How anhnhatcs/uma_data_mining_project works
This repository is a customer-churn classification project centered on a Jupyter notebook and the Telco churn CSV. Its documented workflow covers data exploration and preparation, visualization, classifier training and tuning, then error analysis and model comparison. The README describes these capabilities, but no notebook source was provided, so the graph reflects documented relationships rather than verified implementation calls.
Main entry points: data_mining_project_team_7.ipynb Explore and prepare · WA_Fn-UseC_-Telco-Customer-Churn.csv Telco churn data
Components 9
Data analysis
- Telco churn data
WA_Fn-UseC_-Telco-Customer-Churn.csv - Explore and prepare
data_mining_project_team_7.ipynb - Data visualization
data_mining_project_team_7.ipynb
Modeling
- Classifier training
data_mining_project_team_7.ipynb - Hyperparameter tuning
data_mining_project_team_7.ipynb
Evaluation
- Error analysis
data_mining_project_team_7.ipynb - Model evaluation
data_mining_project_team_7.ipynb - Model comparison
data_mining_project_team_7.ipynb
Other components
- Project user
Connections 8
- Project user → Explore and prepare: runs analysis
- Telco churn data → Explore and prepare: loads data
README.md - Explore and prepare → Data visualization: examines data
README.md - Explore and prepare → Classifier training: splits data
README.md - Classifier training → Hyperparameter tuning: tunes models
README.md - Classifier training → Model evaluation: evaluates models
README.md - Model evaluation → Error analysis: analyzes errors
README.md - Model evaluation → Model comparison: compares models
README.md