Final_Project_Customer_Clustering

💼 Project Description

Objectives:

• Provide valuable insights about the customers and their shopping behaviour
• Customer segmentation into clusters
• Analysing each cluster, in order to give conclusion on how to target each cluster
• Market basket analysis

💡 Conclusion

💻 Data Set

Source: UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Online+Retail

The used data set is a transnational data set which contains transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Attribute Information:

• InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation
• StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
• Description: Product (item) name. Nominal.
• Quantity: The quantities of each product (item) per transaction. Numeric.
• InvoiceDate: Invoice Date and time. Numeric, the day and time when each transaction was generated.
• UnitPrice: Unit price. Numeric, Product price per unit in sterling.
• CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
• Country: Country name. Nominal, the name of the country where each customer resides.

Size: 541909 instances and 8 attributes

🛠️ Unsupervised Machine Learning

• K-Means
• Scikit-learn
• Pickle Python

🔗 Links