Bookscrape-trends

A data pipeline and dashboard project that scrapes book metadata from Goodreads' Best Books of 2025 list, cleans and transforms the results, and explores book popularity and reader-interest trends in Power BI.

Overview

This project collects book-level data such as title, author, publication date, genres, page count, language, ratings, and reader-interest metrics. The cleaned dataset is then used for exploratory analysis and dashboarding.

The goal is to answer questions like:

• Which books are generating the most reader interest?
• How do ratings relate to popularity?
• Which genres appear most often?
• Do page length or publication timing seem related to engagement?

Dataset

The dataset includes fields such as:

• book_title
• publication_date
• author
• genres
• num_pages
• rank
• language
• curr_readers
• want_read
• avg_rating
• num_ratings

Workflow

1. Scrape book data from Goodreads using Selenium
2. Store the raw results
3. Clean and standardize fields in Python
4. Prepare the dataset for analysis
5. Build a Power BI dashboard for trend exploration

Tech Stack

• Python
• pandas
• NumPy
• Selenium
• Jupyter Notebook
• Power BI

Project Structure

bookscrape-trends/
├── data/               # raw and/or cleaned data files
├── processing.ipynb    # scraping, cleaning, and transformation workflow
├── utils.py            # helper functions
├── sample.env          # sample environment variables
├── requirements.txt
├── README.md
└── .gitignore

Dashboard