diff --git a/api/dask-clustering.py b/api/dask-clustering.py
index ba0eea6..9019024 100644
--- a/api/dask-clustering.py
+++ b/api/dask-clustering.py
@@ -1,7 +1,8 @@
 import dask.dataframe as dd
 import dask.array as da
 import numpy as np
-from utils.helper_functions import FileProgressTracker
+from api.utils.helper_functions import FileProgressTracker
+
 
 def compute_percentiles(df, col, quantiles):
     """Compute given percentiles for a column in a Dask DataFrame."""
@@ -47,33 +48,46 @@ def _assign_conditionally(part, col, percentiles, filter_col, filter_val, cluste
     return df
 
 
-# ------------------------------------------
-# Example Usage
-# ------------------------------------------
-
 input_folder = "/mnt/samsung_2tb/mixed_data/output/pca_transformed_results/output/*.parquet"
-
 print("Loading data")
+
 # Read the data
 df = dd.read_parquet(input_folder, engine='pyarrow')
 
-print("Assign cluster by column for PCA_2")
+print("Assign cluster by column for PCA_1")
 # 1) First level clustering by PCA_1:
 df_test, pca1_percentiles = assign_clusters_by_column(df, 'PCA_1', cluster_col='cluster_id')
 
-with FileProgressTracker(description="Clustering", total_files=50, interval=60) as tracker:
+print("Clustering for PCA 2")
+with FileProgressTracker(description="Clustering", total_files=50, interval=4) as tracker:
     for i in range(50):
-        # 2) Second level clustering by PCA_2 within a specific bucket of PCA_1 (for example, bucket 1):
-        # Compute percentiles for PCA_2, but only for the subset where cluster_id == 1
+
+        # 2) Second level clustering by PCA_2 within a bucket of PCA_1 for all buckets:
         pca2_percentiles = compute_percentiles(df_test[df_test['cluster_id'] == i], 'PCA_2', quantiles=np.arange(0,102,2))
 
-        # Assign cluster_id_2 only to rows where cluster_id == 1
+        # Assign cluster_id_2 only to rows where cluster_id == i 
         df_test = assign_clusters_conditionally(df_test, filter_col='cluster_id', filter_val=i, col='PCA_2', cluster_col='cluster_id_2', percentiles=pca2_percentiles)
 
-        # At this point, `df` has both cluster_id (from PCA_1) and cluster_id_2 (from PCA_2 for bucket 1) columns.
+        # Update progress bar
+        tracker.update_progress()
+
+print("Clustering for PCA_3")
+with FileProgressTracker(description="Clustering", total_files=50, interval=4) as tracker:
+    for i in range(50):
+
+        # 2) Third level clustering by PCA_3 within a specific bucket of PCA_2
+        # Compute percentiles for PCA_2, but only for the subset where cluster_id == i
+        pca2_percentiles = compute_percentiles(df_test[df_test['cluster_id'] == i], 'PCA_3', quantiles=np.arange(0,102,2))
+
+        # Assign cluster_id_2 only to rows where cluster_id == 1
+        df_test = assign_clusters_conditionally(df_test, filter_col='cluster_id', filter_val=i, col='PCA_3', cluster_col='cluster_id_3', percentiles=pca2_percentiles)
 
+        # Update progress bar
         tracker.update_progress()
 
+df['combined_cluster_id'] = (df_test['cluster_id'] * 50**2) + (df_test['cluster_id_2'] * 50) + df_test['cluster_id_3']
+
+df['combined_cluster_id'] = df['combined_cluster_id'].astype(int)
 
 print("Cluster completed, saving results")
 # After completing all clustering steps, repartition to 400 partitions
@@ -82,59 +96,3 @@ def _assign_conditionally(part, col, percentiles, filter_col, filter_val, cluste
 # Save as Parquet files
 output_path = "/mnt/10tb_hdd/buckets_pca_dask"
 df.to_parquet(output_path, engine='pyarrow', write_index=False)
-
-
-# import dask.dataframe as dd
-# import dask.array as da
-# import numpy as np
-# import os 
-
-# input_folder = "/mnt/samsung_2tb/mixed_data/output/pca_transformed_results/output/*.parquet"
-
-# df = dd.read_parquet(input_folder, engine="pyarrow")
-
-# def get_percentiles(dataframe):
-#     pca_array = dataframe.to_dask_array(lengths=True)
-#     percentiles_to_compute = [i for i in np.arange(102, step=2)]
-#     percentiles = da.percentile( pca_array, q=percentiles_to_compute, method='linear', internal_method='tdigest').compute()
-
-#     return percentiles.tolist()
-
-# def assign_bucket(part, name_pca_column, percentiles):
-#     cluster_ids = np.digitize(part[name_pca_column], percentiles) - 1
-#     cluster_ids = np.where(cluster_ids==50, 49, cluster_ids)
-#     part['cluster_id'] = cluster_ids
-#     return part
-
-# pca1_percentiles = get_percentiles(df['PCA_1'])
-
-# df = df.map_partitions(assign_bucket())
-
-
-# for i in range(50): 
-#     bucket = df[df['cluster_id'] == i]
-
-#     # Convert 'PCA_1' column to a Dask array
-#     pca2_array = df['PCA_2'].to_dask_array(lengths=True)
-
-#     # Define the percentiles to compute
-#     percentiles_to_compute = [i for i in np.arange(102, step=2)]
-
-#     # Compute the percentiles using Dask's internal method
-#     percentiles = da.percentile(
-#         pca2_array, 
-#         q=percentiles_to_compute, 
-#         method='linear', 
-#         internal_method='tdigest'  # or 'tdigest' for memory efficiency
-#     ).compute()
-
-#     def assign_bucket(part):
-#         cluster_ids = np.digitize(part['PCA_1'], percentiles) - 1 
-#         cluster_ids = np.where(cluster_ids==50, 49, cluster_ids )
-#         part['cluster_id'] = cluster_ids
-#         return part
-
-#     df = df.map_partitions(assign_bucket)
-
-#     for i in range(50):
-