eCommerce behavior data from multi category store

DSND Term 2 Project: eCommerce behavior data from multi category store

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

The code should run with no issues using Python versions 3.*. If you use Anaconda, please make sure install below libraries:

1. squarify
2. xgboost

Also, due to the data volume is huge and not able to upload, please download the data (2019-Nov.csv) from Kaggle here.

Project Motivation

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?

File Descriptions

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.

Results

The analysis and insight summary can be found at the Medium post here.

Licensing, Authors, Acknowledgements

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!