Data Mining Master Project

Project Summary

Performed Coherence Check, spotted outliers using DBSCAN and also Manual Filtering. Missing values were dealt carefully, imputing using KNN and Logistic Regression, mainly.
In order to segment clients, K-Protopytes (on categorical variables) and Hierarchical Clustering uppon K-Medoids (on numeric variables) were performed. Marketing campaings were also designed accordingly to the characteristics of each cluster.

Name		Name	Last commit message	Last commit date
Latest commit History 56 Commits
.ipynb_checkpoints		.ipynb_checkpoints
figures		figures
202122_Fall_DM_GroupAF_Notebook.ipynb		202122_Fall_DM_GroupAF_Notebook.ipynb
202122_Fall_DM_GroupAF_Report.pdf		202122_Fall_DM_GroupAF_Report.pdf
DM_group_project.ipynb		DM_group_project.ipynb
DM_profiling_report.html		DM_profiling_report.html
README.md		README.md
a2z_insurance.sas7bdat		a2z_insurance.sas7bdat
k_prototypes_Article.pdf		k_prototypes_Article.pdf
project_description.pdf		project_description.pdf