Amazon-Products-Sales-Dataset-2023

Overview

In this project, I conducted a comprehensive analysis of Amazon product data, focusing specifically on baby products, using Python's Pandas library and various data visualization tools. The analysis aimed to uncover key insights, including identifying top-selling products, discount prices, ratings, and profitability. Through detailed exploration and visualization, I highlighted trends in product performance, profitability, and customer preferences. This analysis provides actionable insights to guide data-driven strategies for optimizing product promotions, pricing, and inventory management.

Questions

• What are the top-selling products that have the highest sales?

• Which products generate the most profit?

• Are there specific products with unusually high or low ratings?

• What is the correlation between product profit and discount percentage?

Data source

• The Amazon Products Sales Dataset 2023 was taken from Kaggle : (https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset/data?select=Baby+Products.csv)

Data Processing and Transformation

• Data cleaning:

• Handling missing values by either imputing them with suitable replacements (e.g., mean, median...) or dropping rows/columns where data was insufficient.
• Ensuring correct data types for all columns (e.g., converting discount_price to float format and numeric columns to appropriate numerical types).

• Featur selection:

• Selected key columns for analysis, including product, main_category, sub_category, ratings, discount_price, actual_price, discount_percentage and profit, while dropping irrelevant columns like image, link, no_of_ratings.

• Creating new features like discount_percentage and profit to provide more detailed insights into the impact of discount_percentage on sales and profit.

• Data Transformation:

• Grouping Numerical Columns: Grouping numerical columns like Age and Previous Purchases into meaningful bins (e.g., age groups like "18-25"...) to make the data easier to analyze and interpret.

• Incorporating Profit and Discount Metrics: understanding the correlation between discount_percentage and profit and determining the impact of discounts on sales profitability.

• Converting Data Types: Ensuring columns are converted to appropriate data types.

• Aggregation:

• Summarizing sales data by discount_price, discont_percentage, profit... to understand performance trends.

Best practices and how to Replicate and run the project

Prerequisites for Replicating the Analysis

1. Git:

• Git is required to clone the repository to your local machine. You can follow the installation instructions available at this website here

2. Visual Studio Code (VSC) The analysis was conducted using Jupyter Notebook within Visual Studio Code (VSC). To get started with VSC:

• Download and install Visual Studio Code from here
• Once installed, open VSC and ensure the Jupyter extension is installed. You can search for the Jupyter extension in the Extensions view (Ctrl+Shift+X) and install it.

3. Jupyter Notebook After installing VSC and the Jupyter extension, you need to install Jupyter Notebook itself. Follow these steps:

• Open a terminal in VSC and install Jupyter by running the command: pip install notebook
• After installation, you should be able to run Jupyter notebooks inside VSC. For more detailed instructions, refer to the official documentation here

4. CSV File:

• Download the required CSV file and place it in the project folder on your local machine. This file is necessary for running the analysis.

Getting started to Replicate and Run the Project

1. Clone the Repository:

• Clone the project repository to your local machine by using the following URL: https://github.com/JamilaAr/Amazon-Products-Sales-Dataset-2023
• Run the following command in your terminal: git clone https://github.com/JamilaAr/Amazon-Products-Sales-Dataset-2023

2. Create and Activate a Virtual Environment:

• Set up a virtual environment and install the required packages from requirements.txt:

• For Linux/Mac:

• python3 -m venv venv
• source venv/bin/activate
• pip install -r requirements.txt

• For Git Bash(Windows):

• python -m venv venv
• source venv/Scripts/activate
• pip install -r requirements.txt

3. Deactivate the Virtual Environment (After Use):

deactivate

4. Ensure that the CSV file is placed in the main project directory.

5. Run the .ipynb file: Open and execute the following file for analysis:

• Amazon_Baby_Products.ipynb

I will be presenting a Tableau dashboard to analyze Baby Products. This interactive visualization provides clear and dynamic insights into sales patterns and profitability.

• Best products by actual price.

• Top-selling baby products by ratings [Click on the link] (https://public.tableau.com/app/profile/jamila.arabat/viz/AnalyzeAmazonbabyproductsperformance/Dashboard1)

Conclusion

The Amazon Products Sales project delivered valuable insights into the sales performance of baby products. Key findings included identifying top-selling and highly profitable products by analyzing discount percentages, ratings, and overall sales trends. Additionally, the analysis revealed a moderate positive correlation between discount_percentage and profit, indicating that as discounts increase, profitability also tends to improve. These insights can help refine promotional strategies and enhance decision-making for future product offerings.