- About Dataset- This dataset contains the 2022 and 2023 global mart sales datasets.
- Used Python version 3.10.10 and Jupyter version 7.2.1 for this project.
- Used Kaggle API to Download Data from Kaggle.
- Used Jupyter Notebook to read data into Pandas dataframe, and transform data using Python.
- Transformation includes- renaming column , changing data type, adding calculated columns dropping unnecessary columns
- Used SQLalchemy to load data into df_orders table into prebuilt Database project4python_sql.
- Used window authentication to load data into SSMS and ODBC driver for connection.
- Analysis of Data in SSMS includes - find the top 10 highest revenue-generating product find the top 5 products in each region by highest revenue-generating find month-over-month growth for years 2022 and 2023 for each category which month has the highest sales which subcategory has the highest YoY growth