shrutipandit707 / kmeans

Unsupervised K-Means Clustering for Online Retail Data

In this case study, we are attempting to solve a real world business problem using Unsupervised Clustering K-Means techniques. Online retail is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Table of Contents

• General Info
• Technologies Used
• Acknowledgements

General Information

• Provide general information about your project here.

We will be using Unsupervised Clustering K-Means techniques for Online Retail Data.

• What is the background of your project?

In this case study, we are attempting to solve a real world business problem using Unsupervised Clustering K-Means techniques. Online retail is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

• Business Problem Statement:

In this case study, we are attempting to solve a real world business problem using Unsupervised Clustering K-Means techniques. Online retail is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

• What is the dataset that is being used?

We have got the dataset from Upgrad.

Technologies Used

• Python - version 3.6.9
• Numpy - version 1.21.5
• Pandas - version 1.3.5
• Seaborn - version 0.11.2

Acknowledgements

Give credit here.

• This project was inspired by Upgrad.
• This project was based on Upgrad's Tutorial.

Contact

Created by [@shrutipandit707] - feel free to contact us!