BPNN, K-means, K-medoids
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Updated
Jun 28, 2024 - Jupyter Notebook
BPNN, K-means, K-medoids
A comparative study of K-centroid clustering algorithms, including KMeans, CustomKMeans, Fermat-Weber KMedians, and Weiszfeld KMedians, highlighting their performance on separated and non-separated datasets.
Final project of the International Master in Data Science in which our team develop marketing strategies for a fashion retail company targeted at specific customer segments and provide them with customized offers. The segmentation was done by employing RFM analysis in conjunction with unsupervised clustering algorithms.
Customer-Segmentation---Purchasing-Behavior
Performed exploratory data analysis (EDA), built predictive models, and derived actionable insights.
Unsupervised machine learning
Clustered behavioral data into two groups, regardless of gender, and evaluated cluster consistency with gender division using silhouette and Davies-Bouldin scores. Additionally, identified the optimal cluster count using the elbow method and re-evaluated clustering efficacy.
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