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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "4ba6aba8"
},
"source": [
"# 🤖 **Data Collection, Creation, Storage, and Processing**\n"
]
},
{
"cell_type": "markdown",
"source": [
"### N.B. on dataset naming\n",
"\n",
"This notebook is adapted from the original workshop template, which was based on book data. \n",
"For consistency with the second notebook and to avoid breaking the pipeline, some variable and file names (such as \"df_books\" or \"synthetic_book_reviews.csv\") were kept unchanged.\n",
"\n",
"However, all the data used in this project relates to hotels. \n",
"For example, \"title\" refers to the hotel name, \"price\" corresponds to a proxy of the hotel ADR, and \"units_sold\" represents booking demand.\n",
"\n",
"This approach allows us to reuse the structure of the original notebooks while applying it to a different business problem."
],
"metadata": {
"id": "N_ZPZM4Ugbr2"
}
},
{
"cell_type": "markdown",
"source": [
"We use two datasets for this project.\n",
"The first one is a hotel booking dataset with information like price (ADR), cancellations and booking details.\n",
"The second one is a hotel review dataset with text reviews and ratings.\n",
"\n",
"The first dataset is quantitative data and the second one is qualitative data."
],
"metadata": {
"id": "WzucGkQ4grQm"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "jpASMyIQMaAq"
},
"source": [
"## **1.** 📦 Install required packages"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f48c8f8c",
"outputId": "d267db0b-b091-418f-a5c3-5ada4358f32e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.12/dist-packages (4.13.5)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
"Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
"Requirement already satisfied: textblob in /usr/local/lib/python3.12/dist-packages (0.19.0)\n",
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (2.8.3)\n",
"Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (4.15.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2026.1)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n",
"Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
"Requirement already satisfied: nltk>=3.9 in /usr/local/lib/python3.12/dist-packages (from textblob) (3.9.1)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (8.3.2)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (1.5.3)\n",
"Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (2025.11.3)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (4.67.3)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
]
}
],
"source": [
"!pip install beautifulsoup4 pandas matplotlib seaborn numpy textblob"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lquNYCbfL9IM"
},
"source": [
"## **2.** 🏨 Load hotel booking and hotel review datasets"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0IWuNpxxYDJF"
},
"source": [
"### *a. Initial setup*\n",
"Define the base url of the website you will scrape as well as how and what you will scrape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "91d52125",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "84a7ad0f-fbd2-45f0-d4ce-495f24390ee4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" hotel is_canceled lead_time arrival_date_year arrival_date_month \\\n",
"0 Resort Hotel 0 342 2015 July \n",
"1 Resort Hotel 0 737 2015 July \n",
"2 Resort Hotel 0 7 2015 July \n",
"3 Resort Hotel 0 13 2015 July \n",
"4 Resort Hotel 0 14 2015 July \n",
"\n",
" arrival_date_week_number arrival_date_day_of_month \\\n",
"0 27 1 \n",
"1 27 1 \n",
"2 27 1 \n",
"3 27 1 \n",
"4 27 1 \n",
"\n",
" stays_in_weekend_nights stays_in_week_nights adults ... deposit_type \\\n",
"0 0 0 2 ... No Deposit \n",
"1 0 0 2 ... No Deposit \n",
"2 0 1 1 ... No Deposit \n",
"3 0 1 1 ... No Deposit \n",
"4 0 2 2 ... No Deposit \n",
"\n",
" agent company days_in_waiting_list customer_type adr \\\n",
"0 NaN NaN 0 Transient 0.0 \n",
"1 NaN NaN 0 Transient 0.0 \n",
"2 NaN NaN 0 Transient 75.0 \n",
"3 304.0 NaN 0 Transient 75.0 \n",
"4 240.0 NaN 0 Transient 98.0 \n",
"\n",
" required_car_parking_spaces total_of_special_requests reservation_status \\\n",
"0 0 0 Check-Out \n",
"1 0 0 Check-Out \n",
"2 0 0 Check-Out \n",
"3 0 0 Check-Out \n",
"4 0 1 Check-Out \n",
"\n",
" reservation_status_date \n",
"0 2015-07-01 \n",
"1 2015-07-01 \n",
"2 2015-07-02 \n",
"3 2015-07-02 \n",
"4 2015-07-03 \n",
"\n",
"[5 rows x 32 columns]\n",
" name city reviews.date reviews.rating \\\n",
"0 Hotel Russo Palace Mableton 2013-09-22 00:00:00+00:00 4.0 \n",
"1 Hotel Russo Palace Mableton 2015-04-03 00:00:00+00:00 5.0 \n",
"2 Hotel Russo Palace Mableton 2014-05-13 00:00:00+00:00 5.0 \n",
"3 Hotel Russo Palace Mableton 2013-10-27 00:00:00+00:00 5.0 \n",
"4 Hotel Russo Palace Mableton 2015-03-05 00:00:00+00:00 5.0 \n",
"\n",
" reviews.text hotel_type \n",
"0 Pleasant 10 min walk along the sea front to th... Resort Hotel \n",
"1 Really lovely hotel. Stayed on the very top fl... Resort Hotel \n",
"2 Ett mycket bra hotell. Det som drog ner betyge... Resort Hotel \n",
"3 We stayed here for four nights in October. The... Resort Hotel \n",
"4 We stayed here for four nights in October. The... Resort Hotel \n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import time\n",
"\n",
"# Load hotel datasets\n",
"bookings = pd.read_csv(\"hotel_bookings.csv\")\n",
"reviews_raw = pd.read_csv(\"7282_1.csv\")\n",
"\n",
"# Keep useful columns from reviews\n",
"reviews_raw = reviews_raw[[\n",
" \"name\",\n",
" \"city\",\n",
" \"reviews.date\",\n",
" \"reviews.rating\",\n",
" \"reviews.text\"\n",
"]].copy()\n",
"\n",
"reviews_raw = reviews_raw.dropna(subset=[\"name\", \"reviews.text\", \"reviews.rating\"])\n",
"reviews_raw[\"reviews.date\"] = pd.to_datetime(reviews_raw[\"reviews.date\"], errors=\"coerce\")\n",
"reviews_raw = reviews_raw.dropna(subset=[\"reviews.date\"])\n",
"\n",
"# Create hotel type from review dataset\n",
"def classify_hotel_type(name, city):\n",
" text = (str(name) + \" \" + str(city)).lower()\n",
" resort_words = [\"resort\", \"spa\", \"beach\", \"island\", \"sea\", \"pool\", \"palace\"]\n",
" for word in resort_words:\n",
" if word in text:\n",
" return \"Resort Hotel\"\n",
" return \"City Hotel\"\n",
"\n",
"reviews_raw[\"hotel_type\"] = reviews_raw.apply(\n",
" lambda row: classify_hotel_type(row[\"name\"], row[\"city\"]),\n",
" axis=1\n",
")\n",
"\n",
"# Mean ADR by hotel type from booking dataset\n",
"adr_by_type = bookings.groupby(\"hotel\")[\"adr\"].mean().to_dict()\n",
"\n",
"# Create hotel-level dataset using review data\n",
"df_books = (\n",
" reviews_raw.groupby(\"name\", as_index=False)\n",
" .agg(\n",
" rating=(\"reviews.rating\", \"mean\"),\n",
" n_reviews=(\"reviews.text\", \"size\"),\n",
" hotel_type=(\"hotel_type\", lambda x: x.mode().iloc[0] if not x.mode().empty else \"City Hotel\"),\n",
" city=(\"city\", lambda x: x.mode().iloc[0] if not x.mode().empty else \"Unknown\")\n",
" )\n",
")\n",
"\n",
"# Keep \"title\" column name for compatibility with notebook 2\n",
"df_books = df_books.rename(columns={\"name\": \"title\"})\n",
"\n",
"# Create a hotel price proxy based on real ADR\n",
"df_books[\"price\"] = df_books[\"hotel_type\"].map(adr_by_type)\n",
"df_books[\"price\"] = df_books[\"price\"] * np.random.uniform(0.85, 1.15, len(df_books))\n",
"\n",
"print(bookings.head())\n",
"print(reviews_raw.head())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oCdTsin2Yfp3"
},
"source": [
"### *b. Build a hotel-level dataframe with title, price, and rating*"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "xqO5Y3dnYhxt",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3c6eb1f7-9812-4ce0-bd25-d9827a378de5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" title rating n_reviews hotel_type \\\n",
"0 1785 Inn 2.625000 16 City Hotel \n",
"1 1900 House 4.571429 14 City Hotel \n",
"2 40 Berkeley Hostel 3.329193 161 City Hotel \n",
"3 A Bed & Breakfast In Cambridge 3.574074 54 City Hotel \n",
"4 Acorn Motor Inn 3.750000 20 City Hotel \n",
"\n",
" city price \n",
"0 North Conway 118.821735 \n",
"1 Narragansett 117.644891 \n",
"2 Boston 105.233747 \n",
"3 Cambridge 93.780093 \n",
"4 Oak Harbor 100.787105 \n",
"(623, 6)\n"
]
}
],
"source": [
"# The hotel-level dataframe is already created in cell 5\n",
"print(df_books.head())\n",
"print(df_books.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T0TOeRC4Yrnn"
},
"source": [
"### *c. ✋🏻🛑⛔️ Use the hotel-level dataframe as df_books with \"title\", \"price\", and \"rating\"*"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "l5FkkNhUYTHh",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "26d2a8d5-97f1-4918-bb96-8034659868c5"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" title price rating n_reviews \\\n",
"0 1785 Inn 118.821735 2.625000 16 \n",
"1 1900 House 117.644891 4.571429 14 \n",
"2 40 Berkeley Hostel 105.233747 3.329193 161 \n",
"3 A Bed & Breakfast In Cambridge 93.780093 3.574074 54 \n",
"4 Acorn Motor Inn 100.787105 3.750000 20 \n",
"\n",
" hotel_type city \n",
"0 City Hotel North Conway \n",
"1 City Hotel Narragansett \n",
"2 City Hotel Boston \n",
"3 City Hotel Cambridge \n",
"4 City Hotel Oak Harbor "
],
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"\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_books",
"summary": "{\n \"name\": \"df_books\",\n \"rows\": 623,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 623,\n \"samples\": [\n \"Hampton Inn Roanoke/salem\",\n \"Super 8 Metropolis\",\n \"Drury Inn and Suites Columbus Convention Center\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.514494608519156,\n \"min\": 81.95384182332211,\n \"max\": 121.06048279685156,\n \"num_unique_values\": 623,\n \"samples\": [\n 97.03571613889734,\n 96.43920055033284,\n 112.37363684162978\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0470488242452078,\n \"min\": 0.0,\n \"max\": 8.368932038834952,\n \"num_unique_values\": 435,\n \"samples\": [\n 2.25,\n 3.1818181818181817,\n 4.235294117647059\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 80,\n \"min\": 1,\n \"max\": 1185,\n \"num_unique_values\": 157,\n \"samples\": [\n 714,\n 44,\n 156\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hotel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Resort Hotel\",\n \"City Hotel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 494,\n \"samples\": [\n \"Alexandria\",\n \"Detroit Lakes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 24
}
],
"source": [
"# df_books is already ready and contains title, price, and rating\n",
"df_books = df_books[[\"title\", \"price\", \"rating\", \"n_reviews\", \"hotel_type\", \"city\"]].copy()\n",
"df_books.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "duI5dv3CZYvF"
},
"source": [
"### *d. Save web-scraped dataframe either as a CSV or Excel file*"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "lC1U_YHtZifh"
},
"outputs": [],
"source": [
"# Save hotel-level dataframe\n",
"df_books.to_csv(\"hotel_level_features.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qMjRKMBQZlJi"
},
"source": [
"### *e. ✋🏻🛑⛔️ View first fiew lines*"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "O_wIvTxYZqCK",
"outputId": "2f6edaf4-e853-4d9c-c2a1-c17502991c08"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" title price rating n_reviews \\\n",
"0 1785 Inn 118.821735 2.625000 16 \n",
"1 1900 House 117.644891 4.571429 14 \n",
"2 40 Berkeley Hostel 105.233747 3.329193 161 \n",
"3 A Bed & Breakfast In Cambridge 93.780093 3.574074 54 \n",
"4 Acorn Motor Inn 100.787105 3.750000 20 \n",
"\n",
" hotel_type city \n",
"0 City Hotel North Conway \n",
"1 City Hotel Narragansett \n",
"2 City Hotel Boston \n",
"3 City Hotel Cambridge \n",
"4 City Hotel Oak Harbor "
],
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"type": "dataframe",
"variable_name": "df_books",
"summary": "{\n \"name\": \"df_books\",\n \"rows\": 623,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 623,\n \"samples\": [\n \"Hampton Inn Roanoke/salem\",\n \"Super 8 Metropolis\",\n \"Drury Inn and Suites Columbus Convention Center\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.514494608519156,\n \"min\": 81.95384182332211,\n \"max\": 121.06048279685156,\n \"num_unique_values\": 623,\n \"samples\": [\n 97.03571613889734,\n 96.43920055033284,\n 112.37363684162978\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0470488242452078,\n \"min\": 0.0,\n \"max\": 8.368932038834952,\n \"num_unique_values\": 435,\n \"samples\": [\n 2.25,\n 3.1818181818181817,\n 4.235294117647059\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 80,\n \"min\": 1,\n \"max\": 1185,\n \"num_unique_values\": 157,\n \"samples\": [\n 714,\n 44,\n 156\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hotel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Resort Hotel\",\n \"City Hotel\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 494,\n \"samples\": [\n \"Alexandria\",\n \"Detroit Lakes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 26
}
],
"source": [
"df_books.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p-1Pr2szaqLk"
},
"source": [
"## **3.** 🧩 Create a meaningful connection between real & synthetic datasets"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SIaJUGIpaH4V"
},
"source": [
"### *a. Initial setup*"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "-gPXGcRPuV_9"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import random\n",
"from datetime import datetime\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"random.seed(2025)\n",
"np.random.seed(2025)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pY4yCoIuaQqp"
},
"source": [
"### *b. Generate popularity scores based on hotel rating and review volume*"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "mnd5hdAbaNjz"
},
"outputs": [],
"source": [
"def generate_popularity_score(avg_rating, n_reviews):\n",
" base = round(avg_rating)\n",
"\n",
" if n_reviews >= 20:\n",
" volume_bonus = 1\n",
" else:\n",
" volume_bonus = 0\n",
"\n",
" noise = random.choice([-1, 0, 0, 1])\n",
"\n",
" return int(np.clip(base + volume_bonus + noise, 1, 5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n4-TaNTFgPak"
},
"source": [
"### *c. ✋🏻🛑⛔️ Create a \"popularity_score\" column from rating and number of reviews*"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "V-G3OCUCgR07"
},
"outputs": [],
"source": [
"df_books[\"popularity_score\"] = df_books.apply(\n",
" lambda row: generate_popularity_score(row[\"rating\"], row[\"n_reviews\"]),\n",
" axis=1\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HnngRNTgacYt"
},
"source": [
"### *d. Decide on the sentiment_label based on the popularity score with a get_sentiment function*"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "kUtWmr8maZLZ"
},
"outputs": [],
"source": [
"def get_sentiment(popularity_score):\n",
" if popularity_score <= 2:\n",
" return \"negative\"\n",
" elif popularity_score == 3:\n",
" return \"neutral\"\n",
" else:\n",
" return \"positive\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HF9F9HIzgT7Z"
},
"source": [
"### *e. ✋🏻🛑⛔️ Run the function to create a \"sentiment_label\" column from \"popularity_score\"*"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "tafQj8_7gYCG"
},
"outputs": [],
"source": [
"df_books[\"sentiment_label\"] = df_books[\"popularity_score\"].apply(get_sentiment)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T8AdKkmASq9a"
},
"source": [
"## **4.** 📈 Generate synthetic hotel demand data for 18 months"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OhXbdGD5fH0c"
},
"source": [
"### *a. Create a generate_sales_profile function based on hotel type and popularity*"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "qkVhYPXGbgEn"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"# Build a real monthly baseline from booking data\n",
"bookings[\"month_date\"] = pd.to_datetime(\n",
" bookings[\"arrival_date_month\"] + \" \" + bookings[\"arrival_date_year\"].astype(str),\n",
" format=\"%B %Y\",\n",
" errors=\"coerce\"\n",
")\n",
"\n",
"bookings = bookings.dropna(subset=[\"month_date\"])\n",
"bookings[\"month_num\"] = bookings[\"month_date\"].dt.month\n",
"\n",
"monthly_baseline = (\n",
" bookings.groupby([\"hotel\", \"month_num\"])\n",
" .size()\n",
" .reset_index(name=\"base_demand\")\n",
")\n",
"\n",
"def generate_sales_profile(hotel_type, popularity_score):\n",
" months = pd.date_range(end=datetime.today(), periods=18, freq=\"M\")\n",
" hotel_baseline = monthly_baseline[monthly_baseline[\"hotel\"] == hotel_type]\n",
"\n",
" if hotel_baseline.empty:\n",
" base_mean = 100\n",
" else:\n",
" base_mean = hotel_baseline[\"base_demand\"].mean()\n",
"\n",
" multiplier_map = {\n",
" 1: 0.6,\n",
" 2: 0.8,\n",
" 3: 1.0,\n",
" 4: 1.2,\n",
" 5: 1.4\n",
" }\n",
"\n",
" popularity_multiplier = multiplier_map.get(popularity_score, 1.0)\n",
"\n",
" records = []\n",
" for month in months:\n",
" month_num = month.month\n",
"\n",
" month_row = hotel_baseline[hotel_baseline[\"month_num\"] == month_num]\n",
" if not month_row.empty:\n",
" seasonal_base = month_row[\"base_demand\"].values[0]\n",
" else:\n",
" seasonal_base = base_mean\n",
"\n",
" units = max(\n",
" 5,\n",
" int(np.random.normal((seasonal_base / 40) * popularity_multiplier, 5))\n",
" )\n",
"\n",
" records.append((month.strftime(\"%Y-%m\"), units))\n",
"\n",
" return records"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L2ak1HlcgoTe"
},
"source": [
"### *b. Build sales_data using hotel_type and popularity_score*"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "SlJ24AUafoDB"
},
"outputs": [],
"source": [
"sales_data = []\n",
"\n",
"for _, row in df_books.iterrows():\n",
" records = generate_sales_profile(row[\"hotel_type\"], row[\"popularity_score\"])\n",
" for month, units in records:\n",
" sales_data.append({\n",
" \"title\": row[\"title\"],\n",
" \"month\": month,\n",
" \"units_sold\": units,\n",
" \"sentiment_label\": row[\"sentiment_label\"]\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4IXZKcCSgxnq"
},
"source": [
"### *c. ✋🏻🛑⛔️ Create a df_sales DataFrame from sales_data*"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "wcN6gtiZg-ws"
},
"outputs": [],
"source": [
"df_sales = pd.DataFrame(sales_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EhIjz9WohAmZ"
},
"source": [
"### *d. Save df_sales as synthetic_sales_data.csv & view first few lines*"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MzbZvLcAhGaH",
"outputId": "7975a59e-178e-4d25-98f5-02d90cbd97b0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" title month units_sold sentiment_label\n",
"0 1785 Inn 2024-10 151 negative\n",
"1 1785 Inn 2024-11 90 negative\n",
"2 1785 Inn 2024-12 75 negative\n",
"3 1785 Inn 2025-01 71 negative\n",
"4 1785 Inn 2025-02 98 negative\n"
]
}
],
"source": [
"df_sales.to_csv(\"synthetic_sales_data.csv\", index=False)\n",
"\n",
"print(df_sales.head())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7g9gqBgQMtJn"
},
"source": [
"## **5.** 🎯 Generate synthetic customer review dataset using hotel reviews"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gi4y9M9KuDWx"
},
"source": [
"### *a. ✋🏻🛑⛔️ Create fallback review texts for each sentiment label*"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"id": "b3cd2a50"
},
"outputs": [],
"source": [
"synthetic_reviews_by_sentiment = {\n",
" \"positive\": [\n",
" \"The hotel was excellent and the overall experience was very satisfying.\",\n",
" \"Very good service, clean rooms, and a pleasant stay.\",\n",
" \"A great experience with friendly staff and strong service quality.\",\n",
" \"The hotel exceeded expectations and the stay was very comfortable.\",\n",
" \"Excellent service and a very enjoyable overall experience.\",\n",
" \"The rooms were clean and the hotel atmosphere was very pleasant.\",\n",
" \"A very satisfying stay with professional staff and good facilities.\",\n",
" \"The hotel experience was smooth, comfortable, and enjoyable.\",\n",
" \"Strong service quality and a very positive stay overall.\",\n",
" \"The hotel offered a high-quality experience from start to finish.\"\n",
" ],\n",
" \"neutral\": [\n",
" \"The stay was acceptable but not especially memorable.\",\n",
" \"The hotel was average and the experience was correct overall.\",\n",
" \"Some aspects were good, while others could be improved.\",\n",
" \"The stay was fine but quite standard.\",\n",
" \"The hotel met expectations without standing out.\",\n",
" \"The overall experience was balanced, with both positive and negative points.\",\n",
" \"The stay was decent and the service was acceptable.\",\n",
" \"Nothing was particularly bad, but nothing was exceptional either.\",\n",
" \"The experience was normal and relatively satisfactory.\",\n",
" \"The hotel was reasonable but could improve in some areas.\"\n",
" ],\n",
" \"negative\": [\n",
" \"The experience was disappointing and the service could be improved.\",\n",
" \"The hotel did not fully meet expectations.\",\n",
" \"Several aspects of the stay were below standard.\",\n",
" \"The service quality was disappointing during the stay.\",\n",
" \"The overall hotel experience was less satisfying than expected.\",\n",
" \"Some important elements of the stay need improvement.\",\n",
" \"The hotel experience was not fully satisfactory.\",\n",
" \"The service and comfort level were below expectations.\",\n",
" \"The stay had several weak points and was disappointing overall.\",\n",
" \"The customer experience should be improved in future.\"\n",
" ]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fQhfVaDmuULT"
},
"source": [
"### *b. Generate 10 reviews per hotel using real hotel reviews when available*"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "l2SRc3PjuTGM"
},
"outputs": [],
"source": [
"review_rows = []\n",
"\n",
"for _, row in df_books.iterrows():\n",
" hotel_name = row[\"title\"]\n",
" sentiment_label = row[\"sentiment_label\"]\n",
"\n",
" hotel_reviews = reviews_raw[reviews_raw[\"name\"] == hotel_name][\"reviews.text\"].dropna().tolist()\n",
"\n",
" if len(hotel_reviews) >= 10:\n",
" sampled_reviews = random.sample(hotel_reviews, 10)\n",
" elif len(hotel_reviews) > 0:\n",
" sampled_reviews = hotel_reviews.copy()\n",
" while len(sampled_reviews) < 10:\n",
" sampled_reviews.append(random.choice(hotel_reviews))\n",
" else:\n",
" sampled_reviews = [random.choice(synthetic_reviews_by_sentiment[sentiment_label]) for _ in range(10)]\n",
"\n",
" for review_text in sampled_reviews[:10]:\n",
" review_rows.append({\n",
" \"title\": hotel_name,\n",
" \"sentiment_label\": sentiment_label,\n",
" \"review_text\": review_text,\n",
" \"rating\": row[\"rating\"],\n",
" \"popularity_score\": row[\"popularity_score\"]\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bmJMXF-Bukdm"
},
"source": [
"### *c. Create the final dataframe df_reviews & save it as synthetic_book_reviews.csv*"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"id": "ZUKUqZsuumsp"
},
"outputs": [],
"source": [
"df_reviews = pd.DataFrame(review_rows)\n",
"df_reviews.to_csv(\"synthetic_book_reviews.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3946e521",
"outputId": "89a60601-d358-4f6a-b789-5e81b04ca222"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Wrote synthetic_title_level_features.csv\n",
"✅ Wrote synthetic_monthly_revenue_series.csv\n"
]
}
],
"source": [
"\n",
"# ============================================================\n",
"# ✅ Create \"R-ready\" derived inputs (root-level files)\n",
"# ============================================================\n",
"# These two files make the R notebook robust and fast:\n",
"# 1) synthetic_title_level_features.csv -> regression-ready, one row per title\n",
"# 2) synthetic_monthly_revenue_series.csv -> forecasting-ready, one row per month\n",
"\n",
"import numpy as np\n",
"\n",
"def _safe_num(s):\n",
" return pd.to_numeric(\n",
" pd.Series(s).astype(str).str.replace(r\"[^0-9.]\", \"\", regex=True),\n",
" errors=\"coerce\"\n",
" )\n",
"\n",
"# --- Clean hotel metadata (price/rating) ---\n",
"df_books_r = df_books.copy()\n",
"if \"price\" in df_books_r.columns:\n",
" df_books_r[\"price\"] = _safe_num(df_books_r[\"price\"])\n",
"if \"rating\" in df_books_r.columns:\n",
" df_books_r[\"rating\"] = _safe_num(df_books_r[\"rating\"])\n",
"\n",
"df_books_r[\"title\"] = df_books_r[\"title\"].astype(str).str.strip()\n",
"\n",
"# --- Clean sales ---\n",
"df_sales_r = df_sales.copy()\n",
"df_sales_r[\"title\"] = df_sales_r[\"title\"].astype(str).str.strip()\n",
"df_sales_r[\"month\"] = pd.to_datetime(df_sales_r[\"month\"], errors=\"coerce\")\n",
"df_sales_r[\"units_sold\"] = _safe_num(df_sales_r[\"units_sold\"])\n",
"\n",
"# --- Clean reviews ---\n",
"df_reviews_r = df_reviews.copy()\n",
"df_reviews_r[\"title\"] = df_reviews_r[\"title\"].astype(str).str.strip()\n",
"df_reviews_r[\"sentiment_label\"] = df_reviews_r[\"sentiment_label\"].astype(str).str.lower().str.strip()\n",
"if \"rating\" in df_reviews_r.columns:\n",
" df_reviews_r[\"rating\"] = _safe_num(df_reviews_r[\"rating\"])\n",
"if \"popularity_score\" in df_reviews_r.columns:\n",
" df_reviews_r[\"popularity_score\"] = _safe_num(df_reviews_r[\"popularity_score\"])\n",
"\n",
"# --- Sentiment shares per title (from reviews) ---\n",
"sent_counts = (\n",
" df_reviews_r.groupby([\"title\", \"sentiment_label\"])\n",
" .size()\n",
" .unstack(fill_value=0)\n",
")\n",
"for lab in [\"positive\", \"neutral\", \"negative\"]:\n",
" if lab not in sent_counts.columns:\n",
" sent_counts[lab] = 0\n",
"\n",
"sent_counts[\"total_reviews\"] = sent_counts[[\"positive\", \"neutral\", \"negative\"]].sum(axis=1)\n",
"den = sent_counts[\"total_reviews\"].replace(0, np.nan)\n",
"sent_counts[\"share_positive\"] = sent_counts[\"positive\"] / den\n",
"sent_counts[\"share_neutral\"] = sent_counts[\"neutral\"] / den\n",
"sent_counts[\"share_negative\"] = sent_counts[\"negative\"] / den\n",
"sent_counts = sent_counts.reset_index()\n",
"\n",
"# --- Sales aggregation per title ---\n",
"sales_by_title = (\n",
" df_sales_r.dropna(subset=[\"title\"])\n",
" .groupby(\"title\", as_index=False)\n",
" .agg(\n",
" months_observed=(\"month\", \"nunique\"),\n",
" avg_units_sold=(\"units_sold\", \"mean\"),\n",
" total_units_sold=(\"units_sold\", \"sum\"),\n",
" )\n",
")\n",
"\n",
"# --- Hotel-level features (join sales + hotel metadata + sentiment) ---\n",
"df_title = (\n",
" sales_by_title\n",
" .merge(df_books_r[[\"title\", \"price\", \"rating\"]], on=\"title\", how=\"left\")\n",
" .merge(sent_counts[[\"title\", \"share_positive\", \"share_neutral\", \"share_negative\", \"total_reviews\"]],\n",
" on=\"title\", how=\"left\")\n",
")\n",
"\n",
"df_title[\"avg_revenue\"] = df_title[\"avg_units_sold\"] * df_title[\"price\"]\n",
"df_title[\"total_revenue\"] = df_title[\"total_units_sold\"] * df_title[\"price\"]\n",
"\n",
"df_title.to_csv(\"synthetic_title_level_features.csv\", index=False)\n",
"print(\"✅ Wrote synthetic_title_level_features.csv\")\n",
"\n",
"# --- Monthly revenue series (proxy: units_sold * price) ---\n",
"monthly_rev = (\n",
" df_sales_r.merge(df_books_r[[\"title\", \"price\"]], on=\"title\", how=\"left\")\n",
")\n",
"monthly_rev[\"revenue\"] = monthly_rev[\"units_sold\"] * monthly_rev[\"price\"]\n",
"\n",
"df_monthly = (\n",
" monthly_rev.dropna(subset=[\"month\"])\n",
" .groupby(\"month\", as_index=False)[\"revenue\"]\n",
" .sum()\n",
" .rename(columns={\"revenue\": \"total_revenue\"})\n",
" .sort_values(\"month\")\n",
")\n",
"# if revenue is all NA (e.g., missing price), fallback to units_sold as a teaching proxy\n",
"if df_monthly[\"total_revenue\"].notna().sum() == 0:\n",
" df_monthly = (\n",
" df_sales_r.dropna(subset=[\"month\"])\n",
" .groupby(\"month\", as_index=False)[\"units_sold\"]\n",
" .sum()\n",
" .rename(columns={\"units_sold\": \"total_revenue\"})\n",
" .sort_values(\"month\")\n",
" )\n",
"\n",
"df_monthly[\"month\"] = pd.to_datetime(df_monthly[\"month\"], errors=\"coerce\").dt.strftime(\"%Y-%m-%d\")\n",
"df_monthly.to_csv(\"synthetic_monthly_revenue_series.csv\", index=False)\n",
"print(\"✅ Wrote synthetic_monthly_revenue_series.csv\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RYvGyVfXuo54"
},
"source": [
"### *d. ✋🏻🛑⛔️ View the first few lines*"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xfE8NMqOurKo",
"outputId": "952335f7-1288-4af7-f32b-3f2e52e7060b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" title sentiment_label \\\n",
"0 1785 Inn negative \n",
"1 1785 Inn negative \n",
"2 1785 Inn negative \n",
"3 1785 Inn negative \n",
"4 1785 Inn negative \n",
"\n",
" review_text rating popularity_score \n",
"0 I am shocked by how many good reviews this res... 2.625 2 \n",
"1 Very Reasonably priced, Nice Pub, Great breakf... 2.625 2 \n",
"2 to share your opinion of this businesswith YP ... 2.625 2 \n",
"3 My wife and I ate dinner at the 1785 inn durin... 2.625 2 \n",
"4 Billy the bartender is awesome - ask him about... 2.625 2 \n"
]
}
],
"source": [
"print(df_reviews.head())"
]
}
],
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