{ "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", "
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\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", "
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titlepriceratingpopularity_scoresentiment_label
0A Light in the Attic51.77Three3neutral
1Tipping the Velvet53.74One2negative
2Soumission50.10One2negative
3Sharp Objects47.82Four4positive
4Sapiens: A Brief History of Humankind54.23Five3neutral
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\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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 2,\n \"max\": 4,\n \"num_unique_values\": 3,\n \"samples\": [\n 3,\n 2,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ] } ], "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 }