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dbscan-clustering-algorithm

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This project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.

  • Updated Jul 11, 2024
  • Jupyter Notebook

This Human Activity Recogisition analyses human activity patterns using smartphone sensor data from the UCI Human Activity Recognition dataset. It involves outlier detection, correlation analysis, and structural graph analysis. DBSCAN clustering is applied, followed by LDA for dimensionality reduction, to visualise and interpret activity clusters

  • Updated Jun 27, 2024
  • Jupyter Notebook

The main objective of this project is to group customers with similar behavior and characteristics into segments to better understand their needs and preferences. The unsupervised machine learning techniques used in this project include K-means clustering ,hierarchical clustering and DBScan Clustering.

  • Updated Feb 20, 2024
  • Jupyter Notebook

πŸ“ŠπŸŽ―βœ¨ Harness the power of the RFM (Recency, Frequency, Monetary) method to cluster customers based on their purchase behavior! Gain valuable insights into distinct customer segments, enabling you to optimize marketing strategies and drive business growth. πŸ“ˆπŸ’‘πŸš€

  • Updated Jun 10, 2023
  • Jupyter Notebook

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