This project analyzes product trends from Amazon's Best Sellers (amazon.com/Best-Sellers) to identify sales patterns, top-performing categories/brands, and customer preferences. The analysis reveals actionable insights for sellers, marketers, and e-commerce professionals.

Total Data Scraped: 600 rows × 14 columns
After Handling Missing Values: 597 rows × 14 columns

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• As the number of reviews increases, the sales of a product tend to increase significantly.

• Products with the highest number of reviews tend to rank closer to #1 in the seller rankings.

• Parent Category: Clothing, Shoes & Jewelry
• Subcategory: Men's Mules & Clogs
  (Products in these categories with more reviews are purchased more frequently.)

• The top brand is Ring, followed by others identified in the analysis.

• The leading product is Crocs Unisex Adult Classic Clog.

• Products released earlier (e.g., 2004) show lower sales in recent months, while newer products (e.g., 2024 releases) demonstrate higher sales growth.
• Based on this trend, products released in 2025 are expected to achieve the highest sales.

• A geographical analysis highlights the number of distinct brands across countries.
• For instance, China has 126 distinct brands, the highest among all countries.

• Scraped 600 products using Python (BeautifulSoup/Selenium)
• Raw dataset: 600 rows × 14 columns
• Cleaned dataset: 597 rows × 14 columns (Data Preparation Notebook)

• Python > pandas, selenium, BeautifulSoup
• Tableau
• Jupyter Notebook

To replicate or extend this analysis, follow the steps below:

Ensure Python is installed on your machine.

1. Clone the Repository

   git clone https://github.com/mominurr/Amazon-Best-Sellers-Data-Analysis.git

2. Create a Virtual Environment

   python -m venv myvenv

3. Install Dependencies

   pip install -r requirements.txt
   

4. Run the Scraper Script

   Execute the script to scrape data from Amazon.

   python scraper.py

• The scraped data will be saved as data/raw_data.csv.

Open and run the data_preparation.ipynb notebook to handle missing and duplicate values.

• The cleaned data will be saved as data/cleaned_data.csv.

This project is licensed under the MIT License – see the LICENSE file for details.

Contributions are welcome! Feel free to fork the repo and submit a pull request.

For any inquiries or collaborations:

• Portfolio: mominur.dev
• GitHub: github.com/mominurr
• LinkedIn: linkedin.com/in/mominur--rahman
• Email: [email protected]

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