https://github.com/gregoritsch3/ml_clustering_eda_customersegmentation

An EDA and Machine Learning Clustering exercise on the Mall Customer Segmentation synthetic dataset demonstrating the use of KMeans Clustering and the Elbow Method. The clustering algorithm successfully segments the customer base into groups distinguishable by their annual income and spending score.
https://github.com/gregoritsch3/ml_clustering_eda_customersegmentation

clustering kmeans-clustering machine-learning matplotlib numpy pandas scikit-learn scipy seaborn

Last synced: 6 days ago
JSON representation

An EDA and Machine Learning Clustering exercise on the Mall Customer Segmentation synthetic dataset demonstrating the use of KMeans Clustering and the Elbow Method. The clustering algorithm successfully segments the customer base into groups distinguishable by their annual income and spending score.

• Host: GitHub
• URL: https://github.com/gregoritsch3/ml_clustering_eda_customersegmentation
• Owner: Gregoritsch3
• Created: 2024-11-19T16:42:32.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-22T14:03:43.000Z (3 months ago)
• Last Synced: 2024-12-04T08:07:36.451Z (2 months ago)
• Topics: clustering, kmeans-clustering, machine-learning, matplotlib, numpy, pandas, scikit-learn, scipy, seaborn
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python
• Size: 1.16 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# ML_Clustering_EDA_CustomerSegmentation

An EDA and Machine Learning Clustering exercise on the Mall Customer Segmentation synthetic dataset demonstrating the use of KMeans Clustering and the Elbow Method. The clustering algorithm successfully segments the customer base into groups distinguishable by their annual income and spending score. Additionally, an ANOVA hypothesis test examines the differences in the medians of features (Age, Annual Income, Spending Score) as differentiated by gender.