https://github.com/mrsaeeddev/customer-segmentation-using-rfm-matrix-technique-and-k-means-algorithm
https://github.com/mrsaeeddev/customer-segmentation-using-rfm-matrix-technique-and-k-means-algorithm
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/mrsaeeddev/customer-segmentation-using-rfm-matrix-technique-and-k-means-algorithm
- Owner: mrsaeeddev
- Created: 2019-02-02T08:05:56.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-12-04T06:27:08.000Z (over 6 years ago)
- Last Synced: 2025-03-24T22:34:30.660Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 29.7 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer Segmentation Using RFM Matrix Technique and K Means Algorithm
## Analysis of E-commerce data of different countries in order to understand the need of markets and customer segmentation in order to identify best customers
## Dataset:
Typically e-commerce data sets are proprietary and consequently hard to find among publicly
available data. However, The UCI Machine Learning Repository has made this data set containing
actual transactions from 2010 and 2011. The data set is maintained on their site, where it can be
found by the title "Online Retail".
This 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.
## Approach :
In this dataset, I performed Exploratory Data Analysis(EDA) on dataset by which I visualized different parameters of dataset.
Then, I used RFM Matrix technique and K Means Algorithm to identify the best customers.
## Results :
This method can be used by vendors to identify best potential customers which may
be helpful for them to target customers for promotions and marketing compaigns.