├── .gitignore ├── Association Rule Mining ├── AssociationRuleMining(On_clusters).ipynb └── AssociationRuleMining(On_original_dataset) (1).ipynb ├── Clustering ├── Clustering(K_Prototype).ipynb ├── Clustering(Kmodes_&_Kprototypes).ipynb └── Clustering_rules.ipynb ├── Datasets ├── WA_Fn-UseC_-Telco-Customer-Churn.csv ├── clustering_rules.csv ├── clustering_rules_0.xlsx ├── clustering_rules_1.csv ├── clustering_rules_all.xlsx └── new_telco.csv ├── Decision Tree ├── Decision_Tree_Customer_Churn(final).ipynb ├── Decision_Tree_Customer_Churn.ipynb ├── Decision_Tree_Customer_Churn_v3.ipynb ├── WA_Fn-UseC_-Telco-Customer-Churn.csv └── new_telco.csv ├── Ensemble Learning ├── Other Classifiers │ ├── Decision Tree │ │ └── Decision_Tree_Customer_Churn_best_features.ipynb │ ├── KNN │ │ └── KNNonChurnDataset.ipynb │ ├── Logistic Regression │ │ └── LogisticRegressionClassifier.ipynb │ ├── Naive Bayes │ │ └── Naive_bayes_Classifier.ipynb │ ├── Random Forest │ │ └── 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