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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4ba6aba8" | |
| }, | |
| "source": [ | |
| "# 🤖 **Data Collection, Creation, Storage, and Processing**\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "jpASMyIQMaAq" | |
| }, | |
| "source": [ | |
| "## **1.** 📦 Install required packages" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "f48c8f8c", | |
| "outputId": "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", | |
| " <div id=\"df-04c87660-4415-45e9-ad3b-3fa19d9402c2\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
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| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>title</th>\n", | |
| " <th>price</th>\n", | |
| " <th>rating</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>A Light in the Attic</td>\n", | |
| " <td>51.77</td>\n", | |
| " <td>Three</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Tipping the Velvet</td>\n", | |
| " <td>53.74</td>\n", | |
| " <td>One</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Soumission</td>\n", | |
| " <td>50.10</td>\n", | |
| " <td>One</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Sharp Objects</td>\n", | |
| " <td>47.82</td>\n", | |
| " <td>Four</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Sapiens: A Brief History of Humankind</td>\n", | |
| " <td>54.23</td>\n", | |
| " <td>Five</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04c87660-4415-45e9-ad3b-3fa19d9402c2')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
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| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-04c87660-4415-45e9-ad3b-3fa19d9402c2 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-04c87660-4415-45e9-ad3b-3fa19d9402c2');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
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| "\n", | |
| "\n", | |
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| " </div>\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", | |
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| "provenance": [] | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |