📚 Amazon Bestselling Books Analysis 📌 Repository Overview This repository contains an analysis of the Amazon Bestselling Books Dataset, exploring trends in book sales, pricing, customer ratings, and other key factors. The project aims to provide insights into what makes a book a bestseller by leveraging data visualization and statistical analysis.
📂 Repository Structure
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📦 Amazon-Bestselling-Books-Analysis
│── 📄 README.md # Project Documentation
│── 📂 data # Dataset Storage
│ ├── amazon_bestselling_books.csv # Dataset File
│── 📂 notebooks
│ ├── amazon_bestselling_books.ipynb # Jupyter Notebook with full analysis
│── 📂 reports # Generated Reports & Visualizations
│── 📂 scripts # Python scripts for analysis
│── 📂 results # Output files (graphs, insights, etc.)
📊 Dataset Description
The dataset contains information on Amazon’s best-selling books over the years, including title, author, genre, price, user ratings, reviews, and publication year. The analysis focuses on identifying patterns in book sales and understanding factors that contribute to a book's success.
Dataset Features: Feature Name Description Name Title of the book Author Author's name User Rating Average customer rating (out of 5) Reviews Number of user reviews Price Price of the book in USD Year Year the book was a bestseller Genre Fiction or Non-Fiction 📌 Project Goals & Insights ✔ Analyze trends in bestselling books over different years. ✔ Identify the most successful authors based on book count and ratings. ✔ Understand pricing strategies and their impact on book sales. ✔ Compare fiction vs. non-fiction books' performance.
📈 Key Findings 📌 Non-Fiction dominates the bestseller list 📖 📌 Books with high ratings tend to receive more reviews ⭐ 📌 Pricing varies significantly based on genre 💰 📌 Certain authors consistently appear in the bestseller list 🎯
🛠 Installation & Setup
1️⃣ Clone the repository:
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git clone https://github.com/your-repo/amazon-bestselling-books-analysis.git
cd amazon-bestselling-books-analysis
2️⃣ Install dependencies:
bash Copy Edit pip install -r requirements.txt
3️⃣ Run the Jupyter Notebook:
bash Copy Edit jupyter notebook Then open amazon_bestselling_books.ipynb in the notebook interface.
📌 Technologies Used Programming Language: Python Libraries: Pandas, NumPy, Matplotlib, Seaborn 📜 References Dataset Source: Kaggle / Amazon Bestselling Books Dataset Data Science & Visualization Concepts Applied