E-commerce data analysis

To give a brief overview, this notebook is dedicated to sections as follow:

Context of Data
Data Cleaning (a.k.a data preprocessing)
Exploratory Data Analysis
Model results
Conclusion

Context of Data

The dataset consists of transactional data with customers in different countries who make purchases from an online retail company based in the United Kingdom (UK) that sells unique all-occasion gifts. The information is summarized as below:

Company              — UK-based and registered non-store online retail
Products for selling — Mainly all-occasion gifts
Customers            — Most are wholesalers (local or international)
Transactions Period  — 1st Dec 2010–9th Dec 2011 (One year)

The analysis and optimization for storage area (i.e. storage area is expensive so we want to keep the products which give us the best revenue). Keeping in mind that fast delivery is also critical so we like to help the retailer by predicting the amount of sold product. There are missing values in the data, optimization is done by doing the analysis.

Please note the assignment is done in Python. Use the below data dictionary for your reference:

InvoiceNo (invoice_num)   : A number assigned to each transaction
StockCode (stock_code)    : Product code
Description (description) : Product name
Quantity (quantity)       : Number of products purchased for each transaction
InvoiceDate (invoice_date): Timestamp for each transaction
UnitPrice (unit_price)    : Product price per unit
CustomerID (cust_id)      : Unique identifier each customer
Country (country)         : Country name

Model Training

Linear Regression
Decision Tree Regression
Random Forest Regression

Author @vikashryder