--- a
+++ b/ML_Models_Pipeline.py
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+# Clustering Analysis
+
+##1 Hierarchical Clustering
+
+plt.figure(figsize=(15, 10))
+linkage_matrix = linkage(X_train_outliers_removed, method='ward')
+dendrogram(linkage_matrix)
+plt.title('Hierarchical Clustering Dendrogram')
+plt.show()
+k = 2
+clusters = fcluster(linkage_matrix, k, criterion='maxclust')
+
+X_train_scaled
+
+##2 K-means Clustering
+
+num_clusters = 2
+kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+kmeans.fit(X_train_prepared)
+# Assign cluster labels to the original data
+X_train['cluster'] = kmeans.labels_
+# Combine the cluster labels with the original training set
+patient_df_with_clusters = patient_df.loc[X_train.index].copy()
+patient_df_with_clusters['cluster'] = X_train['cluster']
+
+# Explore characteristics of each cluster
+for cluster_label in range(num_clusters):
+    cluster_data = patient_df_with_clusters[patient_df_with_clusters['cluster'] == cluster_label]
+    print(f'\nCluster {cluster_label} Characteristics:')
+    print(cluster_data.describe())
+
+# Calculate the mean of each feature for each cluster
+# Exclude non-numeric columns from the calculation
+cluster_means = patient_df_with_clusters.select_dtypes(include=['number']).groupby('cluster').mean()
+print("\nMean Value of Features for Each Cluster:")
+print(cluster_means)
+
+plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1], c=kmeans.labels_, cmap='viridis')
+plt.title('Clustering Analysis')
+plt.xlabel('Principal Component 1')
+plt.ylabel('Principal Component 2')
+plt.show()
+
+# Classifier: The Model (Logistic Regression, RandomForestClassifier, GradientBoostingClassifier, SVM)
+
+## Run the below code (line 47 to 91) in one chunk
+
+# Prepare the classifiers
+classifiers = {
+    "Logistic Regression": LogisticRegression(),
+    "Random Forest": RandomForestClassifier(random_state=42),
+    "Gradient Boosting": GradientBoostingClassifier(random_state=42),
+    "Support Vector Machine": SVC(probability=True, random_state=42)
+}
+# Train and evaluate the classifiers
+results = {}
+for name, clf in classifiers.items():
+    # Train the classifier
+    clf.fit(X_train_scaled, y_train)
+    # Predict on the test set
+    y_pred = clf.predict(X_test_scaled)
+    y_proba = clf.predict_proba(X_test_scaled)[:, 1]
+    # Evaluate the classifier
+    accuracy = accuracy_score(y_test, y_pred)
+    precision = precision_score(y_test, y_pred)
+    recall = recall_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred)
+    roc_auc = roc_auc_score(y_test, y_proba)
+    cv_scores = cross_val_score(clf, X_train_scaled, y_train, cv=5)
+    # Store results
+    results[name] = {
+        "Accuracy": accuracy,
+        "Precision": precision,
+        "Recall": recall,
+        "F1 Score": f1,
+        "ROC-AUC Score": roc_auc,
+        "Cross-validation scores": cv_scores.tolist(),  # Convert to list for printing
+
+    }
+    # Print the performance
+    print(f"Results for {name}:")
+    print(f"Accuracy: {accuracy:.4f}")
+    print(f"Precision: {precision:.4f}")
+    print(f"Recall: {recall:.4f}")
+    print(f"F1 Score: {f1:.4f}")
+    print(f"ROC-AUC Score: {roc_auc:.4f}\n")
+    print(f"Cross-validation scores: {cv_scores}\n")
+
+# Compare results
+results_df = pd.DataFrame(results).transpose()
+print("Comparison of Classifiers:")
+print(results_df)
+
+## Finding out the most important feature?
+
+# Feature importance
+# Train the classifier
+clf =GradientBoostingClassifier(random_state=42)
+clf.fit(X_train_prepared, y_train)
+
+feature_importance = clf.feature_importances_
+important_features = X_train_prepared.columns[np.argsort(feature_importance)[::-1]]
+print("Most Important Feature:", important_features[0])
+
+# 6.2 Exclude the best feature and retrain the classifier
+X_train_subset = X_train_prepared.drop(important_features[0], axis=1)
+X_test_subset = X_test_prepared.drop(important_features[0], axis=1)
+clf_subset = RandomForestClassifier(random_state=42)
+clf_subset.fit(X_train_subset, y_train)
+
+y_pred_test_subset = clf_subset.predict(X_test_subset)
+print("Test Accuracy (Excluding Best Feature):", accuracy_score(y_test, y_pred_test_subset))
+
+# Train the classifier
+clf =RandomForestClassifier(random_state=42)
+clf.fit(X_train_prepared, y_train)
+
+feature_importance = clf.feature_importances_
+important_features = X_train_prepared.columns[np.argsort(feature_importance)[::-1]]
+
+print("Most Important Feature:", important_features[0])
+
+# 6.2 Exclude the best feature and retrain the classifier
+X_train_subset = X_train_prepared.drop(important_features[0], axis=1)
+X_test_subset = X_test_prepared.drop(important_features[0], axis=1)
+
+clf_subset = RandomForestClassifier(random_state=42)
+clf_subset.fit(X_train_subset, y_train)
+
+y_pred_test_subset = clf_subset.predict(X_test_subset)
+print("Test Accuracy (Excluding Best Feature):", accuracy_score(y_test, y_pred_test_subset))