The purpose of the analysis is to focus on how product sales data to be utilized to make data driven decisions.
The dataset is a set of products with multiple features. The classifier model suggests if following product will be sold or not based on these features.
It is important to note that even though many products are present in inventory, only about 10% sell each year and many of the products only have a single sale in the course of a year.
Thus the classifier model backed by data science analysis like tree analysis and boosting methods provides binary classification that can be used as the main determinant evaluating the inventory.
Tools used
- Python jupyter notebook
- Plotly chart studio for interactive plots