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Exploratory Data Analysis of E-Commerce Transactional Data
- Make sure business stakeholders ask the right questions - often by exploring and visualizing data - and validate their business assumptions with a thorough investigation
- Spot any potential anomalies in data to avoid feeding wrong data to a machine learning model
- Interpret the model output and test its assumptions
- Context of Data
- Data Cleaning (a.k.a data preprocessing)
- Exploratory Data Analysis
- Results
- Conclusion
- 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 2010â??9th Dec 2011 (One year)



- Quantity has negative values
- Unit Price has zero values (FREE items?)








- United Kingdom
- Germany
- France
- Ireland (EIRE)
- Spain


- United Kingdom
- Netherlands
- Ireland (EIRE)
- Germany
- France
- The customer with the highest number of orders comes from the United Kingdom (UK)
- The customer with the highest money spent on purchases comes from the Netherlands
- The company receives the highest number of orders from customers in the UK (since it is a UK-based company). Therefore, the TOP 5 countries (including the UK) that place the highest number of orders are as follow - United Kingdom, Germany, France, Ireland (EIRE), Spain
- As the company receives the highest number of orders from customers in the UK (since it is a UK-based company), customers in the UK spend the most on their purchases. Therefore, the TOP 5 countries (including the UK) that spend the most money on purchases are as follow â?? United Kingdom, Netherlands, Ireland (EIRE), Germany, France
- November 2011 has the highest sales. The month with the lowest sales is undetermined as the dataset consists of transactions until 9th December 2011 in December
- There are no transactions on Saturday between 1st Dec 2010 - 9th Dec 2011
- The number of orders received by the company tends to increases from Monday to Thursday and decrease afterward
- The company receives the highest number of orders at 12:00 pm. Possibly most customers made purchases during lunch hour between 12:00 pm - 2:00 pm
- The company tends to give out FREE items for purchases occasionally each month (Except June 2011). However, it is not clear what factors contribute to giving out the FREE items to the particular customers