This is an experiment to process e-commerce data. The repo contains examples of how to apply machine learning algorithms with this type of data.

The data is from a kaggle competition, you can find it here : https://www.kaggle.com/carrie1/ecommerce-data/kernels

It contians information from transactions from a UK retailer. The fields are:

• InvoiceNo: purchase identifier
• StockCode: product identifier
• Description: description of the product
• Quantity: amount of items for that product that are contained in the purchase
• InvoiceDate: date of the purchase
• UnitPrice: price of the product
• CustomerID: customer identifier
• Country: country that purchase took place

Create virtualenv

	virtualenv venv 
	source venv/bin/activate
	pip install -r requirements.txt 

Start jupyter

	jupyter-notebook

The examples provided are:

1. Data Cleaning: clean the data, filling missing inforation
2. Data Exploration: see how the data is distributed, find outliers
3. Clustering: perform clustering methods to find patterns in the data
4. Classification: classify sales according to the money spent
5. Recommender System: generate recommendations based on the users' history

The examples 2,3,4 and 5 use the cleaned data result from the data cleaning process.