Amazon Best Selling Books Analysis with Pandas

This project analyzes Amazon's best-selling books dataset using Python and Pandas to uncover trends, patterns, and insights about popular books, authors, ratings, and genres.

Project Overview

The analysis includes:

• Data cleaning and preprocessing
• Exploring best-selling authors
• Analyzing ratings and reviews
• Genre distribution
• Year-wise trends
• Most popular books on Amazon

The project demonstrates practical data analysis skills using Pandas and data visualization libraries.

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Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

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Dataset

The dataset contains information about Amazon best-selling books, including:

• Book Title
• Author
• User Rating
• Reviews
• Price
• Year
• Genre

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Project Structure

amazon-bestsellers-pandas/
│
├── data/
│   └── amazon_books.csv
│
├── notebooks/
│   └── analysis.ipynb
│
├── images/
│   └── charts/
│
├── README.md
└── requirements.txt

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Installation

Clone the repository:

git clone https://github.com/fibonacciFiasco/amazon-bestsellers-pandas.git

Move into the project directory:

cd amazon-bestsellers-pandas

Install dependencies:

pip install -r requirements.txt

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Usage

Run the Jupyter Notebook:

jupyter notebook

Open:

analysis.ipynb

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Key Insights

• Most frequent best-selling authors
• Average ratings across genres
• Relationship between reviews and popularity
• Yearly best-selling trends
• Fiction vs Non-Fiction comparisons

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Sample Visualizations

• Ratings distribution
• Top authors by number of best sellers
• Genre comparison charts
• Review trends over the years

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Future Improvements

• Interactive dashboard using Streamlit
• Machine learning predictions
• Advanced visual analytics
• Web deployment

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Author

Created by Diya Patel

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License

This project is for educational and learning purposes.