{ "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": "13d0dd5e-82c6-489f-b1f0-e970186a4eb7" }, "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": "code", "execution_count": 4, "metadata": { "id": "l5FkkNhUYTHh" }, "outputs": [], "source": [ "# 🗂️ Create DataFrame\n", "df_books = pd.DataFrame({\n", " \"title\": titles,\n", " \"price\": prices,\n", " \"rating\": ratings\n", "})" ] }, { "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": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "O_wIvTxYZqCK", "outputId": "349b36b0-c008-4fd5-d4a4-dba38ae18337" }, "outputs": [ { "output_type": "execute_result", "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", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_books", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 6 } ], "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": 7, "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": 8, "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": "code", "execution_count": 9, "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": 10, "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": 11, "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 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": 12, "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": 13, "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*" ] }, { "cell_type": "code", "execution_count": 14, "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": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MzbZvLcAhGaH", "outputId": "c692bb04-7263-4115-a2ba-c72fe0180722" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " title month units_sold sentiment_label\n", "0 A Light in the Attic 2024-08 100 neutral\n", "1 A Light in the Attic 2024-09 109 neutral\n", "2 A Light in the Attic 2024-10 102 neutral\n", "3 A Light in the Attic 2024-11 107 neutral\n", "4 A Light in the Attic 2024-12 108 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": 16, "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", " \"The author's storytelling was vivid and powerful. Highly recommended!\",\n", " \"An absolute masterpiece. I couldn't put it down from start to finish.\",\n", " \"Gripping, intelligent, and beautifully crafted — I loved every page.\",\n", " \"The emotional depth and layered narrative were just perfect.\",\n", " \"A thought-provoking journey with stunning character development.\",\n", " \"Everything about this book just clicked. A top-tier read!\",\n", " \"A flawless blend of emotion, intrigue, and style. Truly impressive.\",\n", " \"Absolutely stunning work of fiction. Five stars from me.\",\n", " \"Remarkably executed with breathtaking prose.\",\n", " \"The pacing was perfect and I was hooked from page one.\",\n", " \"Heartfelt and hopeful — a story well worth telling.\",\n", " \"A vivid journey through complex emotions and stunning imagery.\",\n", " \"This book had soul. Every word felt like it mattered.\",\n", " \"It delivered more than I ever expected. Powerful and wise.\",\n", " \"The characters leapt off the page and into my heart.\",\n", " \"I could see every scene clearly in my mind — beautifully descriptive.\",\n", " \"Refreshing, original, and impossible to forget.\",\n", " \"A radiant celebration of resilience and love.\",\n", " \"Powerful themes handled with grace and insight.\",\n", " \"An unforgettable literary experience.\",\n", " \"The best book club pick we’ve had all year.\",\n", " \"A layered, lyrical narrative that resonates deeply.\",\n", " \"Surprising, profound, and deeply humane.\",\n", " \"One of those rare books I wish I could read again for the first time.\",\n", " \"Both epic and intimate — a perfect balance.\",\n", " \"It reads like a love letter to the human spirit.\",\n", " \"Satisfying and uplifting with a memorable ending.\",\n", " \"This novel deserves every bit of praise it gets.\",\n", " \"Introspective, emotional, and elegantly composed.\",\n", " \"A tour de force in contemporary fiction.\",\n", " \"Left me smiling, teary-eyed, and completely fulfilled.\",\n", " \"A novel with the rare ability to entertain and enlighten.\",\n", " \"Incredibly moving. I highlighted so many lines.\",\n", " \"A smart, sensitive take on relationships and identity.\",\n", " \"You feel wiser by the end of it.\",\n", " \"A gorgeously crafted tale about hope and second chances.\",\n", " \"Poignant and real — a beautiful escape.\",\n", " \"Brims with insight and authenticity.\",\n", " \"Compelling characters and a satisfying plot.\",\n", " \"An empowering and important read.\",\n", " \"Elegantly crafted and deeply humane.\",\n", " \"Taut storytelling that never lets go.\",\n", " \"Each chapter offered a new treasure.\",\n", " \"Lyrical writing that stays with you.\",\n", " \"A wonderful blend of passion and thoughtfulness.\",\n", " \"Uplifting, honest, and completely engrossing.\",\n", " \"This one made me believe in storytelling again.\"\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", " \"The writing was fine, though I didn’t fully connect with the story.\",\n", " \"Had a few memorable moments but lacked depth in some areas.\",\n", " \"A mixed experience — neither fully engaging nor forgettable.\",\n", " \"There was potential, but it didn't quite come together for me.\",\n", " \"A reasonable effort that just didn’t leave a lasting impression.\",\n", " \"Serviceable but not something I'd go out of my way to recommend.\",\n", " \"Not much to dislike, but not much to rave about either.\",\n", " \"It had its strengths, though they didn’t shine consistently.\",\n", " \"I’m on the fence — parts were enjoyable, others not so much.\",\n", " \"The book had a unique concept but lacked execution.\",\n", " \"A middle-of-the-road read.\",\n", " \"Engaging at times, but it lost momentum.\",\n", " \"Would have benefited from stronger character development.\",\n", " \"It passed the time, but I wouldn't reread it.\",\n", " \"The plot had some holes that affected immersion.\",\n", " \"Mediocre pacing made it hard to stay invested.\",\n", " \"Satisfying in parts, underwhelming in others.\",\n", " \"Neutral on this one — didn’t love it or hate it.\",\n", " \"Fairly forgettable but with glimpses of promise.\",\n", " \"The themes were solid, but not well explored.\",\n", " \"Competent, just not compelling.\",\n", " \"Had moments of clarity and moments of confusion.\",\n", " \"I didn’t regret reading it, but I wouldn’t recommend it.\",\n", " \"Readable, yet uninspired.\",\n", " \"There was a spark, but it didn’t ignite.\",\n", " \"A slow burn that didn’t quite catch fire.\",\n", " \"I expected more nuance given the premise.\",\n", " \"A safe, inoffensive choice.\",\n", " \"Some parts lagged, others piqued my interest.\",\n", " \"Decent, but needed polish.\",\n", " \"Moderately engaging but didn’t stick the landing.\",\n", " \"It simply lacked that emotional punch.\",\n", " \"Just fine — no better, no worse.\",\n", " \"Some thoughtful passages amid otherwise dry writing.\",\n", " \"I appreciated the ideas more than the execution.\",\n", " \"Struggled with cohesion.\",\n", " \"Solidly average.\",\n", " \"Good on paper, flat in practice.\",\n", " \"A few bright spots, but mostly dim.\",\n", " \"The kind of book that fades from memory.\",\n", " \"It scratched the surface but didn’t dig deep.\",\n", " \"Standard fare with some promise.\",\n", " \"Okay, but not memorable.\",\n", " \"Had potential that went unrealized.\",\n", " \"Could have been tighter, sharper, deeper.\",\n", " \"A blend of mediocrity and mild interest.\",\n", " \"I kept reading, but barely.\"\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", " \"Uninspired writing and a story that never quite took off.\",\n", " \"Unfortunately, it was dull and predictable throughout.\",\n", " \"The pacing dragged and I couldn’t find anything compelling.\",\n", " \"This felt like a chore to read — lacked heart and originality.\",\n", " \"Nothing really worked for me in this book.\",\n", " \"A frustrating read that left me unsatisfied.\",\n", " \"I kept hoping it would improve, but it never did.\",\n", " \"The characters didn’t feel real, and the dialogue was forced.\",\n", " \"I couldn't connect with the story at all.\",\n", " \"A slow, meandering narrative with little payoff.\",\n", " \"Tried too hard to be deep, but just felt empty.\",\n", " \"The tone was uneven and confusing.\",\n", " \"Way too repetitive and lacking progression.\",\n", " \"The ending was abrupt and unsatisfying.\",\n", " \"No emotional resonance — I felt nothing throughout.\",\n", " \"I expected much more, but this fell flat.\",\n", " \"Poorly edited and full of clichés.\",\n", " \"The premise was interesting, but poorly executed.\",\n", " \"Just didn’t live up to the praise.\",\n", " \"A disjointed mess from start to finish.\",\n", " \"Overly long and painfully dull.\",\n", " \"Dialogue that felt robotic and unrealistic.\",\n", " \"A hollow shell of what it could’ve been.\",\n", " \"It lacked a coherent structure.\",\n", " \"More confusing than complex.\",\n", " \"Reading it felt like a task, not a treat.\",\n", " \"There was no tension, no emotion — just words.\",\n", " \"Characters with no motivation or development.\",\n", " \"The plot twists were nonsensical.\",\n", " \"Regret buying this book.\",\n", " \"Nothing drew me in, nothing made me stay.\",\n", " \"Too many subplots and none were satisfying.\",\n", " \"Tedious and unimaginative.\",\n", " \"Like reading a rough draft.\",\n", " \"Disjointed, distant, and disappointing.\",\n", " \"A lot of buildup with no payoff.\",\n", " \"I don’t understand the hype.\",\n", " \"This book simply didn’t work.\",\n", " \"Forgettable in every sense.\",\n", " \"More effort should’ve gone into editing.\",\n", " \"The story lost its way early on.\",\n", " \"It dragged endlessly.\",\n", " \"I kept checking how many pages were left.\",\n", " \"This lacked vision and clarity.\",\n", " \"I expected substance — got fluff.\",\n", " \"It failed to make me care.\"\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": 17, "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": 18, "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": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3946e521", "outputId": "514d7bef-0488-4933-b03c-953b9e8a7f66" }, "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 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": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xfE8NMqOurKo", "outputId": "191730ba-d5e2-4df7-97d2-99feb0b704af" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " title sentiment_label \\\n", "0 A Light in the Attic neutral \n", "1 A Light in the Attic neutral \n", "2 A Light in the Attic neutral \n", "3 A Light in the Attic neutral \n", "4 A Light in the Attic neutral \n", "\n", " review_text rating popularity_score \n", "0 Had potential that went unrealized. Three 3 \n", "1 The themes were solid, but not well explored. Three 3 \n", "2 It simply lacked that emotional punch. Three 3 \n", "3 Serviceable but not something I'd go out of my... Three 3 \n", "4 Standard fare with some promise. Three 3 \n" ] } ], "source": [ "print(df_reviews.head())" ] } ], "metadata": { "colab": { "collapsed_sections": [ "jpASMyIQMaAq", "lquNYCbfL9IM", "0IWuNpxxYDJF", "oCdTsin2Yfp3", "T0TOeRC4Yrnn", "duI5dv3CZYvF", "qMjRKMBQZlJi", "p-1Pr2szaqLk", "SIaJUGIpaH4V", "pY4yCoIuaQqp", "n4-TaNTFgPak", "HnngRNTgacYt", "HF9F9HIzgT7Z", "T8AdKkmASq9a", "OhXbdGD5fH0c", "L2ak1HlcgoTe", "4IXZKcCSgxnq", "EhIjz9WohAmZ", "Gi4y9M9KuDWx", "fQhfVaDmuULT", "bmJMXF-Bukdm", "RYvGyVfXuo54" ], "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }