📊 Amazon Products Data Analysis

🔎 Overview

This project provides a comprehensive analysis of Amazon product data using Python and Jupyter Notebook. The analysis covers:

• 📥 Data loading and cleaning
• 📈 Summary statistics
• 📊 Visualizations of product categories, prices, availability, and ratings

All visualizations are generated directly from the notebook and provide actionable insights into the dataset.

🖼️ Example Visualizations

Below are sample images generated by the notebook:

🏷️ Top 10 Product Categories

💲 Initial Price Distribution

📦 Product Availability

⭐ Rating Distribution

To view all visualizations, open and run amazon-products-analysis.ipynb in Jupyter.

⚙️ Easily Customizable Analysis

This notebook is designed to be easily edited and extended. You can quickly adapt the code to analyze other columns, add new visualizations, or focus on different aspects of the dataset. Whether you want to explore reviews, seller information, or any other field, Bright Data's structured format makes it simple to unlock new insights.

📊 Data-Driven Decisions with Bright Data

This is just a simple example of how to make data-driven decisions using Bright Data's structured, accurate data. With Bright Data, you get high-quality, ready-to-use datasets for Amazon and many other sources, making your data projects seamless and efficient.

• Explore Bright Data's Amazon Datasets

📂 Dataset Source

This dataset is provided by Bright Data, the perfect choice for structured data that is easy to work with and analyze. Bright Data offers high-quality, ready-to-use datasets for Amazon and many other sources, making your data projects seamless and efficient.

🚀 How to Run

1. Clone this repository:
   ```sh git clone https://github.com/Danihashko/amazon-analysis.git cd amazon-analysis ```

2. Install Python dependencies:
   ```sh pip install pandas matplotlib seaborn ```

3. Launch Jupyter Notebook:
   ```sh jupyter notebook amazon-products-analysis.ipynb ```

4. Run all cells to reproduce the analysis and visualizations.

📁 Repository Contents

• amazon-products-analysis.ipynb — Main analysis notebook
• amazon-products.csv — Amazon products dataset (sample)
• images/ — Folder for generated charts (add your own after running the notebook)
• README.md — Project documentation

📜 License

This project is released under the MIT License.

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