Project title: Superstore-Data-Analysis
The link for the Project:
Description
This project, I analysed the areas where there are least sales of a Superstore In this project. I used libraries like panda, matplotlib, numpy and seaborn to explore which region has least sales, how are the sales of each category is divided across the regions, how are the Sales across each segment, which State has the lowest sales and which City has the lowest sales. I completed this task for my internship with The Sparks Foundation.
About the Dataset
The given dataset was given as a task from the Sparks Foundation https://www.thesparksfoundationsingapore.org/.With this dataset I am tried to visualize the sales of category, sub-category in each states and country to achieve the objective of as of which are the areas where business is low. The name of the Dataset used for this projects is SampleSuperstore.csv. There are 9994 rows and 13 columns with each row containing data about commodities.
I have used be using Python 3 for this analysis. The Libraries/Packages I used in this projects are as follows:
- numpy (as np is one of the very famous packages for working with arrays in python)
- pandas (Is greatly used in analysis of data and making dataframe)
- matplotlib (Lets make our Analyzation fun and interative with the visualization library matplotlib)
- seaborn (Adding more colours into matplotlib visualization)
Inferences and Conclusion
The analysis gives an overview of the sales of commodities. The observation contains a lot of information.
With that, we’ve come to the end of this analysis. The following are conclusions drawn from the analysis. Hope you enjoyed!! >
- Region with least sales is South in category furniture
- Segment with least sales is Consumer
- State with least sales is North Dakota
- City with least sales is Abilene
- Lowest shipping mode is on the Same Day
References
- Pandas user guide: https://pandas.pydata.org/docs/user_guide/index.html
- Matplotlib user guide: https://matplotlib.org/3.3.1/users/index.html
- Seaborn user guide & tutorial: https://seaborn.pydata.org/tutorial.html
- Stackoverflow Community (Get answers of any problems): https://stackoverflow.com/questions
- Python solutions in Geeksforgeeks (Solutions made easy): https://www.geeksforgeeks.org/python-programming-language/
- Opendatasets Python library (Choosing and using datasets in python made easy): https://github.com/JovianML/opendatasets