{ "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": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f48c8f8c", "outputId": "9369faf8-ddb9-4269-aba0-a3c77abbcf94", "collapsed": true }, "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": 9, "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": 10, "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": 15, "metadata": { "id": "l5FkkNhUYTHh", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "collapsed": true, "outputId": "c7dffd5c-1b18-4bfe-cc7c-ba8b323e66c9" }, "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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titlepricerating
0A Light in the Attic51.77Three
1Tipping the Velvet53.74One
2Soumission50.10One
3Sharp Objects47.82Four
4Sapiens: A Brief History of Humankind54.23Five
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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": 15 } ], "source": [ "import pandas as pd\n", "\n", "df_books = pd.DataFrame({\n", " \"title\": titles,\n", " \"price\": prices,\n", " \"rating\": ratings\n", "})\n", "\n", "df_books.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "duI5dv3CZYvF" }, "source": [ "### *d. Save web-scraped dataframe either as a CSV or Excel file*" ] }, { "cell_type": "code", "execution_count": 16, "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": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "O_wIvTxYZqCK", "outputId": "b4b2d1e0-0412-42c8-8ee0-88ee14b634bd" }, "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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titlepricerating
0A Light in the Attic51.77Three
1Tipping the Velvet53.74One
2Soumission50.10One
3Sharp Objects47.82Four
4Sapiens: A Brief History of Humankind54.23Five
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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": 17 } ], "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": 18, "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": 19, "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": 23, "metadata": { "id": "V-G3OCUCgR07", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "5ef8bd1e-b956-4a7f-d4b7-ed20ec4d9b90" }, "outputs": [ { "output_type": "execute_result", "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 3\n", "3 Sharp Objects 47.82 Four 4\n", "4 Sapiens: A Brief History of Humankind 54.23 Five 4" ], "text/html": [ "\n", "
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titlepriceratingpopularity_score
0A Light in the Attic51.77Three3
1Tipping the Velvet53.74One2
2Soumission50.10One3
3Sharp Objects47.82Four4
4Sapiens: A Brief History of Humankind54.23Five4
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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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 1,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 23 } ], "source": [ "df_books[\"popularity_score\"] = df_books[\"rating\"].apply(generate_popularity_score)\n", "df_books.head()" ] }, { "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": 25, "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", "source": [ "df_books[\"sentiment_label\"] = df_books[\"popularity_score\"].apply(get_sentiment)\n", "df_books.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "cWbieOzIn14l", "outputId": "c16b0c24-f10a-419c-f337-806a8136e5d9" }, "execution_count": 26, "outputs": [ { "output_type": "execute_result", "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 3 \n", "3 Sharp Objects 47.82 Four 4 \n", "4 Sapiens: A Brief History of Humankind 54.23 Five 4 \n", "\n", " sentiment_label \n", "0 neutral \n", "1 negative \n", "2 neutral \n", "3 positive \n", "4 positive " ], "text/html": [ "\n", "
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titlepriceratingpopularity_scoresentiment_label
0A Light in the Attic51.77Three3neutral
1Tipping the Velvet53.74One2negative
2Soumission50.10One3neutral
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4Sapiens: A Brief History of Humankind54.23Five4positive
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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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 1,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 26 } ] }, { "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": 27, "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": 28, "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": 29, "metadata": { "id": "wcN6gtiZg-ws", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "4053f37e-ae9a-47bc-9138-bd7de4888119" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title month units_sold sentiment_label\n", "0 A Light in the Attic 2024-09 130 neutral\n", "1 A Light in the Attic 2024-10 139 neutral\n", "2 A Light in the Attic 2024-11 132 neutral\n", "3 A Light in the Attic 2024-12 137 neutral\n", "4 A Light in the Attic 2025-01 138 neutral" ], "text/html": [ "\n", "
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titlemonthunits_soldsentiment_label
0A Light in the Attic2024-09130neutral
1A Light in the Attic2024-10139neutral
2A Light in the Attic2024-11132neutral
3A Light in the Attic2024-12137neutral
4A Light in the Attic2025-01138neutral
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_sales", "summary": "{\n \"name\": \"df_sales\",\n \"rows\": 18000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\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\": \"month\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 18,\n \"samples\": [\n \"2024-09\",\n \"2024-10\",\n \"2025-05\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"units_sold\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 101,\n \"min\": 0,\n \"max\": 374,\n \"num_unique_values\": 355,\n \"samples\": [\n 155,\n 264,\n 65\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 29 } ], "source": [ "df_sales = pd.DataFrame(sales_data)\n", "df_sales.head()" ] }, { "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": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MzbZvLcAhGaH", "outputId": "54e73bf1-06ab-49eb-c7bd-ce49e2757807" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " title month units_sold sentiment_label\n", "0 A Light in the Attic 2024-09 130 neutral\n", "1 A Light in the Attic 2024-10 139 neutral\n", "2 A Light in the Attic 2024-11 132 neutral\n", "3 A Light in the Attic 2024-12 137 neutral\n", "4 A Light in the Attic 2025-01 138 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": 37, "metadata": { "id": "b3cd2a50" }, "outputs": [], "source": [ "synthetic_reviews_by_sentiment = {\n", " \"positive\": [\n", " \"This book was an absolute joy to read from beginning to end.\",\n", " \"I loved the characters and the storytelling style.\",\n", " \"An engaging and beautifully written story.\",\n", " \"The plot was compelling and kept me hooked throughout.\",\n", " \"A fantastic read that exceeded my expectations.\",\n", " \"The author did a wonderful job bringing the story to life.\",\n", " \"I couldn’t put this book down once I started it.\",\n", " \"The pacing and character development were excellent.\",\n", " \"A thoughtful and inspiring book.\",\n", " \"One of the best novels I’ve read this year.\",\n", " \"The writing style was captivating and easy to follow.\",\n", " \"A memorable story with strong emotional impact.\",\n", " \"The themes were handled with depth and care.\",\n", " \"I highly recommend this book to anyone who enjoys fiction.\",\n", " \"A well-crafted story that left me satisfied.\",\n", " \"The author’s creativity really shines in this book.\",\n", " \"An immersive world and compelling narrative.\",\n", " \"The ending was satisfying and well thought out.\",\n", " \"I found the characters relatable and interesting.\",\n", " \"A delightful and engaging read.\",\n", " \"The book balanced humor and drama very well.\",\n", " \"A creative and original storyline.\",\n", " \"The dialogue felt natural and authentic.\",\n", " \"A powerful story that stayed with me after finishing.\",\n", " \"The writing was polished and enjoyable.\",\n", " \"A refreshing take on a familiar genre.\",\n", " \"The book delivered a very rewarding reading experience.\",\n", " \"The story flowed smoothly from start to finish.\",\n", " \"A beautifully structured narrative.\",\n", " \"This book kept me interested the whole time.\",\n", " \"A very satisfying and entertaining read.\",\n", " \"The author built tension very effectively.\",\n", " \"The emotional moments were very impactful.\",\n", " \"The plot twists were clever and surprising.\",\n", " \"I appreciated the depth of the characters.\",\n", " \"The atmosphere of the book was wonderfully crafted.\",\n", " \"A highly enjoyable and well-written novel.\",\n", " \"The book had a strong and engaging voice.\",\n", " \"A captivating story with memorable scenes.\",\n", " \"The pacing was perfect for the story.\",\n", " \"A great blend of storytelling and character growth.\",\n", " \"The book was both entertaining and meaningful.\",\n", " \"A very polished and thoughtful piece of writing.\",\n", " \"I finished the book feeling very satisfied.\",\n", " \"The narrative pulled me in immediately.\",\n", " \"The story was imaginative and engaging.\",\n", " \"A compelling and rewarding reading experience.\",\n", " \"The author’s style made the book easy to enjoy.\",\n", " \"An uplifting and enjoyable novel.\",\n", " \"I would gladly read more from this author.\"\n", " ],\n", "\n", " \"neutral\": [\n", " \"The book was okay overall.\",\n", " \"It was a fairly average reading experience.\",\n", " \"Some parts were interesting while others were slow.\",\n", " \"The story had its moments but also some dull sections.\",\n", " \"The characters were decent but not particularly memorable.\",\n", " \"I found the book to be moderately engaging.\",\n", " \"The plot was straightforward and easy to follow.\",\n", " \"It was neither particularly exciting nor boring.\",\n", " \"The writing style was simple and clear.\",\n", " \"The book was fine for casual reading.\",\n", " \"I didn’t feel strongly about the story either way.\",\n", " \"Some chapters worked better than others.\",\n", " \"The pacing felt uneven at times.\",\n", " \"The overall story was acceptable but not remarkable.\",\n", " \"It was a decent book to pass the time.\",\n", " \"The characters were serviceable but lacked depth.\",\n", " \"The book delivered what it promised.\",\n", " \"I thought the ending was predictable.\",\n", " \"The themes were present but not deeply explored.\",\n", " \"It was an average book in this genre.\",\n", " \"The narrative was easy to understand.\",\n", " \"I neither loved nor disliked the book.\",\n", " \"The writing was competent but not exceptional.\",\n", " \"Some sections felt longer than necessary.\",\n", " \"The story was mildly interesting.\",\n", " \"I found it somewhat engaging at times.\",\n", " \"The book had a few good moments.\",\n", " \"Overall it was a standard reading experience.\",\n", " \"Nothing about the book particularly stood out.\",\n", " \"The story progressed in a predictable way.\",\n", " \"The characters were adequate for the story.\",\n", " \"The book was readable but not especially exciting.\",\n", " \"It was a fairly typical example of the genre.\",\n", " \"The plot structure felt familiar.\",\n", " \"The pacing was steady but not gripping.\",\n", " \"The book met my basic expectations.\",\n", " \"There were both enjoyable and forgettable moments.\",\n", " \"The story had a clear structure.\",\n", " \"It was a neutral reading experience overall.\",\n", " \"The writing style was straightforward.\",\n", " \"I didn’t find it particularly memorable.\",\n", " \"The ideas were presented clearly enough.\",\n", " \"The story moved along at a moderate pace.\",\n", " \"The book felt somewhat formulaic.\",\n", " \"It was easy enough to finish.\",\n", " \"The narrative was simple and direct.\",\n", " \"The book had some interesting concepts.\",\n", " \"The overall execution was average.\",\n", " \"I felt indifferent after finishing it.\",\n", " \"It was neither impressive nor disappointing.\"\n", " ],\n", "\n", " \"negative\": [\n", " \"I struggled to stay interested in this book.\",\n", " \"The story felt dull and unoriginal.\",\n", " \"I found the pacing painfully slow.\",\n", " \"The characters were flat and uninteresting.\",\n", " \"This book did not meet my expectations at all.\",\n", " \"I had trouble finishing the book.\",\n", " \"The plot felt confusing and poorly structured.\",\n", " \"The writing style didn’t appeal to me.\",\n", " \"The story lacked excitement and tension.\",\n", " \"I found many parts of the book boring.\",\n", " \"The characters felt underdeveloped.\",\n", " \"The dialogue often sounded unnatural.\",\n", " \"The book dragged on for too long.\",\n", " \"I didn’t enjoy the author’s storytelling style.\",\n", " \"The ending was disappointing.\",\n", " \"The plot felt predictable and uninspired.\",\n", " \"The book lacked emotional impact.\",\n", " \"I couldn’t connect with any of the characters.\",\n", " \"The pacing made the story difficult to enjoy.\",\n", " \"I felt the book needed stronger editing.\",\n", " \"The narrative felt scattered and unfocused.\",\n", " \"The story failed to hold my attention.\",\n", " \"The book started slow and never improved.\",\n", " \"I was disappointed by the lack of depth.\",\n", " \"The writing felt repetitive.\",\n", " \"The story never fully came together.\",\n", " \"The characters’ decisions felt unrealistic.\",\n", " \"I found the plot twists unconvincing.\",\n", " \"The book felt longer than it needed to be.\",\n", " \"The themes were poorly developed.\",\n", " \"The story lacked originality.\",\n", " \"The writing style made the book difficult to read.\",\n", " \"The pacing ruined the flow of the story.\",\n", " \"The book didn’t deliver on its premise.\",\n", " \"The narrative felt disorganized.\",\n", " \"I lost interest halfway through.\",\n", " \"The characters felt forgettable.\",\n", " \"The book failed to build meaningful tension.\",\n", " \"The plot had too many weak moments.\",\n", " \"The writing felt bland and uninspired.\",\n", " \"I expected much more from this book.\",\n", " \"The story lacked a clear direction.\",\n", " \"The emotional moments felt forced.\",\n", " \"The book was frustrating to read.\",\n", " \"The author’s style didn’t work for me.\",\n", " \"The story never became engaging.\",\n", " \"The plot development felt weak.\",\n", " \"The book felt tedious overall.\",\n", " \"The ending left me unsatisfied.\",\n", " \"Overall, this was a disappointing read.\"\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": 32, "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": 33, "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": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3946e521", "outputId": "d2f4dc59-f06f-4532-a6e2-177661000d14" }, "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": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "xfE8NMqOurKo", "outputId": "22f634b9-4044-4091-9cdb-559c4e1751c5" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " 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 I found the book to be moderately engaging. Three 3 \n", "1 The pacing was steady but not gripping. Three 3 \n", "2 It was a decent book to pass the time. Three 3 \n", "3 Some sections felt longer than necessary. Three 3 \n", "4 The book was okay overall. Three 3 " ], "text/html": [ "\n", "
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titlesentiment_labelreview_textratingpopularity_score
0A Light in the AtticneutralI found the book to be moderately engaging.Three3
1A Light in the AtticneutralThe pacing was steady but not gripping.Three3
2A Light in the AtticneutralIt was a decent book to pass the time.Three3
3A Light in the AtticneutralSome sections felt longer than necessary.Three3
4A Light in the AtticneutralThe book was okay overall.Three3
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_reviews", "summary": "{\n \"name\": \"df_reviews\",\n \"rows\": 10000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\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\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"review_text\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 150,\n \"samples\": [\n \"This book did not meet my expectations at all.\",\n \"I had trouble finishing the book.\",\n \"I found many parts of the book boring.\"\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 \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 1,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 36 } ], "source": [ "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 }