Project

Black Friday Sale Analysis

This is Final Capstone Project for ALY6000 22821 Introduction to Data Analytics Winter 2021 CPS.
It is primarily to introduce and learn Data Analytics and Descriptive using R.
This paper summarizes our preliminary (Exploratory Data Analysis EDA) analysis on the selected dataset, the Black friday Dataset from Amazon for this group project. It shared some insightful results from the EDA and descriptive statistics. Further, this paper identifies a set of analysis used to answer the business questions on the dataset and justifies those findings.
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Direct Questions that can be framed are

Based on the data- we can create data that are understandable for a human eye are

1. Age
2. Gender
3. Occupation- we can try
4. City category
5. Marital status

Rest of the variables are high in numbers and we would be needing more data. On the outlook we can create question and visualizations on the above-mentioned variables

Questions:

1. Number of purchases based on the age
2. Number of purchases made based on the marital status
3. We can do the plots age and marital status
4. Number of purchases based on the city category- arrange is descending order
5. Number of purchases based on the gender and age
6. Plot purchases based on the gender and age
7. Plot on gender, marital status, and age
8. We can make plot based on the purchases based on occupation, say we arrange the occupation and purchase in descending order, we can assume with more purchases will be having better income- have top 5 spenders
9. Based on gender we can bifurcate the product categories- particular product categories bought by males and females- we can do it by arranging them in descending order
10. Based on this we can have target ads

Just to get started, we had business study, once we did it, we analyzed that we can create more insights based on the above data. If anyone has different approach, we can try that as well and come up with visualizations.

Our Paper Presentation Slides

Roadmap

Just to get started, we had business study, once we did it, we analyzed that we can create more insights based on the above data. If anyone has different approach, we can try that as well and come up with visualizations.

See the open issues for a list of proposed features (and known issues)

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the GPL v3 License. See LICENSE for more information.

Contact

Team Members:

1. Neil Mascarenhas - About me?
2. Palak Agarwal
3. Shivaprasad Chandrashekarappa
4. Sushma Karjol

Project Link: https://mascarenhasneil.github.io/BlackFriday_Sale_Analysis/

References

Click to expand!

1. 4 awesome customer Segmentation examples (and why they work). (2020, February 19). Retrieved February 25, 2021, from https://manychat.com/blog/customer-segmentation-examples/

2. 8 ways to enhance your content marketing with social media. (n.d.). Retrieved February 25, 2021, from https://www.brandwatch.com/blog/marketing-8-ways-social-listening-can-benefit-your-content-marketing/

3. Bar charts - geom_bar. (n.d.). Retrieved February 25, 2021, from https://ggplot2.tidyverse.org/reference/geom_bar.html

4. Chandel, S. (2018, November 12). Black-Friday-Dataset. Retrieved February 19, 2021, from https://github.com/shwetachandel/Black-Friday-Dataset.

5. Create elegant data visualisations using the grammar of graphics. (n.d.). Retrieved February 24, 2021, from https://ggplot2.tidyverse.org/

6. Ggplot2 barplots : Quick start guide - r software and data visualization. (n.d.). Retrieved February 21, 2021, from http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization

7. Holtz, Y. (n.d.). Basic barplot with ggplot2. Retrieved February 25, 2021, from https://www.r-graph-gallery.com/218-basic-barplots-with-ggplot2.html

8. Kabacoff, R. (2015). R in action: Data analysis and graphics with R. Shelter Island, NY: Manning.

9. Munjal, H. (n.d.). 7 tips to increase your black friday sales. Retrieved February 25, 2021, from https://learn.g2.com/increase-black-friday-sales