| Project Title | Analyzing Amazon Sales data |
|---|---|
| Technologies | Data Science |
| Domain | E-commerce |
| Project Difficulty Level | Advanced |
Sales management has gained importance to meet increasing competition and the need for improved methods of distribution to reduce cost and to increase profits. Sales management today is the most important function in a commercial and business enterprise.
Do ETL: Extract-Transform-Load some Amazon dataset and find for me Sales-trend -> month-wise, year-wise, yearly_month-wise
Find key metrics and factors and show the meaningful relationships between attributes. Do your own research and come up with your findings.
You can find the dataset on the given link
Download Data
Python, Tableau, Power BI or you can use any tools and techniques as per your convenience. We would appreciate your valid imagination in finding solutions.
● You are supposed to write code in a modular fashion
● Safe: It can be used without causing harm.
● Testable: It can be tested at the code level.
● Maintainable: It can be maintained, even as your codebase grows.
● Portable: It works the same in every environment (operating system)
For Tableau : You will have to share the Tableau Public Link of your work. For Python : You have to submit your code PDF file at the final submission.
You have to create a detailed project report and submit that document as per the given
sample.
Demo link
Sample Project Report
We welcome contributions from anyone interested in analyzing Amazon sales data! Whether you're a seasoned data scientist, an e-commerce enthusiast, or someone eager to learn and contribute, there are several ways you can get involved:
- Data Analysis: Dive into our Amazon sales dataset, extract meaningful insights, and uncover trends in sales data. Analyze sales trends month-wise, year-wise, and yearly month-wise to identify patterns and fluctuations. Share your findings and suggestions for further analysis.
- ETL (Extract-Transform-Load): Help with the Extract-Transform-Load process to preprocess and clean the Amazon dataset. Ensure data quality and consistency to facilitate accurate analysis and interpretation of sales trends. Implement efficient ETL pipelines and data transformation techniques to optimize data processing.
- Visualization: Create visualizations using tools like Python, Tableau, or Power BI to represent sales trends and key metrics effectively. Visualize month-wise, year-wise, and yearly month-wise sales data to communicate insights clearly and intuitively. Your visualizations can help stakeholders understand the significance of sales trends and make informed decisions.
- Statistical Analysis: Conduct statistical analysis to identify meaningful relationships between attributes in the Amazon sales dataset. Explore correlations, trends, and dependencies between sales metrics, product categories, customer demographics, and other relevant factors. Your analysis can uncover valuable insights into factors influencing sales performance and profitability.
- Documentation: Contribute to documenting the analysis process, methodology, and findings to ensure transparency and reproducibility. Update README files, provide detailed explanations of data analysis techniques, and document key insights for reference. Clear and well-organized documentation helps users understand the analysis and replicate the results.
- Feedback and Suggestions: Provide feedback on existing analyses, suggest improvements, or propose new ideas for analysis. Share your thoughts on data processing techniques, visualization strategies, or potential areas for further exploration. Your input helps enhance the quality and relevance of our analysis and drives continuous improvement.
To get started, simply follow these steps:
- Clone the Repository: Clone the project repository to your local machine using Git. Navigate to the directory where you want to clone the repository and run the following command:
git clone https://github.com/Alexcj10/analyzing-amazon-sales-data.git - Explore the Project: Once you've cloned the repository, explore the project files, datasets, and documentation. Familiarize yourself with the project structure and objectives to understand how you can contribute effectively.
- Choose a Contribution: Decide how you'd like to contribute based on your skills and interests. Whether you're analyzing data, visualizing trends, documenting findings, or providing feedback, there's a role for you in our project.
- Make Your Contribution: Implement your chosen contribution by following the guidelines provided in the project's README file. Write code, conduct analysis, create visualizations, or document your findings as needed. Ensure that your contributions align with the project's goals and objectives.
- Submit Your Contribution: Once your contribution is ready, submit it for review by creating a pull request. Provide a clear description of your changes, including any relevant context or explanations. Collaborate with other contributors and address feedback to refine your contribution.
If you have any questions, need assistance, or want to discuss potential contributions, feel free to reach out by email at alexchandarjoshva@gmail.com. We're here to support you throughout the contribution process and look forward to collaborating with you!
If you're interested in contributing but prefer to work on a specific aspect or feature of the project, don't hesitate to reach out. We can discuss potential tasks or areas where your expertise could be valuable. Additionally, if you have ideas for new analyses, visualizations, or features that you'd like to explore, feel free to share them with us. Your input guides the direction of our project, and we're always open to new ideas and collaborations.
We're excited to have you join us in analyzing Amazon sales data and uncovering valuable insights to drive business decisions and improve sales performance!