001
From: Susan Li <susanli2005@hotmail.com>
Date: Mon, 30 Aug 2021 12:59:25 -0400
Subject: [PATCH] Add notebook

---
 Classic LogReg model_update.ipynb | 1184 +++++++++++++++++++++++++++++
 1 file changed, 1184 insertions(+)
 create mode 100644 Classic LogReg model_update.ipynb

diff --git a/Classic LogReg model_update.ipynb b/Classic LogReg model_update.ipynb
new file mode 100644
index 0000000..cb7a202
--- /dev/null
+++ b/Classic LogReg model_update.ipynb	
@@ -0,0 +1,1184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, I used top 100 ranked hotels, filtered out avg nightly rate outliers, and used hotel names, fitted a classic LogReg model then predict_prob."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.read_csv('2878240_2020_02_13.csv')\n",
+    "df2 = pd.read_csv('2878258_2020_02_13.csv')\n",
+    "\n",
+    "df1['checkin_date'] = pd.to_datetime(df1['checkin_date'])\n",
+    "df1['checkout_date'] = pd.to_datetime(df1['checkout_date'])\n",
+    "df2['checkin_date'] = pd.to_datetime(df2['checkin_date'])\n",
+    "df2['checkout_date'] = pd.to_datetime(df2['checkout_date'])\n",
+    "df1.sort_values(by=['checkin_date'], inplace=True)\n",
+    "df2.sort_values(by=['checkin_date'], inplace=True)\n",
+    "frames = [df1, df2]\n",
+    "df = pd.concat(frames)\n",
+    "\n",
+    "df = df.drop_duplicates(keep='first')\n",
+    "df['length_of_stay'] = (df['checkout_date'] - df['checkin_date']).dt.days\n",
+    "df['avg_nightly_rate'] = df['total_price'] / df['num_of_rooms'] / df['length_of_stay']\n",
+    "\n",
+    "df = df.loc[df['rank'] <= 100]\n",
+    "df1 = df[['searchid', 'purchased', 'rank', 'avg_nightly_rate', 'hotel_name']]\n",
+    "P = np.percentile(df1['avg_nightly_rate'], [5, 95])\n",
+    "df1 = df1[(df1['avg_nightly_rate'] > P[0]) & (df1['avg_nightly_rate'] < P[1])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "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>searchid</th>\n",
+       "      <th>purchased</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>avg_nightly_rate</th>\n",
+       "      <th>hotel_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>24425</td>\n",
+       "      <td>7AB1B34B-3901-47FE-B4DF-0A5F3FC16164</td>\n",
+       "      <td>False</td>\n",
+       "      <td>49</td>\n",
+       "      <td>247.0</td>\n",
+       "      <td>Grand Hotel&amp; Suites Toronto</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24424</td>\n",
+       "      <td>7AB1B34B-3901-47FE-B4DF-0A5F3FC16164</td>\n",
+       "      <td>False</td>\n",
+       "      <td>48</td>\n",
+       "      <td>239.0</td>\n",
+       "      <td>Toronto Marriott City Centre Hotel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24423</td>\n",
+       "      <td>7AB1B34B-3901-47FE-B4DF-0A5F3FC16164</td>\n",
+       "      <td>False</td>\n",
+       "      <td>47</td>\n",
+       "      <td>216.0</td>\n",
+       "      <td>The York Boutique Suites</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24422</td>\n",
+       "      <td>7AB1B34B-3901-47FE-B4DF-0A5F3FC16164</td>\n",
+       "      <td>False</td>\n",
+       "      <td>46</td>\n",
+       "      <td>204.0</td>\n",
+       "      <td>Aaira Suites</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24421</td>\n",
+       "      <td>7AB1B34B-3901-47FE-B4DF-0A5F3FC16164</td>\n",
+       "      <td>False</td>\n",
+       "      <td>45</td>\n",
+       "      <td>189.0</td>\n",
+       "      <td>Simply Comfort. Gorgeous Apartments in the Hea...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   searchid  purchased  rank  \\\n",
+       "24425  7AB1B34B-3901-47FE-B4DF-0A5F3FC16164      False    49   \n",
+       "24424  7AB1B34B-3901-47FE-B4DF-0A5F3FC16164      False    48   \n",
+       "24423  7AB1B34B-3901-47FE-B4DF-0A5F3FC16164      False    47   \n",
+       "24422  7AB1B34B-3901-47FE-B4DF-0A5F3FC16164      False    46   \n",
+       "24421  7AB1B34B-3901-47FE-B4DF-0A5F3FC16164      False    45   \n",
+       "\n",
+       "       avg_nightly_rate                                         hotel_name  \n",
+       "24425             247.0                        Grand Hotel& Suites Toronto  \n",
+       "24424             239.0                 Toronto Marriott City Centre Hotel  \n",
+       "24423             216.0                           The York Boutique Suites  \n",
+       "24422             204.0                                       Aaira Suites  \n",
+       "24421             189.0  Simply Comfort. Gorgeous Apartments in the Hea...  "
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    0.986439\n",
+       "True     0.013561\n",
+       "Name: purchased, dtype: float64"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1['purchased'].value_counts() / df1.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Avg nightly rate seems to have a little bit negative effect on purchase, rank does not seem to have effect on purchase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "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>rank</th>\n",
+       "      <th>avg_nightly_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>purchased</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>False</td>\n",
+       "      <td>46.497938</td>\n",
+       "      <td>304.596275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>True</td>\n",
+       "      <td>47.812500</td>\n",
+       "      <td>282.140774</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                rank  avg_nightly_rate\n",
+       "purchased                             \n",
+       "False      46.497938        304.596275\n",
+       "True       47.812500        282.140774"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby('purchased').mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the hotels that have the highest avg purchase rate, none of them were positioned within top 20, and their prices were somewhere in between around 200 dollars and 500 dollars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "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>purchased</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>avg_nightly_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hotel_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>Luxury Penthouse In Downtown Toronto</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>70.250000</td>\n",
+       "      <td>548.607143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>iResidence in Toronto- Vacation Home</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>85.500000</td>\n",
+       "      <td>390.314815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Elegant condo with stunning lake &amp; views</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>51.777778</td>\n",
+       "      <td>263.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Livingsuites Toronto - Front &amp; John</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>27.555556</td>\n",
+       "      <td>199.388889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>QuickStay - Style in Yorkville (Yonge &amp; Bloor)</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>87.357143</td>\n",
+       "      <td>202.982143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Stunning Suites - Beautiful 2bdr Condo</td>\n",
+       "      <td>0.064516</td>\n",
+       "      <td>57.451613</td>\n",
+       "      <td>347.912442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Galaxy Suites - Iceboat Toronto</td>\n",
+       "      <td>0.062500</td>\n",
+       "      <td>68.031250</td>\n",
+       "      <td>204.269271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Rooms in Downtown Heritage House</td>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>71.764706</td>\n",
+       "      <td>177.558824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Sky Suites Yorkville</td>\n",
+       "      <td>0.055556</td>\n",
+       "      <td>78.277778</td>\n",
+       "      <td>425.287478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>HomeHop - Ice</td>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>426.658897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Livingsuites Toronto - 20 Blue Jays Way</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>62.700000</td>\n",
+       "      <td>234.050000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Galaxy Suites - City View Toronto</td>\n",
+       "      <td>0.047619</td>\n",
+       "      <td>66.952381</td>\n",
+       "      <td>192.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>QuickStay - Gorgeous 2-Bedroom in the Heart of Downtown</td>\n",
+       "      <td>0.045455</td>\n",
+       "      <td>31.318182</td>\n",
+       "      <td>280.757576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1 Bedroom Flat in Spadina/fort York</td>\n",
+       "      <td>0.040000</td>\n",
+       "      <td>81.760000</td>\n",
+       "      <td>183.139333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>elegant condo with panoramic views</td>\n",
+       "      <td>0.038462</td>\n",
+       "      <td>79.192308</td>\n",
+       "      <td>304.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>QuickStay - Sunlit Luxury Loft on King West</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>81.777778</td>\n",
+       "      <td>372.339506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Queen St West Designer Executive Suites</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>74.928571</td>\n",
+       "      <td>334.226190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>West House by Elevate Rooms</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>63.928571</td>\n",
+       "      <td>231.075397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Noel Suites - Air Canada Centre</td>\n",
+       "      <td>0.034483</td>\n",
+       "      <td>29.827586</td>\n",
+       "      <td>247.321648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Diamond Vacation Homes - York Street</td>\n",
+       "      <td>0.034483</td>\n",
+       "      <td>33.862069</td>\n",
+       "      <td>268.574713</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    purchased       rank  \\\n",
+       "hotel_name                                                                 \n",
+       "Luxury Penthouse In Downtown Toronto                 0.250000  70.250000   \n",
+       "iResidence in Toronto- Vacation Home                 0.166667  85.500000   \n",
+       "Elegant condo with stunning lake & views             0.111111  51.777778   \n",
+       "Livingsuites Toronto - Front & John                  0.111111  27.555556   \n",
+       "QuickStay - Style in Yorkville (Yonge & Bloor)       0.071429  87.357143   \n",
+       "Stunning Suites - Beautiful 2bdr Condo               0.064516  57.451613   \n",
+       "Galaxy Suites - Iceboat Toronto                      0.062500  68.031250   \n",
+       "Rooms in Downtown Heritage House                     0.058824  71.764706   \n",
+       "Sky Suites Yorkville                                 0.055556  78.277778   \n",
+       "HomeHop - Ice                                        0.052632  68.000000   \n",
+       "Livingsuites Toronto - 20 Blue Jays Way              0.050000  62.700000   \n",
+       "Galaxy Suites - City View Toronto                    0.047619  66.952381   \n",
+       "QuickStay - Gorgeous 2-Bedroom in the Heart of ...   0.045455  31.318182   \n",
+       "1 Bedroom Flat in Spadina/fort York                  0.040000  81.760000   \n",
+       "elegant condo with panoramic views                   0.038462  79.192308   \n",
+       "QuickStay - Sunlit Luxury Loft on King West          0.037037  81.777778   \n",
+       "Queen St West Designer Executive Suites              0.035714  74.928571   \n",
+       "West House by Elevate Rooms                          0.035714  63.928571   \n",
+       "Noel Suites - Air Canada Centre                      0.034483  29.827586   \n",
+       "Diamond Vacation Homes - York Street                 0.034483  33.862069   \n",
+       "\n",
+       "                                                    avg_nightly_rate  \n",
+       "hotel_name                                                            \n",
+       "Luxury Penthouse In Downtown Toronto                      548.607143  \n",
+       "iResidence in Toronto- Vacation Home                      390.314815  \n",
+       "Elegant condo with stunning lake & views                  263.000000  \n",
+       "Livingsuites Toronto - Front & John                       199.388889  \n",
+       "QuickStay - Style in Yorkville (Yonge & Bloor)            202.982143  \n",
+       "Stunning Suites - Beautiful 2bdr Condo                    347.912442  \n",
+       "Galaxy Suites - Iceboat Toronto                           204.269271  \n",
+       "Rooms in Downtown Heritage House                          177.558824  \n",
+       "Sky Suites Yorkville                                      425.287478  \n",
+       "HomeHop - Ice                                             426.658897  \n",
+       "Livingsuites Toronto - 20 Blue Jays Way                   234.050000  \n",
+       "Galaxy Suites - City View Toronto                         192.750000  \n",
+       "QuickStay - Gorgeous 2-Bedroom in the Heart of ...        280.757576  \n",
+       "1 Bedroom Flat in Spadina/fort York                       183.139333  \n",
+       "elegant condo with panoramic views                        304.666667  \n",
+       "QuickStay - Sunlit Luxury Loft on King West               372.339506  \n",
+       "Queen St West Designer Executive Suites                   334.226190  \n",
+       "West House by Elevate Rooms                               231.075397  \n",
+       "Noel Suites - Air Canada Centre                           247.321648  \n",
+       "Diamond Vacation Homes - York Street                      268.574713  "
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby('hotel_name').mean().sort_values('purchased', ascending=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to the above, the number 1 purchased hotel is \"Luxury Penthouse In Downtown Toronto\". The reason is that this hotel was booked once after only 4 searches. We run into the same problems that described in Wayfair paper, that is, if we just simply compare purchase rate between two properties, we will run into assumption that “Luxury Penthouse In Downtown Toronto” is the highest purchased hotel, while in fact, \"Luxury Penthouse In Downtown Toronto\" just got lucky."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    3\n",
+       "True     1\n",
+       "Name: purchased, dtype: int64"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc[df1['hotel_name'] == 'Luxury Penthouse In Downtown Toronto']['purchased'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    42\n",
+       "True      2\n",
+       "Name: purchased, dtype: int64"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc[df1['hotel_name'] == 'QuickStay - Gorgeous 2-Bedroom in the Heart of Downtown']['purchased'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An illustration of the power of position effects for the above 20 hotels - no effect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(x=\"rank\", y=\"purchased\", data=df1.groupby('hotel_name').mean().sort_values('purchased', ascending=False).head(20))\n",
+    "plt.xlabel('position on page')\n",
+    "plt.ylabel('average purchase rate');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I was expecting something like \"purchase rate goes down when position goes down\". Note, the position number higher means goes down on the page. In another word, if this hotel is positioned down on the page, it will likely to sell less. This is not the case here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new = df1.groupby('hotel_name').mean().sort_values('purchased', ascending=False)\n",
+    "df_new.reset_index(inplace=True)\n",
+    "df_new.loc[df_new['hotel_name'].str.contains('Hotel'), 'category'] = 'hotel'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new.to_csv('df_new.csv', encoding='utf-8', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new = pd.read_csv('df_new.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fe6b3b3ad30>"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df_new.loc[df_new['category'] == 'apartment']['purchased'], hist=False, label='apartment')\n",
+    "sns.distplot(df_new.loc[df_new['category'] == 'hotel']['purchased'], hist=False, label='hotel')\n",
+    "plt.legend()\n",
+    "#plt.xlim(0,0.10);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "apt = df_new.loc[df_new['category'] == 'apartment', 'purchased']\n",
+    "hotel = df_new.loc[df_new['category'] == 'hotel', 'purchased']\n",
+    "fig, ax = plt.subplots(figsize=(10,5))\n",
+    "ax.hist(apt, color='g', alpha=1.0, bins=100, range = [0, 0.20],\n",
+    "       label='apartment')\n",
+    "ax.hist(hotel, color='y', alpha=0.7, bins=100, range = [0, 0.20],\n",
+    "       label='hotel')\n",
+    "plt.xlabel('purchase rate', fontsize=12)\n",
+    "plt.ylabel('frequency', fontsize=12)\n",
+    "plt.title('Purchase rate distribution by accommodation type')\n",
+    "plt.tick_params(labelsize=12)\n",
+    "plt.xlim(0, 0.050)\n",
+    "plt.legend()\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The average purchase rate for apartment is 0.013 (1.3%), the average purchase rate for hotel is 0.005 (0.5%). This means an apartment suite is more than twice likely to be purchased, vs. a hotel room, in Toronto."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    269.000000\n",
+       "mean       0.013220\n",
+       "std        0.023584\n",
+       "min        0.000000\n",
+       "25%        0.000000\n",
+       "50%        0.009302\n",
+       "75%        0.018519\n",
+       "max        0.250000\n",
+       "Name: purchased, dtype: float64"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.loc[df_new['category'] == 'apartment']['purchased'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    172.000000\n",
+       "mean       0.005266\n",
+       "std        0.007014\n",
+       "min        0.000000\n",
+       "25%        0.000000\n",
+       "50%        0.000000\n",
+       "75%        0.011809\n",
+       "max        0.024590\n",
+       "Name: purchased, dtype: float64"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.loc[df_new['category'] == 'hotel']['purchased'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(x=\"avg_nightly_rate\", y=\"purchased\", data=df1.groupby('hotel_name').mean().sort_values('avg_nightly_rate', ascending=False).head(20))\n",
+    "plt.xlabel('avg nightly rate')\n",
+    "plt.ylabel('avg purchase rate');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df1['avg_nightly_rate'], rug=True, hist=False)\n",
+    "plt.title('Distribution of avg nightly rate');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df1['rank'], rug=True, hist=False)\n",
+    "plt.title('Distribution of position');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit a classic logistic regression model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.get_dummies(df1, columns=['hotel_name'])\n",
+    "X = df1.drop('purchased', axis=1)\n",
+    "y = df1.purchased\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3, random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_result = X_test.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.drop('searchid', axis=1, inplace=True)\n",
+    "X_test.drop('searchid', axis=1, inplace=True)\n",
+    "\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)\n",
+    "logReg = LogisticRegression().fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Probability estimates, for the True class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_result['probability'] = logReg.predict_proba(X_test)[:,1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Re-construct dummies back to hotel name variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df_result.ix[:, 3:444].stack()\n",
+    "x = pd.Series(pd.Categorical(x[x!=0].index.get_level_values(1)))\n",
+    "x = pd.DataFrame({'hotel_name':x.values})\n",
+    "x['hotel_name'] = x['hotel_name'].map(lambda x: x.lstrip('hotel_name_'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_result = df_result[['searchid', 'rank', 'avg_nightly_rate', 'probability']].reset_index()\n",
+    "df_result.drop('index', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sort probabilities for the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "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>searchid</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>avg_nightly_rate</th>\n",
+       "      <th>probability</th>\n",
+       "      <th>hotel_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>9521</td>\n",
+       "      <td>3BCF0DC0-A00B-4A29-B962-D7A6CD955396</td>\n",
+       "      <td>25</td>\n",
+       "      <td>179.500000</td>\n",
+       "      <td>0.057441</td>\n",
+       "      <td>Livingsuites Toronto - Front &amp; John</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2260</td>\n",
+       "      <td>623199FE-640A-43D5-8A14-240717026444</td>\n",
+       "      <td>25</td>\n",
+       "      <td>179.500000</td>\n",
+       "      <td>0.057441</td>\n",
+       "      <td>Livingsuites Toronto - Front &amp; John</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11853</td>\n",
+       "      <td>7AE84D0F-B3E8-4065-9978-2883B1EA61D2</td>\n",
+       "      <td>25</td>\n",
+       "      <td>192.000000</td>\n",
+       "      <td>0.055300</td>\n",
+       "      <td>Livingsuites Toronto - Front &amp; John</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2982</td>\n",
+       "      <td>1B9149FC-53AF-47DC-997F-A13A9A8A6326</td>\n",
+       "      <td>22</td>\n",
+       "      <td>209.333333</td>\n",
+       "      <td>0.052189</td>\n",
+       "      <td>Livingsuites Toronto - Front &amp; John</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10310</td>\n",
+       "      <td>7BEC0985-CBA9-447C-AA6B-288D6965B351</td>\n",
+       "      <td>27</td>\n",
+       "      <td>163.250000</td>\n",
+       "      <td>0.045482</td>\n",
+       "      <td>QuickStay - Gorgeous 2-Bedroom in the Heart of...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7703</td>\n",
+       "      <td>DEBC69E0-DC75-4340-9058-9565FD7731E5</td>\n",
+       "      <td>39</td>\n",
+       "      <td>208.666667</td>\n",
+       "      <td>0.044252</td>\n",
+       "      <td>Sarkar Suites - Blue Jays Way</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4552</td>\n",
+       "      <td>1E908602-1831-4CF0-B3FA-9F782628957C</td>\n",
+       "      <td>29</td>\n",
+       "      <td>149.000000</td>\n",
+       "      <td>0.043888</td>\n",
+       "      <td>Sarkar Suites - Maple Leaf Square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>937</td>\n",
+       "      <td>201102BC-849B-4A4B-8D8F-02A603B08EDA</td>\n",
+       "      <td>47</td>\n",
+       "      <td>227.000000</td>\n",
+       "      <td>0.042400</td>\n",
+       "      <td>Sarkar Suites - Blue Jays Way</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2338</td>\n",
+       "      <td>509C50E0-A72B-45FA-9997-CEA757D83996</td>\n",
+       "      <td>57</td>\n",
+       "      <td>241.500000</td>\n",
+       "      <td>0.042154</td>\n",
+       "      <td>CN Tower View Design apart 3BD-2BA DT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>384</td>\n",
+       "      <td>2A39CF6E-8B8A-46FD-8BBA-D135E5B2F03B</td>\n",
+       "      <td>23</td>\n",
+       "      <td>160.666667</td>\n",
+       "      <td>0.041904</td>\n",
+       "      <td>Sarkar Suites - Maple Leaf Square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>544</td>\n",
+       "      <td>50DDDA38-2FF8-44C9-82AC-74AB0FFA2DC3</td>\n",
+       "      <td>91</td>\n",
+       "      <td>153.666667</td>\n",
+       "      <td>0.041883</td>\n",
+       "      <td>Simply Comfort. Modern Downtown Apartment</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11065</td>\n",
+       "      <td>A6ADC02C-ED7F-4A3C-A724-36A64BE21306</td>\n",
+       "      <td>39</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>0.041692</td>\n",
+       "      <td>Sarkar Suites - Blue Jays Way</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5080</td>\n",
+       "      <td>1E908602-1831-4CF0-B3FA-9F782628957C</td>\n",
+       "      <td>99</td>\n",
+       "      <td>139.333333</td>\n",
+       "      <td>0.041210</td>\n",
+       "      <td>QuickStay - Beautiful Toronto Condo (City Views)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3575</td>\n",
+       "      <td>C690E16A-6108-4B16-9CFE-E2A1A1FCEE7E</td>\n",
+       "      <td>27</td>\n",
+       "      <td>175.000000</td>\n",
+       "      <td>0.040367</td>\n",
+       "      <td>Sarkar Suites - Maple Leaf Square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2178</td>\n",
+       "      <td>B0710740-B143-4143-A929-558D76F2F742</td>\n",
+       "      <td>49</td>\n",
+       "      <td>246.666667</td>\n",
+       "      <td>0.040040</td>\n",
+       "      <td>Sarkar Suites - Blue Jays Way</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5396</td>\n",
+       "      <td>578FF0C7-0FF2-4C5D-8765-129E4F286E07</td>\n",
+       "      <td>34</td>\n",
+       "      <td>213.666667</td>\n",
+       "      <td>0.039405</td>\n",
+       "      <td>QuickStay - Gorgeous 2-Bedroom in the Heart of...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6058</td>\n",
+       "      <td>EAF56532-5031-4F45-AB53-0B5C21963E89</td>\n",
+       "      <td>78</td>\n",
+       "      <td>268.200000</td>\n",
+       "      <td>0.039380</td>\n",
+       "      <td>Sarkar Suites - Blue Jays Way</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9077</td>\n",
+       "      <td>4CFB9FB5-8EA9-4228-B190-C01E24F95A3A</td>\n",
+       "      <td>28</td>\n",
+       "      <td>184.000000</td>\n",
+       "      <td>0.039327</td>\n",
+       "      <td>Sarkar Suites - Maple Leaf Square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2445</td>\n",
+       "      <td>5C16BC70-1E3E-471D-85AA-224E81C1121A</td>\n",
+       "      <td>26</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>0.039313</td>\n",
+       "      <td>Sarkar Suites - Maple Leaf Square</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9332</td>\n",
+       "      <td>F4B50DBB-A59F-4D19-AD95-4C12B78810C8</td>\n",
+       "      <td>100</td>\n",
+       "      <td>196.000000</td>\n",
+       "      <td>0.038696</td>\n",
+       "      <td>QuickStay - Style in Yorkville (Yonge &amp; Bloor)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   searchid  rank  avg_nightly_rate  \\\n",
+       "9521   3BCF0DC0-A00B-4A29-B962-D7A6CD955396    25        179.500000   \n",
+       "2260   623199FE-640A-43D5-8A14-240717026444    25        179.500000   \n",
+       "11853  7AE84D0F-B3E8-4065-9978-2883B1EA61D2    25        192.000000   \n",
+       "2982   1B9149FC-53AF-47DC-997F-A13A9A8A6326    22        209.333333   \n",
+       "10310  7BEC0985-CBA9-447C-AA6B-288D6965B351    27        163.250000   \n",
+       "7703   DEBC69E0-DC75-4340-9058-9565FD7731E5    39        208.666667   \n",
+       "4552   1E908602-1831-4CF0-B3FA-9F782628957C    29        149.000000   \n",
+       "937    201102BC-849B-4A4B-8D8F-02A603B08EDA    47        227.000000   \n",
+       "2338   509C50E0-A72B-45FA-9997-CEA757D83996    57        241.500000   \n",
+       "384    2A39CF6E-8B8A-46FD-8BBA-D135E5B2F03B    23        160.666667   \n",
+       "544    50DDDA38-2FF8-44C9-82AC-74AB0FFA2DC3    91        153.666667   \n",
+       "11065  A6ADC02C-ED7F-4A3C-A724-36A64BE21306    39        228.000000   \n",
+       "5080   1E908602-1831-4CF0-B3FA-9F782628957C    99        139.333333   \n",
+       "3575   C690E16A-6108-4B16-9CFE-E2A1A1FCEE7E    27        175.000000   \n",
+       "2178   B0710740-B143-4143-A929-558D76F2F742    49        246.666667   \n",
+       "5396   578FF0C7-0FF2-4C5D-8765-129E4F286E07    34        213.666667   \n",
+       "6058   EAF56532-5031-4F45-AB53-0B5C21963E89    78        268.200000   \n",
+       "9077   4CFB9FB5-8EA9-4228-B190-C01E24F95A3A    28        184.000000   \n",
+       "2445   5C16BC70-1E3E-471D-85AA-224E81C1121A    26        183.000000   \n",
+       "9332   F4B50DBB-A59F-4D19-AD95-4C12B78810C8   100        196.000000   \n",
+       "\n",
+       "       probability                                         hotel_name  \n",
+       "9521      0.057441                Livingsuites Toronto - Front & John  \n",
+       "2260      0.057441                Livingsuites Toronto - Front & John  \n",
+       "11853     0.055300                Livingsuites Toronto - Front & John  \n",
+       "2982      0.052189                Livingsuites Toronto - Front & John  \n",
+       "10310     0.045482  QuickStay - Gorgeous 2-Bedroom in the Heart of...  \n",
+       "7703      0.044252                      Sarkar Suites - Blue Jays Way  \n",
+       "4552      0.043888                  Sarkar Suites - Maple Leaf Square  \n",
+       "937       0.042400                      Sarkar Suites - Blue Jays Way  \n",
+       "2338      0.042154              CN Tower View Design apart 3BD-2BA DT  \n",
+       "384       0.041904                  Sarkar Suites - Maple Leaf Square  \n",
+       "544       0.041883          Simply Comfort. Modern Downtown Apartment  \n",
+       "11065     0.041692                      Sarkar Suites - Blue Jays Way  \n",
+       "5080      0.041210   QuickStay - Beautiful Toronto Condo (City Views)  \n",
+       "3575      0.040367                  Sarkar Suites - Maple Leaf Square  \n",
+       "2178      0.040040                      Sarkar Suites - Blue Jays Way  \n",
+       "5396      0.039405  QuickStay - Gorgeous 2-Bedroom in the Heart of...  \n",
+       "6058      0.039380                      Sarkar Suites - Blue Jays Way  \n",
+       "9077      0.039327                  Sarkar Suites - Maple Leaf Square  \n",
+       "2445      0.039313                  Sarkar Suites - Maple Leaf Square  \n",
+       "9332      0.038696     QuickStay - Style in Yorkville (Yonge & Bloor)  "
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_result.merge(x, left_index=True, right_index=True).sort_values('probability', ascending=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems that PC users come to PC website, when come to book hotels, they know what they want and it does not take them long to make decision, and they make relative quick decisions on hotel bookings, and most likely these bookings are toward condo or appartment suits. \n",
+    "\n",
+    "What does it mean when we say \"a very good hotel\"? It depends. For many of us, Hilton Toronto is a very good hotel and I would be happy to see it on the top position of the page. But if most of PC customers are interested in apartment suites, they don't need to be persuaded and they know (more or less) on what they want. So, why not position apartment suites on the top positions of the page?"
+   ]
+  },
+  {
+   "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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}