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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "4ba6aba8"
},
"source": [
"# 🤖 **Data Collection, Creation, Storage, and Processing**\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jpASMyIQMaAq"
},
"source": [
"## **1.** 📦 Install required packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f48c8f8c",
"outputId": "7dfed569-99a5-44c7-b7dd-afabaa3d9257"
},
"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) (2025.3)\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) (4.61.1)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\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) (8.3.1)\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.** ⛏ Web-scrape all book titles, prices, and ratings from books.toscrape.com"
]
},
{
"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": 2,
"metadata": {
"id": "91d52125"
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import pandas as pd\n",
"import time\n",
"\n",
"base_url = \"https://books.toscrape.com/catalogue/page-{}.html\"\n",
"headers = {\"User-Agent\": \"Mozilla/5.0\"}\n",
"\n",
"titles, prices, ratings = [], [], []"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oCdTsin2Yfp3"
},
"source": [
"### *b. Fill titles, prices, and ratings from the web pages*"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "xqO5Y3dnYhxt"
},
"outputs": [],
"source": [
"# Loop through all 50 pages\n",
"for page in range(1, 51):\n",
" url = base_url.format(page)\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, \"html.parser\")\n",
" books = soup.find_all(\"article\", class_=\"product_pod\")\n",
"\n",
" for book in books:\n",
" titles.append(book.h3.a[\"title\"])\n",
" prices.append(float(book.find(\"p\", class_=\"price_color\").text[1:]))\n",
" ratings.append(book.p.get(\"class\")[1])\n",
"\n",
" time.sleep(0.5) # polite scraping delay"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T0TOeRC4Yrnn"
},
"source": [
"### *c. ✋🏻🛑⛔️ Create a dataframe df_books that contains the now complete \"title\", \"price\", and \"rating\" objects*"
]
},
{
"cell_type": "markdown",
"source": [
"**First part to complete**"
],
"metadata": {
"id": "bPhNHyHQ20KI"
}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "l5FkkNhUYTHh"
},
"outputs": [],
"source": [
"df_books = pd.DataFrame({\"title\": titles, \"price\": prices, \"rating\": ratings})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "duI5dv3CZYvF"
},
"source": [
"### *d. Save web-scraped dataframe either as a CSV or Excel file*"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "lC1U_YHtZifh"
},
"outputs": [],
"source": [
"# 💾 Save to CSV\n",
"df_books.to_csv(\"books_data.csv\", index=False)\n",
"\n",
"# 💾 Or save to Excel\n",
"# df_books.to_excel(\"books_data.xlsx\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qMjRKMBQZlJi"
},
"source": [
"### *e. ✋🏻🛑⛔️ View first fiew lines*"
]
},
{
"cell_type": "markdown",
"source": [
"**Second part to complete**"
],
"metadata": {
"id": "F54lNj_c3VB2"
}
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "O_wIvTxYZqCK",
"outputId": "28f6f57d-c34a-4d52-f473-eede10139889"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" title price rating\n",
"0 A Light in the Attic 51.77 Three\n",
"1 Tipping the Velvet 53.74 One\n",
"2 Soumission 50.10 One\n",
"3 Sharp Objects 47.82 Four\n",
"4 Sapiens: A Brief History of Humankind 54.23 Five"
],
"text/html": [
"\n",
" <div id=\"df-30f83754-00af-4649-8299-fd130f8cf24c\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>price</th>\n",
" <th>rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A Light in the Attic</td>\n",
" <td>51.77</td>\n",
" <td>Three</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tipping the Velvet</td>\n",
" <td>53.74</td>\n",
" <td>One</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Soumission</td>\n",
" <td>50.10</td>\n",
" <td>One</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sharp Objects</td>\n",
" <td>47.82</td>\n",
" <td>Four</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sapiens: A Brief History of Humankind</td>\n",
" <td>54.23</td>\n",
" <td>Five</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30f83754-00af-4649-8299-fd130f8cf24c')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
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"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
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" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
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" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-30f83754-00af-4649-8299-fd130f8cf24c');\n",
" const dataTable =\n",
" 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",
" dataTable['output_type'] = 'display_data';\n",
" 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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"\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"display(df_books\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Tipping the Velvet\",\n \"Sapiens: A Brief History of Humankind\",\n \"Soumission\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.647672562837028,\n \"min\": 47.82,\n \"max\": 54.23,\n \"num_unique_values\": 5,\n \"samples\": [\n 53.74,\n 54.23,\n 50.1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"One\",\n \"Five\",\n \"Three\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {}
}
],
"source": [
"display(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": 9,
"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 rating (with some randomness) with a generate_popularity_score function*"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "mnd5hdAbaNjz"
},
"outputs": [],
"source": [
"def generate_popularity_score(rating):\n",
" base = {\"One\": 2, \"Two\": 3, \"Three\": 3, \"Four\": 4, \"Five\": 4}.get(rating, 3)\n",
" trend_factor = random.choices([-1, 0, 1], weights=[1, 3, 2])[0]\n",
" return int(np.clip(base + trend_factor, 1, 5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n4-TaNTFgPak"
},
"source": [
"### *c. ✋🏻🛑⛔️ Run the function to create a \"popularity_score\" column from \"rating\"*"
]
},
{
"cell_type": "markdown",
"source": [
"**Third part to complete**"
],
"metadata": {
"id": "fP1PDJwE5CdT"
}
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "V-G3OCUCgR07"
},
"outputs": [],
"source": [
"df_books['popularity_score'] = df_books['rating'].apply(generate_popularity_score)"
]
},
{
"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": 12,
"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": 13,
"metadata": {
"id": "tafQj8_7gYCG"
},
"outputs": [],
"source": [
"df_books['sentiment_label'] = df_books['popularity_score'].apply(get_sentiment)"
]
},
{
"cell_type": "markdown",
"source": [
"Fiurth thing to complete (!!)"
],
"metadata": {
"id": "8yC1FQoe5NQb"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "T8AdKkmASq9a"
},
"source": [
"## **4.** 📈 Generate synthetic book sales data of 18 months"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OhXbdGD5fH0c"
},
"source": [
"### *a. Create a generate_sales_profit function that would generate sales patterns based on sentiment_label (with some randomness)*"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "qkVhYPXGbgEn"
},
"outputs": [],
"source": [
"def generate_sales_profile(sentiment):\n",
" months = pd.date_range(end=datetime.today(), periods=18, freq=\"M\")\n",
"\n",
" if sentiment == \"positive\":\n",
" base = random.randint(200, 300)\n",
" trend = np.linspace(base, base + random.randint(20, 60), len(months))\n",
" elif sentiment == \"negative\":\n",
" base = random.randint(20, 80)\n",
" trend = np.linspace(base, base - random.randint(10, 30), len(months))\n",
" else: # neutral\n",
" base = random.randint(80, 160)\n",
" trend = np.full(len(months), base + random.randint(-10, 10))\n",
"\n",
" seasonality = 10 * np.sin(np.linspace(0, 3 * np.pi, len(months)))\n",
" noise = np.random.normal(0, 5, len(months))\n",
" monthly_sales = np.clip(trend + seasonality + noise, a_min=0, a_max=None).astype(int)\n",
"\n",
" return list(zip(months.strftime(\"%Y-%m\"), monthly_sales))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L2ak1HlcgoTe"
},
"source": [
"### *b. Run the function as part of building sales_data*"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "SlJ24AUafoDB"
},
"outputs": [],
"source": [
"sales_data = []\n",
"for _, row in df_books.iterrows():\n",
" records = generate_sales_profile(row[\"sentiment_label\"])\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*\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MzbZvLcAhGaH",
"outputId": "3033960f-762f-4806-9016-09a610aa7f1b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" title month units_sold sentiment_label\n",
"0 A Light in the Attic 2024-09 100 neutral\n",
"1 A Light in the Attic 2024-10 109 neutral\n",
"2 A Light in the Attic 2024-11 102 neutral\n",
"3 A Light in the Attic 2024-12 107 neutral\n",
"4 A Light in the Attic 2025-01 108 neutral\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 reviews"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gi4y9M9KuDWx"
},
"source": [
"### *a. ✋🏻🛑⛔️ Ask ChatGPT to create a list of 50 distinct generic book review texts for the sentiment labels \"positive\", \"neutral\", and \"negative\" called synthetic_reviews_by_sentiment*"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "b3cd2a50"
},
"outputs": [],
"source": [
"synthetic_reviews_by_sentiment = {\n",
" \"positive\": [\n",
" \"A compelling and heartwarming read that stayed with me long after I finished.\",\n",
" \"Brilliantly written! The characters were unforgettable and the plot was engaging.\",\n",
" \"One of the best books I've read this year — inspiring and emotionally rich.\",\n",
" \"An absolute page-turner from start to finish.\",\n",
" \"The storytelling was captivating and beautifully crafted.\",\n",
" \"I couldn't put it down; every chapter pulled me in deeper.\",\n",
" \"A masterfully told story with vivid and memorable characters.\",\n",
" \"Emotionally powerful and wonderfully executed.\",\n",
" \"The author’s writing style is elegant and immersive.\",\n",
" \"A truly uplifting and satisfying read.\",\n",
" \"This book exceeded all my expectations.\",\n",
" \"Rich in detail and full of heart.\",\n",
" \"An inspiring story that left me feeling hopeful.\",\n",
" \"The pacing was perfect and kept me engaged throughout.\",\n",
" \"A fantastic journey that I didn’t want to end.\",\n",
" \"Thought-provoking and deeply moving.\",\n",
" \"A beautifully imagined world with compelling themes.\",\n",
" \"The dialogue felt natural and authentic.\",\n",
" \"An unforgettable literary experience.\",\n",
" \"Creative, original, and wonderfully written.\",\n",
" \"The characters felt real and relatable.\",\n",
" \"An engaging plot with satisfying twists.\",\n",
" \"I was hooked from the very first page.\",\n",
" \"A powerful narrative that resonated with me.\",\n",
" \"Smart, insightful, and emotionally rich.\",\n",
" \"An entertaining and meaningful story.\",\n",
" \"The writing was polished and full of depth.\",\n",
" \"A refreshing and unique perspective.\",\n",
" \"The emotional impact of this book was incredible.\",\n",
" \"A must-read for fans of the genre.\",\n",
" \"It balanced humor and heart perfectly.\",\n",
" \"A gripping story that kept me invested.\",\n",
" \"The themes were explored thoughtfully and effectively.\",\n",
" \"An excellent blend of action and character development.\",\n",
" \"A deeply satisfying conclusion to a great story.\",\n",
" \"Beautiful prose paired with a compelling storyline.\",\n",
" \"It left me thinking about it for days.\",\n",
" \"An inspiring tale of resilience and growth.\",\n",
" \"The atmosphere was vivid and immersive.\",\n",
" \"A rewarding and memorable reading experience.\",\n",
" \"The author created a world I completely believed in.\",\n",
" \"Every chapter added something meaningful.\",\n",
" \"A story told with passion and precision.\",\n",
" \"Truly a standout book in its category.\",\n",
" \"An emotionally satisfying and engaging read.\",\n",
" \"The plot twists were surprising yet believable.\",\n",
" \"A wonderfully crafted and enjoyable novel.\",\n",
" \"It struck the perfect balance between drama and warmth.\",\n",
" \"An outstanding work of storytelling.\",\n",
" \"A delightful and enriching book overall.\"\n",
" ],\n",
" \"neutral\": [\n",
" \"An average book — not great, but not bad either.\",\n",
" \"Some parts really stood out, others felt a bit flat.\",\n",
" \"It was okay overall. A decent way to pass the time.\",\n",
" \"A fairly standard story without many surprises.\",\n",
" \"The writing was fine, though not particularly memorable.\",\n",
" \"An easy read, but nothing especially remarkable.\",\n",
" \"It had its moments, though it didn’t fully captivate me.\",\n",
" \"The characters were decent but not very memorable.\",\n",
" \"A straightforward plot that delivered what it promised.\",\n",
" \"Not my favorite, but not disappointing either.\",\n",
" \"The pacing was uneven in places.\",\n",
" \"An enjoyable enough read, though somewhat predictable.\",\n",
" \"It was mildly interesting but lacked depth.\",\n",
" \"A simple story told competently.\",\n",
" \"Some chapters were engaging, others less so.\",\n",
" \"A solid effort, though it didn’t stand out.\",\n",
" \"It met my expectations, but didn’t exceed them.\",\n",
" \"The themes were clear but not deeply explored.\",\n",
" \"An adequate book for a quiet afternoon.\",\n",
" \"The storyline was easy to follow but fairly conventional.\",\n",
" \"There were interesting ideas, though not fully developed.\",\n",
" \"The writing style was straightforward and clear.\",\n",
" \"A mixed experience with highs and lows.\",\n",
" \"It started strong but lost momentum midway.\",\n",
" \"An acceptable read with room for improvement.\",\n",
" \"The ending was satisfactory but not surprising.\",\n",
" \"Some aspects worked better than others.\",\n",
" \"A predictable but readable story.\",\n",
" \"It held my attention, though not consistently.\",\n",
" \"The characters were serviceable for the plot.\",\n",
" \"An average addition to the genre.\",\n",
" \"It had potential that wasn’t fully realized.\",\n",
" \"The book was competently written.\",\n",
" \"A neutral reading experience overall.\",\n",
" \"Not particularly engaging, but not dull either.\",\n",
" \"It delivered a standard narrative arc.\",\n",
" \"Some emotional moments, though not deeply impactful.\",\n",
" \"The setting was interesting but underused.\",\n",
" \"An easy but forgettable read.\",\n",
" \"It was fine, though I likely won’t revisit it.\",\n",
" \"The dialogue was functional but unremarkable.\",\n",
" \"A moderately entertaining story.\",\n",
" \"It felt balanced but lacked excitement.\",\n",
" \"The book maintained a steady tone throughout.\",\n",
" \"Neither impressive nor disappointing.\",\n",
" \"A passable story with conventional elements.\",\n",
" \"It did what it set out to do.\",\n",
" \"A readable book without strong highs or lows.\",\n",
" \"The plot moved along at a reasonable pace.\",\n",
" \"Overall, a fairly ordinary reading experience.\"\n",
" ],\n",
" \"negative\": [\n",
" \"I struggled to get through this one — it just didn’t grab me.\",\n",
" \"The plot was confusing and the characters felt underdeveloped.\",\n",
" \"Disappointing. I had high hopes, but they weren't met.\",\n",
" \"The pacing was painfully slow and unfocused.\",\n",
" \"I found the story dull and unengaging.\",\n",
" \"The writing felt flat and uninspired.\",\n",
" \"The characters lacked depth and realism.\",\n",
" \"A forgettable and frustrating read.\",\n",
" \"The dialogue felt awkward and forced.\",\n",
" \"It failed to hold my interest.\",\n",
" \"The story seemed disorganized and messy.\",\n",
" \"I couldn’t connect with any of the characters.\",\n",
" \"The ending was unsatisfying and abrupt.\",\n",
" \"The book felt much longer than it needed to be.\",\n",
" \"A predictable plot with no real surprises.\",\n",
" \"The themes were shallow and poorly executed.\",\n",
" \"I found myself skimming just to finish it.\",\n",
" \"The narrative lacked clarity and focus.\",\n",
" \"An underwhelming and disappointing experience.\",\n",
" \"The story never really came together.\",\n",
" \"It started poorly and never improved.\",\n",
" \"The writing style didn’t appeal to me at all.\",\n",
" \"A tedious read from beginning to end.\",\n",
" \"The characters’ motivations felt unclear.\",\n",
" \"It lacked originality and creativity.\",\n",
" \"The plot twists felt forced and unrealistic.\",\n",
" \"I expected much more from this author.\",\n",
" \"The book felt rushed and incomplete.\",\n",
" \"A bland story with little emotional impact.\",\n",
" \"The world-building was weak and inconsistent.\",\n",
" \"I regret spending time on this book.\",\n",
" \"The storyline was repetitive and dull.\",\n",
" \"It failed to deliver on its premise.\",\n",
" \"The tone felt inconsistent and confusing.\",\n",
" \"The book was difficult to stay engaged with.\",\n",
" \"The emotional moments felt unearned.\",\n",
" \"A frustratingly uneven narrative.\",\n",
" \"The characters made unrealistic decisions.\",\n",
" \"It didn’t live up to the hype.\",\n",
" \"The writing felt overly simplistic.\",\n",
" \"The book lacked tension and excitement.\",\n",
" \"A poorly executed concept.\",\n",
" \"The pacing dragged throughout.\",\n",
" \"I found it more irritating than enjoyable.\",\n",
" \"The plot holes were distracting.\",\n",
" \"An unsatisfying and forgettable novel.\",\n",
" \"The storytelling felt amateurish.\",\n",
" \"It never fully developed its ideas.\",\n",
" \"A disappointing addition to the genre.\",\n",
" \"Overall, not a book I would recommend.\"\n",
" ]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fQhfVaDmuULT"
},
"source": [
"### *b. Generate 10 reviews per book using random sampling from the corresponding 50*"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "l2SRc3PjuTGM"
},
"outputs": [],
"source": [
"review_rows = []\n",
"for _, row in df_books.iterrows():\n",
" title = row['title']\n",
" sentiment_label = row['sentiment_label']\n",
" review_pool = synthetic_reviews_by_sentiment[sentiment_label]\n",
" sampled_reviews = random.sample(review_pool, 10)\n",
" for review_text in sampled_reviews:\n",
" review_rows.append({\n",
" \"title\": title,\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": 20,
"metadata": {
"id": "ZUKUqZsuumsp"
},
"outputs": [],
"source": [
"df_reviews = pd.DataFrame(review_rows)\n",
"df_reviews.to_csv(\"synthetic_book_reviews.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"source": [
"### *c. inputs for R*"
],
"metadata": {
"id": "_602pYUS3gY5"
}
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3946e521",
"outputId": "cdf0109a-0720-4a51-9882-77712bb22e9d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Wrote synthetic_title_level_features.csv\n",
"✅ Wrote synthetic_monthly_revenue_series.csv\n"
]
}
],
"source": [
"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 book 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",
"# --- Title-level features (join sales + books + 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": "markdown",
"source": [
"**Last part to complete**"
],
"metadata": {
"id": "6Ub_snNWKra_"
}
},
{
"cell_type": "code",
"source": [
"display(df_books.head())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "3yDzBL_S3AOw",
"outputId": "34c6963f-0e45-40e7-c58c-af480f43556b"
},
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" title price rating popularity_score \\\n",
"0 A Light in the Attic 51.77 Three 3 \n",
"1 Tipping the Velvet 53.74 One 2 \n",
"2 Soumission 50.10 One 2 \n",
"3 Sharp Objects 47.82 Four 4 \n",
"4 Sapiens: A Brief History of Humankind 54.23 Five 3 \n",
"\n",
" sentiment_label \n",
"0 neutral \n",
"1 negative \n",
"2 negative \n",
"3 positive \n",
"4 neutral "
],
"text/html": [
"\n",
" <div id=\"df-37551206-2c1b-41a7-b409-da6a6e900bae\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>price</th>\n",
" <th>rating</th>\n",
" <th>popularity_score</th>\n",
" <th>sentiment_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A Light in the Attic</td>\n",
" <td>51.77</td>\n",
" <td>Three</td>\n",
" <td>3</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tipping the Velvet</td>\n",
" <td>53.74</td>\n",
" <td>One</td>\n",
" <td>2</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Soumission</td>\n",
" <td>50.10</td>\n",
" <td>One</td>\n",
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