Data Analysis of Cleaned Book Data
Introduction
This project aims to analyze the cleaned book data scraped from the Books to Scrape website. The dataset includes various attributes of books such as titles, prices, ratings, and genres. By conducting a thorough analysis, we aim to derive insights regarding pricing trends and rating distributions among the books available on the platform.
Objectives
- To visualize the distribution of book prices and identify the most expensive books.
- To explore the distribution of book ratings and understand customer preferences.
- To analyze the relationship between book prices and their ratings.
Dataset
The dataset used in this project is the cleaned book data obtained from Books to Scrape. The key features of the dataset include:
- Book Title: The title of the book.
- Price: The price of the book.
- Rating: The rating given to the book by users.
- Genre: The category under which the book falls.
Tools and Libraries
- Python: The primary programming language used for analysis.
- Pandas: For data manipulation and analysis.
- Matplotlib: For data visualization.
- Seaborn: For enhanced data visualization capabilities with aesthetically pleasing graphics.
Methodology
- Data Loading: The cleaned dataset is loaded into a Pandas DataFrame for analysis.
- Data Exploration: The first few rows of the dataset are displayed to understand its structure and contents.
- Visualization:
- A bar plot is created to display the top 10 most expensive books.
- A count plot is generated to visualize the distribution of book ratings.
- A box plot is constructed to illustrate the price distribution across different rating categories.
Expected Outcomes
- Identify the most expensive books on the platform.
- Understand the rating distribution among the books.
- Analyze how book prices vary with different rating levels.
Conclusion
This data analysis project aims to provide valuable insights into book pricing and ratings, enabling stakeholders to make informed decisions regarding their selections. The visualizations will aid in comprehending the trends within the dataset effectively.