📊 Amazon Best Selling Books Analysis (with Pandas)

📌 Project Overview

This project analyzes the Amazon Best Selling Books dataset using Python (Pandas & NumPy).
The goal was to explore, clean, and analyze spreadsheet data to identify trends in bestselling books over the years.

It’s a beginner-friendly project that shows how to handle real-world CSV data with Python.

---

✨ Features

• ✔ Load and explore dataset with Pandas
• ✔ Data cleaning (remove duplicates, handle missing values)
• ✔ Basic exploratory data analysis (EDA)
• ✔ Sorting & filtering by ratings, reviews, and prices
• ✔ Aggregating data (e.g., most popular authors, categories)
• ✔ Exporting cleaned results back to CSV

---

🛠 Tech Stack

Language: Python 3
Libraries: Pandas, NumPy

---

🚀 How to Run

Clone this repository:

git clone https://github.com/azxigen/amazon-books-analysis.git  
cd amazon-books-analysis

---

📂 Dataset

Amazon Bestselling Books (2009–2019) Contains details like:

• Book Title
• Author
• Genre (Fiction / Non-Fiction)
• User Rating
• Reviews
• Price

---

🔍 Sample Insights

• Top authors with multiple bestselling books
• Price trends across years
• Most common categories in bestsellers

Note: Since no Matplotlib was used, results are shown as Pandas tables & CSV exports.

---

🔮 Future Improvements

• 📊 Add visualizations (Matplotlib / Seaborn)
• 🖥 Build interactive dashboard with Streamlit
• 📈 Compare trends with more recent Amazon data

---

✅ Conclusion

This project demonstrates how spreadsheet-style data can be analyzed using Python and Pandas.
It provides a strong foundation for data analysis, which can be extended with visualizations and machine learning.