DSND Term 2 Project: eCommerce behavior data from multi category store
The code should run with no issues using Python versions 3.*. If you use Anaconda, please make sure install below libraries:
- squarify
- xgboost
Also, due to the data volume is huge and not able to upload, please download the data (2019-Nov.csv) from Kaggle here.
This is DSND Term 2 Write-a-Data-Science-Blog-Post project. In this project, I use eCommerce behavior data to explore:
Question 1: Daily traffic in November?
Question 2: What product categories and brands are the most popular?
Question 3: Will the customer purchases the products while adding them to the shopping cart?
eCommerce behavior data from multi category store.ipynb - The notebooks is exploratory in searching through the data pertaining to the questions showcased by the notebook title. Markdown cells & comments were used to assist in walking through the thought process for individual steps.
The analysis and insight summary can be found at the Medium post here.
Must give credit to REES46 Marketing Platform & Kaggle for the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here. Feel free to use the code here!