Super Store Sales Analysis

About

Superstore is close to a real-world fake dataset with 10,000 records of sales information. The aim of this project is to provide KPIs and insights to monitor trends. To start, cleaning of the dataset is done in Python, performing exploratory data analysis and mining impactful insights utilizing pandas, numpy, and pandasql.

Fun Fact - I used both Pandas and PandaSQL (to execute SQL queries) to answer business questions. The major reason is to check the number of lines of code and the impact of outputs from both libraries on a single screen. Also, I am a MAC user 😌

Languages and Tools Used

Python
SQL - Pandasql
Tableau
Excel

Highlights of Sales Analysis

California state contributed the highest sales in all years generating the highest revenue of more than 700k in 2018. November and December are the top months to generate more than 50% of revenue to total yearly sales in the consumer segment, which can be considered as the safe months to introduce new categories by the store managers.

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