Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
.ipynb_checkpoints		.ipynb_checkpoints
img		img
.gitattributes		.gitattributes
Customer Segmentation using ML(Final).ipynb		Customer Segmentation using ML(Final).ipynb
DataCleaning.csv		DataCleaning.csv
DataExploratory.csv		DataExploratory.csv
MeanRFM.csv		MeanRFM.csv
OnlineRetail.xlsx		OnlineRetail.xlsx
Project-Report.pdf		Project-Report.pdf
README.md		README.md
RFMD.csv		RFMD.csv
StandardScaling.csv		StandardScaling.csv

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Customer-Segmentation

Efficiently Segment different division of customers based on their past purchasing behavior to understand which customer is more loyal or frequently buying the product. Implemented the code in Jupyter Notebook and used hybrid algorithm to effectively segregate the consumer group.

Implemented using Jupyter notebook python 2.7

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The project is used for dividing different groups of consumer from their past retail data. ML Algorithm named SAPK(Semi Supervised Affinity propagation) and K-Means is used for efficient segmentation of user group.

scikit-learn scaling feature-engineering k-means-clustering customer-segmentation data-cleaning-and-preprocessing data-exploration-and-visualization- semi-supervised-affinity-propagation

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Jupyter Notebook 100.0%