diff --git a/requirements.txt b/requirements.txt
index 1fdf8db..4683f85 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,12 +14,16 @@ idna==2.10
 ipykernel==6.25.1
 ipython==8.14.0
 Jinja2==3.1.2
+kafka==1.3.5
+kafka_python==2.1.3
 keras==3.9.2
 MarkupSafe==2.1.3
+matplotlib==3.8.3
 ml-dtypes==0.4.1
 numpy==1.26.4
 opt-einsum==3.4.0
-pandas==2.0.2
+pandas>=2.2.3
+Pillow==11.2.1
 plotly==5.16.1
 protobuf==3.20.3
 pylint==3.3.6
@@ -27,6 +31,7 @@ pytest==8.3.5
 PyPDF2==3.0.1
 python-docx==1.1.2
 requests==2.31.0
+river>=0.22.0
 scikit-learn==1.3.0
 six==1.16.0
 statsmodels==0.14.4
diff --git a/src/PrescriptiveAnalysis2/backend/consumer.py b/src/PrescriptiveAnalysis2/backend/consumer.py
new file mode 100644
index 0000000..9f09684
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/backend/consumer.py
@@ -0,0 +1,189 @@
+import json
+import logging
+import os
+import pickle
+from kafka import KafkaConsumer
+from river import tree, metrics
+import io
+import base64
+import threading
+import time
+import pandas as pd
+from datetime import datetime
+
+class Consumer:
+    """Kafka consumer for processing shopping event data and training a Hoeffding tree model."""
+
+    def __init__(self, bootstrap_servers: str = 'localhost:9092', topic: str = 'shopping_events'):
+        # Configure logging
+        logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+        # Initialize Kafka consumer
+        self.consumer = KafkaConsumer(
+            topic,
+            bootstrap_servers=bootstrap_servers,
+            value_deserializer=lambda m: json.loads(m.decode('utf-8'))
+        )
+
+        # Define categories
+        self.product_categories = ['electronics', 'fashion', 'home', 'books', 'toys']
+        self.event_types = ['view', 'click', 'add_to_cart']
+
+        # Initialize Hoeffding Tree model
+        self.model = tree.HoeffdingTreeClassifier(
+            grace_period=10,
+            delta=0.99,
+            nominal_attributes=[
+                f'product_category_{cat}' for cat in self.product_categories
+            ] + [
+                f'event_type_{et}' for et in self.event_types
+            ],
+            split_criterion='gini'
+        )
+
+        # Initialize metrics
+        self.accuracy = metrics.Accuracy()
+        self.f1_score = metrics.F1()
+        self.sample_count = 0
+        
+        # Set up data directory
+        self.data_dir = './src/PrescriptiveAnalysis2/streamlit_data/'
+        self.tree_dir = os.path.join(self.data_dir, 'tree')
+        self.events_dir = os.path.join(self.data_dir, 'hoeff_events')
+        
+        # Create directories
+        os.makedirs(self.data_dir, exist_ok=True)
+        os.makedirs(self.tree_dir, exist_ok=True)
+        os.makedirs(self.events_dir, exist_ok=True)
+        
+        # In-memory event store
+        self.recent_events = []
+        self.max_recent_events = 100
+        
+        # Last update timestamps
+        self.last_tree_update = 0
+        self.last_events_update = 0
+
+    def preprocess(self, data: dict) -> dict:
+        """Preprocess raw event data into model features."""
+        event_mapping = {'view': 0, 'click': 1, 'add_to_cart': 2}
+        category_mapping = {cat: idx for idx, cat in enumerate(self.product_categories)}
+
+        return {
+            'event_type': event_mapping[data['event_type']],
+            'session_duration': data['session_duration'],
+            'product_category': category_mapping[data['product_category']],
+            'user_total_purchases': data['user_total_purchases']
+        }
+
+    def process_event(self, data: dict):
+        """Process a single shopping event."""
+        features = self.preprocess(data)
+        target = data['purchase']
+
+        try:
+            # Make prediction
+            y_pred = self.model.predict_one(features)
+
+            if y_pred is not None:
+                self.accuracy.update(target, y_pred)
+                self.f1_score.update(target, y_pred)
+
+            # Learn from the new example
+            self.model.learn_one(features, target)
+            self.sample_count += 1
+            
+            # Create readable event record
+            event_record = {
+                "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                "user_id": data["user_id"],
+                "event_type": data["event_type"],
+                "session_duration": data["session_duration"],
+                "product_category": data["product_category"],
+                "user_total_purchases": data["user_total_purchases"],
+                "purchase": "Yes" if target == 1 else "No",
+                "prediction": "Yes" if y_pred == 1 else "No" if y_pred == 0 else "N/A"
+            }
+            
+            # Add to recent events
+            self.recent_events.append(event_record)
+            self.recent_events = self.recent_events[-self.max_recent_events:]  # Keep only most recent
+            
+            # Create pandas DataFrame for events
+            df = pd.DataFrame(self.recent_events)
+            
+            # Update event data file every 5 processed events
+            if self.sample_count % 5 == 0:
+                # Save as CSV for easy reading
+                current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
+                csv_path = os.path.join(self.events_dir, f"events_{current_time}.csv")
+                df.to_csv(csv_path, index=False)
+                self.logger.info(f"Saved events to {csv_path}")
+            
+            # Update tree visualization and accuracy every 20 events
+            if self.sample_count % 20 == 0:
+                self.update_tree_visualization()
+                self.update_accuracy()
+                
+            self.logger.info(f"Processed event: {data['user_id']}, prediction: {y_pred}, actual: {target}")
+
+        except Exception as e:
+            self.logger.error(f"Error processing message: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+
+    def update_tree_visualization(self):
+        """Generate and save tree visualization image"""
+        try:
+            # Generate tree visualization
+            graph = self.model.draw(max_depth=5)
+            
+            # Save as PNG image with timestamp
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            image_path = os.path.join(self.tree_dir, f"tree_{timestamp}")
+            graph.render(filename=image_path, format='png', cleanup=True)
+            
+            self.logger.info(f"Tree visualization saved to {image_path}.png")
+            self.last_tree_update = time.time()
+                
+        except Exception as e:
+            self.logger.error(f"Error updating tree visualization: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+
+    def update_accuracy(self):
+        """Update accuracy metrics file"""
+        try:
+            # Save accuracy to a simple text file
+            accuracy_path = os.path.join(self.data_dir, 'accuracy.txt')
+            with open(accuracy_path, 'w') as f:
+                f.write(str(self.accuracy.get()))
+                
+            self.logger.info(f"Accuracy updated: {self.accuracy.get():.4f}")
+                
+        except Exception as e:
+            self.logger.error(f"Error updating accuracy: {e}")
+
+    def run(self):
+        """Start consuming messages and processing events."""
+        self.logger.info("Starting shopping event consumer and training model...")
+
+        try:
+            for message in self.consumer:
+                data = message.value
+                self.process_event(data)
+
+        except KeyboardInterrupt:
+            self.logger.info("Consumer stopped by user.")
+        except Exception as e:
+            self.logger.error(f"Unexpected error: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+        finally:
+            self.consumer.close()
+            self.logger.info("Consumer connection closed.")
+
+if __name__ == "__main__":
+    consumer = Consumer()
+    consumer.run()
\ No newline at end of file
diff --git a/src/PrescriptiveAnalysis2/backend/consumer_clu.py b/src/PrescriptiveAnalysis2/backend/consumer_clu.py
new file mode 100644
index 0000000..8f98ca6
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/backend/consumer_clu.py
@@ -0,0 +1,237 @@
+import json
+import logging
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from kafka import KafkaConsumer
+from datetime import datetime
+import time
+
+# ========== CluStream Micro-Cluster Code ==========
+
+class MicroCluster:
+    def __init__(self, point, timestamp):
+        self.N = 1
+        self.LS = np.array(point, dtype=float)
+        self.SS = np.square(point)
+        self.timestamp = timestamp
+
+    def add_point(self, point):
+        self.N += 1
+        self.LS += point
+        self.SS += np.square(point)
+
+    def get_centroid(self):
+        return self.LS / self.N
+
+    def get_radius(self):
+        centroid = self.get_centroid()
+        return np.sqrt(np.sum(self.SS / self.N - centroid ** 2))
+
+class CluStream:
+    def __init__(self, max_micro_clusters=3, distance_threshold=50):
+        self.micro_clusters = []
+        self.max_micro_clusters = max_micro_clusters
+        self.distance_threshold = distance_threshold
+
+    def _euclidean(self, a, b):
+        return np.linalg.norm(a - b)
+
+    def update(self, point, timestamp):
+        point = np.array(point)
+        if not self.micro_clusters:
+            self.micro_clusters.append(MicroCluster(point, timestamp))
+            return
+
+        min_dist = float('inf')
+        best_cluster = None
+
+        for cluster in self.micro_clusters:
+            dist = self._euclidean(point, cluster.get_centroid())
+            if dist < min_dist:
+                min_dist = dist
+                best_cluster = cluster
+
+        if min_dist <= self.distance_threshold:
+            best_cluster.add_point(point)
+        elif len(self.micro_clusters) < self.max_micro_clusters:
+            self.micro_clusters.append(MicroCluster(point, timestamp))
+        else:
+            # Replace the oldest cluster
+            oldest_idx = np.argmin([c.timestamp for c in self.micro_clusters])
+            self.micro_clusters[oldest_idx] = MicroCluster(point, timestamp)
+
+    def get_clusters(self):
+        return [cluster.get_centroid() for cluster in self.micro_clusters]
+
+# ========== Kafka + CluStream Consumer ==========
+
+class Consumer:
+    """Kafka consumer for processing parking event data and clustering with CluStream."""
+
+    def __init__(self, bootstrap_servers: str = 'localhost:9092', topic: str = 'parkingstream'):
+        # Configure logging
+        logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+        # Initialize Kafka consumer
+        self.consumer = KafkaConsumer(
+            topic,
+            bootstrap_servers=bootstrap_servers,
+            value_deserializer=lambda m: json.loads(m.decode('utf-8')),
+            auto_offset_reset='latest',
+            enable_auto_commit=True,
+            group_id='parking-group'
+        )
+
+        # Initialize CluStream model
+        self.clustream = CluStream(max_micro_clusters=3, distance_threshold=100)
+
+        # Define colors for clusters
+        self.cluster_colors = ['blue', 'green', 'purple']
+
+        # Initialize counters and storage
+        self.sample_count = 0
+        self.recent_events = []
+        self.max_recent_events = 100
+
+        # Set up data directory
+        self.data_dir = './src/PrescriptiveAnalysis2/streamlit_data/'
+        self.plot_dir = os.path.join(self.data_dir, 'plots')
+        self.events_dir = os.path.join(self.data_dir, 'events')
+        
+        # Create directories
+        os.makedirs(self.data_dir, exist_ok=True)
+        os.makedirs(self.plot_dir, exist_ok=True)
+        os.makedirs(self.events_dir, exist_ok=True)
+
+        # Last update timestamps
+        self.last_plot_update = 0
+        self.last_events_update = 0
+
+    def preprocess(self, data: dict) -> list:
+        """Preprocess raw event data into model features."""
+        return [data['time_of_day'], data['occupancy']]
+
+    def process_event(self, data: dict):
+        """Process a single parking event."""
+        try:
+            # Preprocess data
+            point = self.preprocess(data)
+            timestamp = data['time_of_day']
+
+            # Update CluStream model
+            self.clustream.update(point, timestamp)
+            self.sample_count += 1
+
+            # Determine closest cluster
+            cluster_centers = np.array(self.clustream.get_clusters())
+            if len(cluster_centers) > 0:
+                distances = [np.linalg.norm(np.array(point) - c) for c in cluster_centers]
+                closest_cluster = np.argmin(distances)
+                cluster_label = closest_cluster
+            else:
+                cluster_label = -1  # No clusters yet
+
+            # Create readable event record
+            event_record = {
+                "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                "time_of_day": data["time_of_day"],
+                "occupancy": data["occupancy"],
+                "cluster": cluster_label
+            }
+
+            # Add to recent events
+            self.recent_events.append(event_record)
+            self.recent_events = self.recent_events[-self.max_recent_events:]  # Keep only most recent
+
+            # Create pandas DataFrame for events
+            df = pd.DataFrame(self.recent_events)
+
+            # Update event data file every 5 processed events
+            if self.sample_count % 5 == 0:
+                current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
+                csv_path = os.path.join(self.events_dir, f"events_{current_time}.csv")
+                df.to_csv(csv_path, index=False)
+                self.logger.info(f"Saved events to {csv_path}")
+                self.last_events_update = time.time()
+
+            # Update cluster visualization every 20 events
+            if self.sample_count % 20 == 0:
+                self.update_cluster_visualization()
+
+            self.logger.info(f"Processed event: time_of_day={data['time_of_day']}, occupancy={data['occupancy']}, cluster={cluster_label}")
+
+        except Exception as e:
+            self.logger.error(f"Error processing message: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+
+    def update_cluster_visualization(self):
+        """Generate and save cluster visualization plot."""
+        try:
+            # Prepare data
+            X = np.array([[e['time_of_day'], e['occupancy']] for e in self.recent_events])
+            cluster_centers = np.array(self.clustream.get_clusters())
+
+            # Assign colors based on closest cluster
+            point_colors = []
+            for p in X:
+                if len(cluster_centers) > 0:
+                    distances = [np.linalg.norm(p - c) for c in cluster_centers]
+                    closest_cluster = np.argmin(distances)
+                    point_colors.append(self.cluster_colors[closest_cluster])
+                else:
+                    point_colors.append('gray')  # Default color if no clusters
+
+            # Create plot
+            plt.clf()
+            if len(X) > 0:
+                plt.scatter(X[:, 0], X[:, 1], color=point_colors, alpha=0.5, label='Streamed Points')
+            if len(cluster_centers) > 0:
+                plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1], color='red', s=200, marker='X', label='Micro-Clusters')
+
+            plt.xlabel("Time of Day (minutes)")
+            plt.ylabel("Occupancy (%)")
+            plt.title("CluStream: Streaming Micro-Clustering")
+            plt.legend()
+
+            # Save plot
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            plot_path = os.path.join(self.plot_dir, f"plot_{timestamp}.png")
+            plt.savefig(plot_path)
+            plt.close()
+
+            self.logger.info(f"Cluster visualization saved to {plot_path}")
+            self.last_plot_update = time.time()
+
+        except Exception as e:
+            self.logger.error(f"Error updating cluster visualization: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+
+    def run(self):
+        """Start consuming messages and processing events."""
+        self.logger.info("Starting parking event consumer and clustering with CluStream...")
+
+        try:
+            for message in self.consumer:
+                data = message.value
+                self.process_event(data)
+
+        except KeyboardInterrupt:
+            self.logger.info("Consumer stopped by user.")
+        except Exception as e:
+            self.logger.error(f"Unexpected error: {e}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+        finally:
+            self.consumer.close()
+            self.logger.info("Consumer connection closed.")
+
+if __name__ == "__main__":
+    consumer = Consumer()
+    consumer.run()
+
+
diff --git a/src/PrescriptiveAnalysis2/backend/producer.py b/src/PrescriptiveAnalysis2/backend/producer.py
new file mode 100644
index 0000000..aa230e6
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/backend/producer.py
@@ -0,0 +1,91 @@
+import json
+import random
+import time
+import logging
+from kafka import KafkaProducer
+
+class Producer:
+    """Kafka producer for simulated shopping event data."""
+
+    def __init__(self, bootstrap_servers: str = 'localhost:9092'):
+        # Configure logging
+        logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+        # Initialize Kafka producer
+        self.producer = KafkaProducer(
+            bootstrap_servers=bootstrap_servers,
+            value_serializer=lambda v: json.dumps(v).encode('utf-8')
+        )
+
+        # Parameters
+        self.user_ids = [f"user_{i:04}" for i in range(1, 201)]
+        self.product_categories = ['electronics', 'fashion', 'home', 'books', 'toys']
+        self.event_types = ['view', 'click', 'add_to_cart']
+
+        # Track per-user purchase frequency
+        self.user_purchase_counts = {uid: random.randint(0, 30) for uid in self.user_ids}
+
+    def generate_event(self) -> dict:
+        """Generate a simulated shopping event."""
+        user_id = random.choice(self.user_ids)
+        product_category = random.choice(self.product_categories)
+        event_type = random.choice(self.event_types)
+        session_duration = round(random.uniform(1, 20), 2)
+        user_purchases = self.user_purchase_counts[user_id]
+
+        # Rule-based purchase logic
+        purchase = int(
+            (event_type == 'view' and session_duration > 12 and user_purchases > 10)
+            or (event_type == 'add_to_cart' and session_duration > 5 and user_purchases > 4)
+            or (event_type == 'click' and session_duration > 7 and user_purchases > 6)
+        )
+
+        # Session duration categorization
+        if session_duration <= 6:
+            sd = 'low'
+        elif 6 < session_duration <= 10:
+            sd = 'medium'
+        else:
+            sd = 'high'
+
+        # User purchases categorization
+        if user_purchases <= 5:
+            up = 'very few'
+        elif 5 < user_purchases <= 10:
+            up = 'few'
+        elif 10 < user_purchases <= 20:
+            up = 'medium'
+        else:
+            up = 'high'
+
+        return {
+            "user_id": user_id,
+            "event_type": event_type,
+            "session_duration": sd,
+            "product_category": product_category,
+            "user_total_purchases": up,
+            "purchase": purchase
+        }
+
+    def run(self):
+        """Continuously produce shopping events."""
+        self.logger.info("Starting shopping data producer...")
+        try:
+            while True:
+                event = self.generate_event()
+                self.producer.send('shopping_events', event)
+                self.logger.info(f"Sent: {event}")
+                time.sleep(2)  # Simulate near real-time events
+
+        except KeyboardInterrupt:
+            self.logger.info("Producer stopped by user.")
+        except Exception as e:
+            self.logger.error(f"Unexpected error: {e}")
+        finally:
+            self.producer.close()
+            self.logger.info("Producer connection closed.")
+
+if __name__ == "__main__":
+    producer = Producer()
+    producer.run()
diff --git a/src/PrescriptiveAnalysis2/backend/producer_clu.py b/src/PrescriptiveAnalysis2/backend/producer_clu.py
new file mode 100644
index 0000000..7ec42fd
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/backend/producer_clu.py
@@ -0,0 +1,35 @@
+from kafka import KafkaProducer
+import json
+import time
+import random
+from datetime import datetime
+
+producer = KafkaProducer(
+    bootstrap_servers='localhost:9092',
+    value_serializer=lambda v: json.dumps(v).encode('utf-8')
+)
+
+topic = 'parkingstream'
+
+def generate_occupancy(time_min):
+    """Generate occupancy percentage based on time."""
+    if 420 <= time_min <= 600:     # 7 AM – 10 AM
+        return random.randint(70, 90)
+    elif 720 <= time_min <= 900:   # 12 PM – 3 PM
+        return random.randint(85, 100)
+    elif 1080 <= time_min <= 1320: # 6 PM – 10 PM
+        return random.randint(40, 60)
+    else:
+        return random.randint(5, 30)
+
+time_of_day = 0
+
+while time_of_day < 1440:
+    data = {
+        'time_of_day': time_of_day,
+        'occupancy': generate_occupancy(time_of_day)
+    }
+    print(f"Produced: {data}")
+    producer.send(topic, value=data)
+    time.sleep(2)  # Simulate streaming every second (10 minutes in simulation)
+    time_of_day += 10
\ No newline at end of file
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diff --git a/src/PrescriptiveAnalysis2/backend/visualisations/hoeffding_tree_80.png b/src/PrescriptiveAnalysis2/backend/visualisations/hoeffding_tree_80.png
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diff --git a/src/PrescriptiveAnalysis2/frontend/clustream_ui.py b/src/PrescriptiveAnalysis2/frontend/clustream_ui.py
new file mode 100644
index 0000000..f24fadc
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/frontend/clustream_ui.py
@@ -0,0 +1,242 @@
+import streamlit as st
+import subprocess
+import time
+import os
+import glob
+import shutil
+from PIL import Image
+import pandas as pd
+
+# Configuration
+KAFKA_HOME = "C:/kafka_2.13-3.9.0"  # Update this path if needed
+TOPIC_NAME = "parkingstream"
+
+# Paths (keep relative as in consumer code)
+DATA_DIR = './src/PrescriptiveAnalysis2/streamlit_data/'
+PLOT_DIR = os.path.join(DATA_DIR, 'plots')
+EVENTS_DIR = os.path.join(DATA_DIR, 'events')
+
+# Create directories if they don't exist
+os.makedirs(PLOT_DIR, exist_ok=True)
+os.makedirs(EVENTS_DIR, exist_ok=True)
+
+# Kafka commands
+ZK_CMD = f'{KAFKA_HOME}/bin/windows/zookeeper-server-start.bat {KAFKA_HOME}/config/zookeeper.properties'
+KAFKA_CMD = f'{KAFKA_HOME}/bin/windows/kafka-server-start.bat {KAFKA_HOME}/config/server.properties'
+TOPIC_CMD = f'{KAFKA_HOME}/bin/windows/kafka-topics.bat --create --topic {TOPIC_NAME} --bootstrap-server localhost:9092 --replication-factor 1 --partitions 4'
+
+# Streamlit UI setup
+st.set_page_config(page_title="CluStream Clustering", layout="wide")
+st.title("CluStream Clustering")
+
+# Global variables to track processes
+processes = {
+    'zookeeper': None,
+    'kafka_server': None,
+    'producer': None,
+    'consumer': None
+}
+
+# Initialize session state
+if "running" not in st.session_state:
+    st.session_state.running = False
+if "last_plot_refresh" not in st.session_state:
+    st.session_state.last_plot_refresh = time.time()
+if "last_data_refresh" not in st.session_state:
+    st.session_state.last_data_refresh = time.time()
+
+def clear_directories():
+    """Clear contents of both PLOT_DIR and EVENTS_DIR"""
+    try:
+        # Clear plot directory
+        for filename in os.listdir(PLOT_DIR):
+            file_path = os.path.join(PLOT_DIR, filename)
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                st.error(f'Failed to delete {file_path}. Reason: {e}')
+        
+        # Clear events directory
+        for filename in os.listdir(EVENTS_DIR):
+            file_path = os.path.join(EVENTS_DIR, filename)
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                st.error(f'Failed to delete {file_path}. Reason: {e}')
+        
+        st.success("Cleared all plot and event data files")
+    except Exception as e:
+        st.error(f"Error clearing directories: {e}")
+
+def launch_background(command, process_key=None):
+    """Launch a background process and store reference if needed"""
+    if isinstance(command, list):
+        process = subprocess.Popen(command)
+    else:
+        process = subprocess.Popen(command, shell=True)
+    if process_key:
+        processes[process_key] = process
+    return process
+
+def start_zookeeper():
+    """Start Zookeeper service"""
+    st.info("Starting Zookeeper...")
+    processes['zookeeper'] = launch_background(ZK_CMD)
+    time.sleep(5)
+    st.success("Zookeeper started successfully")
+
+def start_kafka_server():
+    """Start Kafka server"""
+    st.info("Starting Kafka Server...")
+    processes['kafka_server'] = launch_background(KAFKA_CMD)
+    time.sleep(10)
+    st.success("Kafka Server started successfully")
+
+def create_topic():
+    """Create Kafka topic"""
+    st.info("Creating Kafka topic...")
+    try:
+        result = subprocess.run(TOPIC_CMD, shell=True, capture_output=True, text=True)
+        if result.returncode == 0:
+            st.success(f"Topic '{TOPIC_NAME}' created successfully")
+        else:
+            st.warning(f"Topic creation may have failed: {result.stderr}")
+    except Exception as e:
+        st.warning(f"Topic creation may have failed: {e}")
+    time.sleep(2)
+
+def start_producer():
+    """Start the producer script"""
+    st.info("Starting Kafka Producer...")
+    processes['producer'] = launch_background(['python', 'src/PrescriptiveAnalysis2/backend/producer_clu.py'])
+    st.success("Producer started successfully")
+
+def start_consumer():
+    """Start the consumer script"""
+    st.info("Starting Kafka Consumer...")
+    processes['consumer'] = launch_background(['python', 'src/PrescriptiveAnalysis2/backend/consumer_clu.py'])
+    st.success("Consumer started successfully")
+
+def stop_services():
+    """Stop all running services and clear data directories"""
+    st.session_state.running = False
+    
+    # First stop all services
+    for name, process in processes.items():
+        if process:
+            try:
+                process.terminate()
+                process.wait(timeout=5)
+            except:
+                pass
+            processes[name] = None
+    # Then clear the data directories
+    clear_directories()
+    
+    st.success("All services stopped and data cleared successfully")
+
+def start_all_services():
+    """Start the complete pipeline"""
+    if not st.session_state.running:
+        st.session_state.running = True
+        start_zookeeper()
+        start_kafka_server()
+        create_topic()
+        start_producer()
+        start_consumer()
+
+# UI Controls
+st.subheader("")
+col1, col2 = st.columns(2)
+with col1:
+    if st.button("Start Full Pipeline", disabled=st.session_state.running):
+        start_all_services()
+with col2:
+    if st.button("Stop Pipeline", disabled=not st.session_state.running):
+        stop_services()
+
+# Layout containers
+col1, col2 = st.columns([2, 3])
+
+# Event data display container
+with col1:
+    st.subheader("📈 Recent Events")
+    events_placeholder = st.empty()
+
+# Cluster visualization container
+with col2:
+    st.subheader("📊 CluStream Cluster Visualization")
+    plot_placeholder = st.empty()
+
+def get_recent_event_data():
+    """Get the most recent event data from CSV files"""
+    try:
+        csv_files = glob.glob(os.path.join(EVENTS_DIR, "*.csv"))
+        if not csv_files:
+            st.warning("No event CSV files found in ./streamlit_data/events/")
+            return None
+        csv_files.sort(key=os.path.getmtime, reverse=True)
+        df = pd.read_csv(csv_files[0])
+        return df
+    except Exception as e:
+        st.error(f"Error loading event data: {e}")
+        return None
+
+def get_latest_plot_image():
+    """Get the most recent cluster visualization image"""
+    try:
+        png_files = glob.glob(os.path.join(PLOT_DIR, "*.png"))
+        if not png_files:
+            st.warning("No plot PNG files found in ./streamlit_data/plots/")
+            return None
+        png_files.sort(key=os.path.getmtime, reverse=True)
+        return Image.open(png_files[0])
+    except Exception as e:
+        st.error(f"Error loading plot image: {e}")
+        return None
+
+def update_status():
+    """Update pipeline status in sidebar"""
+    status_text = f"""
+    - Zookeeper: {'✅ Running' if processes['zookeeper'] and processes['zookeeper'].poll() is None else '❌ Stopped'}
+    - Kafka Server: {'✅ Running' if processes['kafka_server'] and processes['kafka_server'].poll() is None else '❌ Stopped'}
+    - Producer: {'✅ Running' if processes['producer'] and processes['producer'].poll() is None else '❌ Stopped'}
+    - Consumer: {'✅ Running' if processes['consumer'] and processes['consumer'].poll() is None else '❌ Stopped'}
+    """
+    st.sidebar.markdown(status_text)
+
+# Sidebar for status
+st.sidebar.subheader("Pipeline Status")
+
+# Main UI logic
+if st.session_state.running:
+    # Update UI when pipeline is running
+    update_status()
+    
+    # Update event data
+    df = get_recent_event_data()
+    if df is not None:
+        events_placeholder.dataframe(df, use_container_width=True)
+    else:
+        events_placeholder.warning("Waiting for event data...")
+    
+    # Update cluster visualization
+    img = get_latest_plot_image()
+    if img:
+        plot_placeholder.image(img, use_column_width=True)
+    else:
+        plot_placeholder.warning("Waiting for cluster visualization...")
+    
+    time.sleep(2)  # Update every 2 seconds
+    st.rerun()  # Refresh UI
+else:
+    # Display placeholders when pipeline is not running
+    update_status()
+    events_placeholder.info("Start the pipeline to see recent events.")
+    plot_placeholder.info("Start the pipeline to see the cluster visualization.")
\ No newline at end of file
diff --git a/src/PrescriptiveAnalysis2/frontend/hoeffding_ui.py b/src/PrescriptiveAnalysis2/frontend/hoeffding_ui.py
new file mode 100644
index 0000000..90b60f2
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/frontend/hoeffding_ui.py
@@ -0,0 +1,267 @@
+import streamlit as st
+import subprocess
+import time
+import os
+import glob
+import shutil
+from PIL import Image
+import pandas as pd
+
+# Configuration
+KAFKA_HOME = "C:/kafka_2.13-3.9.0"  # Update this path if needed
+TOPIC_NAME = "shopping_events"
+
+# Paths (keep relative as in original)
+DATA_DIR = './src/PrescriptiveAnalysis2/streamlit_data/'
+TREE_DIR = os.path.join(DATA_DIR, 'tree')
+EVENTS_DIR = os.path.join(DATA_DIR, 'hoeff_events')
+
+# Create directories if they don't exist
+os.makedirs(TREE_DIR, exist_ok=True)
+os.makedirs(EVENTS_DIR, exist_ok=True)
+
+# Kafka commands
+ZK_CMD = f'{KAFKA_HOME}/bin/windows/zookeeper-server-start.bat {KAFKA_HOME}/config/zookeeper.properties'
+KAFKA_CMD = f'{KAFKA_HOME}/bin/windows/kafka-server-start.bat {KAFKA_HOME}/config/server.properties'
+TOPIC_CMD = f'{KAFKA_HOME}/bin/windows/kafka-topics.bat --create --topic {TOPIC_NAME} --bootstrap-server localhost:9092 --replication-factor 1 --partitions 4'
+
+# Streamlit UI setup
+st.set_page_config(page_title="Hoeffding Tree Stream", layout="wide")
+st.title("Hoeffding Tree")
+
+# Global variables to track processes
+processes = {
+    'zookeeper': None,
+    'kafka_server': None,
+    'producer': None,
+    'consumer': None
+}
+
+# Initialize session state
+if "running" not in st.session_state:
+    st.session_state.running = False
+if "last_tree_refresh" not in st.session_state:
+    st.session_state.last_tree_refresh = time.time()
+if "last_data_refresh" not in st.session_state:
+    st.session_state.last_data_refresh = time.time()
+
+def clear_directories():
+    """Clear contents of TREE_DIR and EVENTS_DIR"""
+    try:
+        # Clear tree directory
+        for filename in os.listdir(TREE_DIR):
+            file_path = os.path.join(TREE_DIR, filename)
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                st.error(f'Failed to delete {file_path}. Reason: {e}')
+        
+        # Clear events directory
+        for filename in os.listdir(EVENTS_DIR):
+            file_path = os.path.join(EVENTS_DIR, filename)
+            try:
+                if os.path.isfile(file_path) or os.path.islink(file_path):
+                    os.unlink(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                st.error(f'Failed to delete {file_path}. Reason: {e}')
+        
+        st.success("Cleared all data files")
+    except Exception as e:
+        st.error(f"Error clearing directories: {e}")
+
+def launch_background(command, process_key=None):
+    """Launch a background process and store reference if needed"""
+    if isinstance(command, list):
+        process = subprocess.Popen(command)
+    else:
+        process = subprocess.Popen(command, shell=True)
+    if process_key:
+        processes[process_key] = process
+    return process
+
+def start_zookeeper():
+    """Start Zookeeper service"""
+    st.info("Starting Zookeeper...")
+    processes['zookeeper'] = launch_background(ZK_CMD)
+    time.sleep(5)
+    st.success("Zookeeper started successfully")
+
+def start_kafka_server():
+    """Start Kafka server"""
+    st.info("Starting Kafka Server...")
+    processes['kafka_server'] = launch_background(KAFKA_CMD)
+    time.sleep(10)
+    st.success("Kafka Server started successfully")
+
+def create_topic():
+    """Create Kafka topic"""
+    st.info("Creating Kafka topic...")
+    try:
+        result = subprocess.run(TOPIC_CMD, shell=True, capture_output=True, text=True)
+        if result.returncode == 0:
+            st.success(f"Topic '{TOPIC_NAME}' created successfully")
+        else:
+            st.warning(f"Topic creation may have failed: {result.stderr}")
+    except Exception as e:
+        st.warning(f"Topic creation may have failed: {e}")
+    time.sleep(2)
+
+def start_producer():
+    """Start the producer script"""
+    st.info("Starting Kafka Producer...")
+    processes['producer'] = launch_background(['python', 'src/PrescriptiveAnalysis2/backend/producer.py'])
+    st.success("Producer started successfully")
+
+def start_consumer():
+    """Start the consumer script"""
+    st.info("Starting Kafka Consumer...")
+    processes['consumer'] = launch_background(['python', 'src/PrescriptiveAnalysis2/backend/consumer.py'])
+    st.success("Consumer started successfully")
+
+def stop_services():
+    """Stop all running services and clear data"""
+    st.session_state.running = False
+    for name, process in processes.items():
+        if process:
+            try:
+                process.terminate()
+                process.wait(timeout=5)
+            except:
+                pass
+            processes[name] = None
+    
+    # Clear the data directories
+    clear_directories()
+    st.success("All services stopped and data cleared successfully")
+
+def start_all_services():
+    """Start the complete pipeline"""
+    if not st.session_state.running:
+        st.session_state.running = True
+        start_zookeeper()
+        start_kafka_server()
+        create_topic()
+        start_producer()
+        start_consumer()
+
+# UI Controls
+st.subheader("")
+col1, col2 = st.columns(2)
+with col1:
+    if st.button("Start Full Pipeline", disabled=st.session_state.running):
+        start_all_services()
+with col2:
+    if st.button("Stop Pipeline", disabled=not st.session_state.running):
+        stop_services()
+
+# Layout containers
+col1, col2 = st.columns([2, 3])
+
+# Event data display container
+with col1:
+    st.subheader("📈 Recent Events")
+    events_placeholder = st.empty()
+
+# Tree visualization and stats containers
+with col2:
+    st.subheader("🌳 Hoeffding Tree Visualization")
+    tree_placeholder = st.empty()
+    st.subheader("📉 Model Accuracy")
+    accuracy_placeholder = st.empty()
+
+def get_recent_event_data():
+    """Get the most recent event data from CSV files"""
+    try:
+        csv_files = glob.glob(os.path.join(EVENTS_DIR, "*.csv"))
+        if not csv_files:
+            st.warning("No event CSV files found in ./streamlit_data/hoeff_events/")
+            return None
+        csv_files.sort(key=os.path.getmtime, reverse=True)
+        df = pd.read_csv(csv_files[0])
+        return df
+    except Exception as e:
+        st.error(f"Error loading event data: {e}")
+        return None
+
+def get_latest_tree_image():
+    """Get the most recent tree visualization image"""
+    try:
+        png_files = glob.glob(os.path.join(TREE_DIR, "*.png"))
+        if not png_files:
+            st.warning("No tree PNG files found in ./streamlit_data/tree/")
+            return None
+        png_files.sort(key=os.path.getmtime, reverse=True)
+        return Image.open(png_files[0])
+    except Exception as e:
+        st.error(f"Error loading tree image: {e}")
+        return None
+
+def get_latest_accuracy():
+    """Get the latest accuracy from the accuracy.txt file"""
+    try:
+        acc_file = os.path.join(DATA_DIR, 'accuracy.txt')
+        if not os.path.exists(acc_file):
+            st.warning("Accuracy file not found at ./streamlit_data/accuracy.txt")
+            return None
+        with open(acc_file, 'r') as f:
+            acc_text = f.read().strip()
+            if acc_text:
+                return float(acc_text)
+        return None
+    except Exception as e:
+        st.error(f"Error reading accuracy: {e}")
+        return None
+
+def update_status():
+    """Update pipeline status in sidebar"""
+    status_text = f"""
+    - Zookeeper: {'✅ Running' if processes['zookeeper'] and processes['zookeeper'].poll() is None else '❌ Stopped'}
+    - Kafka Server: {'✅ Running' if processes['kafka_server'] and processes['kafka_server'].poll() is None else '❌ Stopped'}
+    - Producer: {'✅ Running' if processes['producer'] and processes['producer'].poll() is None else '❌ Stopped'}
+    - Consumer: {'✅ Running' if processes['consumer'] and processes['consumer'].poll() is None else '❌ Stopped'}
+    """
+    st.sidebar.markdown(status_text)
+
+# Sidebar for status
+st.sidebar.subheader("Pipeline Status")
+#update_status()
+
+# Main UI logic
+if st.session_state.running:
+    # Update UI when pipeline is running
+    update_status()
+    
+    # Update event data
+    df = get_recent_event_data()
+    if df is not None:
+        events_placeholder.dataframe(df, use_container_width=True)
+    else:
+        events_placeholder.warning("Waiting for event data...")
+    
+    # Update tree visualization
+    img = get_latest_tree_image()
+    if img:
+        tree_placeholder.image(img, use_column_width=True)
+    else:
+        tree_placeholder.warning("Waiting for tree visualization...")
+  
+    # Update accuracy
+    acc = get_latest_accuracy()
+    if acc is not None:
+        accuracy_placeholder.metric("Model Accuracy", f"{acc:.4f}")
+    else:
+        accuracy_placeholder.warning("Waiting for accuracy data...")
+    
+    time.sleep(2)  # Update every 2 seconds
+    st.rerun()  # Refresh UI
+else:
+    # Display placeholders when pipeline is not running
+    update_status()
+    events_placeholder.info("Start the pipeline to see recent events.")
+    tree_placeholder.info("Start the pipeline to see the tree visualization.")
+    accuracy_placeholder.info("Start the pipeline to see model accuracy.")
\ No newline at end of file
diff --git a/src/PrescriptiveAnalysis2/frontend/main_ui.py b/src/PrescriptiveAnalysis2/frontend/main_ui.py
new file mode 100644
index 0000000..a63c598
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/frontend/main_ui.py
@@ -0,0 +1,19 @@
+import streamlit as st
+import subprocess
+
+st.set_page_config(page_title="PRESCRIPTIVE ANALYSIS-2 : STREAMING DATA ANALYSIS")
+
+st.title("PRESCRIPTIVE ANALYSIS-2 : STREAMING DATA ANALYSIS")
+st.markdown("### Select a module to launch:")
+
+option = st.selectbox("Choose Module", ["-- Select --", "Hoeffding Tree", "CluStream"])
+
+if st.button("Run Selected Module"):
+    if option == "Hoeffding Tree":
+        st.success("Launching Hoeffding Tree UI...")
+        subprocess.Popen(["streamlit", "run", "./src/PrescriptiveAnalysis2/frontend/hoeffding_ui.py"])
+    elif option == "CluStream":
+        st.success("Launching Clustream UI...")
+        subprocess.Popen(["streamlit", "run", "./src/PrescriptiveAnalysis2/frontend/clustream_ui.py"])
+    else:
+        st.warning("Please select a valid module.")
diff --git a/src/PrescriptiveAnalysis2/streamlit_data/accuracy.txt b/src/PrescriptiveAnalysis2/streamlit_data/accuracy.txt
new file mode 100644
index 0000000..9c1f7d3
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/accuracy.txt
@@ -0,0 +1 @@
+0.7988826815642458
\ No newline at end of file
diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005756.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005756.csv
new file mode 100644
index 0000000..7c7acf4
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005756.csv
@@ -0,0 +1,101 @@
+timestamp,time_of_day,occupancy,cluster
+2025-04-17 00:54:37,50,26,0
+2025-04-17 00:54:39,60,24,0
+2025-04-17 00:54:41,70,22,0
+2025-04-17 00:54:43,80,19,0
+2025-04-17 00:54:45,90,18,0
+2025-04-17 00:54:47,100,12,0
+2025-04-17 00:54:49,110,24,0
+2025-04-17 00:54:51,120,14,0
+2025-04-17 00:54:53,130,9,0
+2025-04-17 00:54:55,140,7,0
+2025-04-17 00:54:57,150,26,0
+2025-04-17 00:54:59,160,6,0
+2025-04-17 00:55:01,170,8,0
+2025-04-17 00:55:03,180,25,0
+2025-04-17 00:55:05,190,15,1
+2025-04-17 00:55:07,200,27,1
+2025-04-17 00:55:09,210,6,1
+2025-04-17 00:55:11,220,29,1
+2025-04-17 00:55:13,230,25,1
+2025-04-17 00:55:15,240,8,1
+2025-04-17 00:55:17,250,20,1
+2025-04-17 00:55:19,260,23,1
+2025-04-17 00:55:21,270,21,1
+2025-04-17 00:55:23,280,9,1
+2025-04-17 00:55:25,290,24,1
+2025-04-17 00:55:27,300,5,1
+2025-04-17 00:55:29,310,7,1
+2025-04-17 00:55:31,320,30,1
+2025-04-17 00:55:33,330,19,1
+2025-04-17 00:55:35,340,14,1
+2025-04-17 00:55:37,350,24,1
+2025-04-17 00:55:39,360,25,1
+2025-04-17 00:55:42,370,11,1
+2025-04-17 00:55:43,380,10,2
+2025-04-17 00:55:45,390,25,2
+2025-04-17 00:55:47,400,22,2
+2025-04-17 00:55:49,410,14,2
+2025-04-17 00:55:51,420,80,2
+2025-04-17 00:55:53,430,84,2
+2025-04-17 00:55:55,440,87,2
+2025-04-17 00:55:57,450,72,2
+2025-04-17 00:55:59,460,74,2
+2025-04-17 00:56:02,470,77,2
+2025-04-17 00:56:04,480,84,2
+2025-04-17 00:56:06,490,88,2
+2025-04-17 00:56:08,500,77,2
+2025-04-17 00:56:10,510,87,2
+2025-04-17 00:56:12,520,90,2
+2025-04-17 00:56:14,530,79,2
+2025-04-17 00:56:16,540,79,2
+2025-04-17 00:56:18,550,85,2
+2025-04-17 00:56:20,560,86,2
+2025-04-17 00:56:22,570,77,0
+2025-04-17 00:56:24,580,76,0
+2025-04-17 00:56:26,590,72,0
+2025-04-17 00:56:28,600,81,0
+2025-04-17 00:56:30,610,19,0
+2025-04-17 00:56:32,620,10,0
+2025-04-17 00:56:34,630,6,0
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005806.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005806.csv
new file mode 100644
index 0000000..38101b0
--- /dev/null
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005816.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005816.csv
new file mode 100644
index 0000000..e380aa7
--- /dev/null
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005826.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005826.csv
new file mode 100644
index 0000000..e4373fd
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005836.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005836.csv
new file mode 100644
index 0000000..e380d30
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005846.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005846.csv
new file mode 100644
index 0000000..3b5ae16
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005846.csv
@@ -0,0 +1,101 @@
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005856.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005856.csv
new file mode 100644
index 0000000..97adafe
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005856.csv
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005906.csv b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005906.csv
new file mode 100644
index 0000000..2ca7e4d
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/events/events_20250417_005906.csv
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diff --git a/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005743.csv b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005743.csv
new file mode 100644
index 0000000..b69519f
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005743.csv
@@ -0,0 +1,91 @@
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+2025-04-17 00:54:47,user_0099,click,high,fashion,medium,Yes,Yes
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+2025-04-17 00:54:51,user_0086,view,low,books,high,No,Yes
+2025-04-17 00:54:53,user_0170,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:54:55,user_0109,add_to_cart,low,electronics,medium,Yes,No
+2025-04-17 00:54:57,user_0140,click,high,home,high,Yes,Yes
+2025-04-17 00:54:59,user_0137,add_to_cart,low,home,medium,Yes,Yes
+2025-04-17 00:55:01,user_0072,add_to_cart,low,books,few,No,Yes
+2025-04-17 00:55:03,user_0171,click,low,electronics,few,No,Yes
+2025-04-17 00:55:05,user_0050,add_to_cart,low,fashion,high,No,No
+2025-04-17 00:55:07,user_0177,add_to_cart,low,toys,very few,No,Yes
+2025-04-17 00:55:09,user_0120,click,low,fashion,very few,No,No
+2025-04-17 00:55:11,user_0026,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:55:13,user_0049,add_to_cart,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:15,user_0099,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:17,user_0154,click,medium,books,high,No,No
+2025-04-17 00:55:19,user_0143,add_to_cart,high,home,medium,Yes,No
+2025-04-17 00:55:21,user_0023,view,high,electronics,very few,No,No
+2025-04-17 00:55:23,user_0082,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:25,user_0009,add_to_cart,medium,home,medium,Yes,Yes
+2025-04-17 00:55:27,user_0196,add_to_cart,low,toys,high,No,Yes
+2025-04-17 00:55:29,user_0008,view,medium,home,very few,No,No
+2025-04-17 00:55:31,user_0111,click,high,electronics,few,Yes,No
+2025-04-17 00:55:33,user_0121,click,low,electronics,high,No,No
+2025-04-17 00:55:35,user_0192,click,high,fashion,few,Yes,No
+2025-04-17 00:55:37,user_0041,click,high,electronics,high,Yes,No
+2025-04-17 00:55:39,user_0106,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:55:41,user_0184,view,medium,books,high,No,No
+2025-04-17 00:55:43,user_0132,click,medium,fashion,high,Yes,No
+2025-04-17 00:55:45,user_0043,view,low,toys,medium,No,Yes
+2025-04-17 00:55:47,user_0006,view,high,fashion,medium,Yes,Yes
+2025-04-17 00:55:49,user_0164,view,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:51,user_0113,view,medium,fashion,medium,No,Yes
+2025-04-17 00:55:53,user_0197,add_to_cart,medium,toys,high,Yes,No
+2025-04-17 00:55:55,user_0104,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:55:57,user_0008,view,high,books,very few,No,No
+2025-04-17 00:55:59,user_0118,add_to_cart,medium,books,very few,No,No
+2025-04-17 00:56:01,user_0163,view,high,books,medium,Yes,Yes
+2025-04-17 00:56:03,user_0113,view,medium,toys,medium,No,No
+2025-04-17 00:56:05,user_0044,click,medium,toys,high,Yes,No
+2025-04-17 00:56:07,user_0087,click,high,home,medium,Yes,Yes
+2025-04-17 00:56:09,user_0179,view,medium,home,medium,No,No
+2025-04-17 00:56:11,user_0032,view,high,electronics,few,No,No
+2025-04-17 00:56:13,user_0108,add_to_cart,high,fashion,high,Yes,No
+2025-04-17 00:56:15,user_0053,add_to_cart,high,electronics,high,Yes,Yes
+2025-04-17 00:56:17,user_0012,click,low,toys,high,No,No
+2025-04-17 00:56:19,user_0019,click,low,electronics,high,No,No
+2025-04-17 00:56:21,user_0131,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:23,user_0103,add_to_cart,low,toys,medium,No,No
+2025-04-17 00:56:25,user_0022,view,medium,home,medium,No,Yes
+2025-04-17 00:56:27,user_0187,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:29,user_0106,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:31,user_0019,click,high,books,high,Yes,Yes
+2025-04-17 00:56:33,user_0044,view,high,home,high,Yes,Yes
+2025-04-17 00:56:35,user_0172,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:37,user_0096,add_to_cart,medium,electronics,medium,Yes,No
+2025-04-17 00:56:39,user_0179,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:56:41,user_0068,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:56:43,user_0173,view,low,electronics,high,No,No
+2025-04-17 00:56:45,user_0149,click,high,books,high,Yes,Yes
+2025-04-17 00:56:47,user_0076,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:49,user_0045,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:56:51,user_0086,click,high,home,high,Yes,Yes
+2025-04-17 00:56:53,user_0061,view,low,electronics,high,No,No
+2025-04-17 00:56:55,user_0118,view,low,toys,very few,No,No
+2025-04-17 00:56:57,user_0038,view,high,books,high,Yes,Yes
+2025-04-17 00:56:59,user_0098,click,low,fashion,medium,No,No
+2025-04-17 00:57:01,user_0165,click,high,electronics,high,Yes,Yes
+2025-04-17 00:57:03,user_0057,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:57:05,user_0029,view,medium,fashion,high,No,Yes
+2025-04-17 00:57:07,user_0057,view,high,toys,high,Yes,Yes
+2025-04-17 00:57:09,user_0133,click,high,home,high,Yes,Yes
+2025-04-17 00:57:11,user_0037,click,high,home,high,Yes,Yes
+2025-04-17 00:57:13,user_0143,view,medium,electronics,medium,No,Yes
+2025-04-17 00:57:15,user_0041,click,low,books,high,No,No
+2025-04-17 00:57:17,user_0007,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:19,user_0054,view,low,electronics,very few,No,No
+2025-04-17 00:57:21,user_0150,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:57:23,user_0193,click,medium,toys,high,Yes,No
+2025-04-17 00:57:25,user_0183,click,high,electronics,few,No,No
+2025-04-17 00:57:27,user_0134,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:29,user_0170,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:31,user_0113,add_to_cart,low,home,medium,No,No
+2025-04-17 00:57:33,user_0044,click,medium,home,high,Yes,Yes
+2025-04-17 00:57:35,user_0067,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:57:37,user_0146,click,high,books,few,Yes,No
+2025-04-17 00:57:39,user_0156,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:41,user_0157,view,low,books,very few,No,No
+2025-04-17 00:57:43,user_0066,add_to_cart,low,toys,very few,No,No
diff --git a/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005753.csv b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005753.csv
new file mode 100644
index 0000000..e96922d
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005753.csv
@@ -0,0 +1,96 @@
+timestamp,user_id,event_type,session_duration,product_category,user_total_purchases,purchase,prediction
+2025-04-17 00:54:45,user_0179,add_to_cart,high,fashion,medium,Yes,N/A
+2025-04-17 00:54:47,user_0099,click,high,fashion,medium,Yes,Yes
+2025-04-17 00:54:49,user_0020,add_to_cart,low,books,medium,No,Yes
+2025-04-17 00:54:51,user_0086,view,low,books,high,No,Yes
+2025-04-17 00:54:53,user_0170,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:54:55,user_0109,add_to_cart,low,electronics,medium,Yes,No
+2025-04-17 00:54:57,user_0140,click,high,home,high,Yes,Yes
+2025-04-17 00:54:59,user_0137,add_to_cart,low,home,medium,Yes,Yes
+2025-04-17 00:55:01,user_0072,add_to_cart,low,books,few,No,Yes
+2025-04-17 00:55:03,user_0171,click,low,electronics,few,No,Yes
+2025-04-17 00:55:05,user_0050,add_to_cart,low,fashion,high,No,No
+2025-04-17 00:55:07,user_0177,add_to_cart,low,toys,very few,No,Yes
+2025-04-17 00:55:09,user_0120,click,low,fashion,very few,No,No
+2025-04-17 00:55:11,user_0026,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:55:13,user_0049,add_to_cart,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:15,user_0099,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:17,user_0154,click,medium,books,high,No,No
+2025-04-17 00:55:19,user_0143,add_to_cart,high,home,medium,Yes,No
+2025-04-17 00:55:21,user_0023,view,high,electronics,very few,No,No
+2025-04-17 00:55:23,user_0082,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:25,user_0009,add_to_cart,medium,home,medium,Yes,Yes
+2025-04-17 00:55:27,user_0196,add_to_cart,low,toys,high,No,Yes
+2025-04-17 00:55:29,user_0008,view,medium,home,very few,No,No
+2025-04-17 00:55:31,user_0111,click,high,electronics,few,Yes,No
+2025-04-17 00:55:33,user_0121,click,low,electronics,high,No,No
+2025-04-17 00:55:35,user_0192,click,high,fashion,few,Yes,No
+2025-04-17 00:55:37,user_0041,click,high,electronics,high,Yes,No
+2025-04-17 00:55:39,user_0106,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:55:41,user_0184,view,medium,books,high,No,No
+2025-04-17 00:55:43,user_0132,click,medium,fashion,high,Yes,No
+2025-04-17 00:55:45,user_0043,view,low,toys,medium,No,Yes
+2025-04-17 00:55:47,user_0006,view,high,fashion,medium,Yes,Yes
+2025-04-17 00:55:49,user_0164,view,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:51,user_0113,view,medium,fashion,medium,No,Yes
+2025-04-17 00:55:53,user_0197,add_to_cart,medium,toys,high,Yes,No
+2025-04-17 00:55:55,user_0104,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:55:57,user_0008,view,high,books,very few,No,No
+2025-04-17 00:55:59,user_0118,add_to_cart,medium,books,very few,No,No
+2025-04-17 00:56:01,user_0163,view,high,books,medium,Yes,Yes
+2025-04-17 00:56:03,user_0113,view,medium,toys,medium,No,No
+2025-04-17 00:56:05,user_0044,click,medium,toys,high,Yes,No
+2025-04-17 00:56:07,user_0087,click,high,home,medium,Yes,Yes
+2025-04-17 00:56:09,user_0179,view,medium,home,medium,No,No
+2025-04-17 00:56:11,user_0032,view,high,electronics,few,No,No
+2025-04-17 00:56:13,user_0108,add_to_cart,high,fashion,high,Yes,No
+2025-04-17 00:56:15,user_0053,add_to_cart,high,electronics,high,Yes,Yes
+2025-04-17 00:56:17,user_0012,click,low,toys,high,No,No
+2025-04-17 00:56:19,user_0019,click,low,electronics,high,No,No
+2025-04-17 00:56:21,user_0131,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:23,user_0103,add_to_cart,low,toys,medium,No,No
+2025-04-17 00:56:25,user_0022,view,medium,home,medium,No,Yes
+2025-04-17 00:56:27,user_0187,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:29,user_0106,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:31,user_0019,click,high,books,high,Yes,Yes
+2025-04-17 00:56:33,user_0044,view,high,home,high,Yes,Yes
+2025-04-17 00:56:35,user_0172,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:37,user_0096,add_to_cart,medium,electronics,medium,Yes,No
+2025-04-17 00:56:39,user_0179,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:56:41,user_0068,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:56:43,user_0173,view,low,electronics,high,No,No
+2025-04-17 00:56:45,user_0149,click,high,books,high,Yes,Yes
+2025-04-17 00:56:47,user_0076,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:49,user_0045,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:56:51,user_0086,click,high,home,high,Yes,Yes
+2025-04-17 00:56:53,user_0061,view,low,electronics,high,No,No
+2025-04-17 00:56:55,user_0118,view,low,toys,very few,No,No
+2025-04-17 00:56:57,user_0038,view,high,books,high,Yes,Yes
+2025-04-17 00:56:59,user_0098,click,low,fashion,medium,No,No
+2025-04-17 00:57:01,user_0165,click,high,electronics,high,Yes,Yes
+2025-04-17 00:57:03,user_0057,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:57:05,user_0029,view,medium,fashion,high,No,Yes
+2025-04-17 00:57:07,user_0057,view,high,toys,high,Yes,Yes
+2025-04-17 00:57:09,user_0133,click,high,home,high,Yes,Yes
+2025-04-17 00:57:11,user_0037,click,high,home,high,Yes,Yes
+2025-04-17 00:57:13,user_0143,view,medium,electronics,medium,No,Yes
+2025-04-17 00:57:15,user_0041,click,low,books,high,No,No
+2025-04-17 00:57:17,user_0007,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:19,user_0054,view,low,electronics,very few,No,No
+2025-04-17 00:57:21,user_0150,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:57:23,user_0193,click,medium,toys,high,Yes,No
+2025-04-17 00:57:25,user_0183,click,high,electronics,few,No,No
+2025-04-17 00:57:27,user_0134,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:29,user_0170,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:31,user_0113,add_to_cart,low,home,medium,No,No
+2025-04-17 00:57:33,user_0044,click,medium,home,high,Yes,Yes
+2025-04-17 00:57:35,user_0067,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:57:37,user_0146,click,high,books,few,Yes,No
+2025-04-17 00:57:39,user_0156,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:41,user_0157,view,low,books,very few,No,No
+2025-04-17 00:57:43,user_0066,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:45,user_0148,add_to_cart,medium,toys,very few,No,No
+2025-04-17 00:57:47,user_0184,view,low,books,high,No,No
+2025-04-17 00:57:49,user_0032,view,medium,home,few,No,No
+2025-04-17 00:57:51,user_0087,view,low,fashion,medium,No,No
+2025-04-17 00:57:53,user_0187,view,low,home,medium,No,No
diff --git a/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005813.csv b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005813.csv
new file mode 100644
index 0000000..20c2635
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005813.csv
@@ -0,0 +1,101 @@
+timestamp,user_id,event_type,session_duration,product_category,user_total_purchases,purchase,prediction
+2025-04-17 00:54:55,user_0109,add_to_cart,low,electronics,medium,Yes,No
+2025-04-17 00:54:57,user_0140,click,high,home,high,Yes,Yes
+2025-04-17 00:54:59,user_0137,add_to_cart,low,home,medium,Yes,Yes
+2025-04-17 00:55:01,user_0072,add_to_cart,low,books,few,No,Yes
+2025-04-17 00:55:03,user_0171,click,low,electronics,few,No,Yes
+2025-04-17 00:55:05,user_0050,add_to_cart,low,fashion,high,No,No
+2025-04-17 00:55:07,user_0177,add_to_cart,low,toys,very few,No,Yes
+2025-04-17 00:55:09,user_0120,click,low,fashion,very few,No,No
+2025-04-17 00:55:11,user_0026,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:55:13,user_0049,add_to_cart,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:15,user_0099,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:17,user_0154,click,medium,books,high,No,No
+2025-04-17 00:55:19,user_0143,add_to_cart,high,home,medium,Yes,No
+2025-04-17 00:55:21,user_0023,view,high,electronics,very few,No,No
+2025-04-17 00:55:23,user_0082,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:25,user_0009,add_to_cart,medium,home,medium,Yes,Yes
+2025-04-17 00:55:27,user_0196,add_to_cart,low,toys,high,No,Yes
+2025-04-17 00:55:29,user_0008,view,medium,home,very few,No,No
+2025-04-17 00:55:31,user_0111,click,high,electronics,few,Yes,No
+2025-04-17 00:55:33,user_0121,click,low,electronics,high,No,No
+2025-04-17 00:55:35,user_0192,click,high,fashion,few,Yes,No
+2025-04-17 00:55:37,user_0041,click,high,electronics,high,Yes,No
+2025-04-17 00:55:39,user_0106,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:55:41,user_0184,view,medium,books,high,No,No
+2025-04-17 00:55:43,user_0132,click,medium,fashion,high,Yes,No
+2025-04-17 00:55:45,user_0043,view,low,toys,medium,No,Yes
+2025-04-17 00:55:47,user_0006,view,high,fashion,medium,Yes,Yes
+2025-04-17 00:55:49,user_0164,view,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:51,user_0113,view,medium,fashion,medium,No,Yes
+2025-04-17 00:55:53,user_0197,add_to_cart,medium,toys,high,Yes,No
+2025-04-17 00:55:55,user_0104,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:55:57,user_0008,view,high,books,very few,No,No
+2025-04-17 00:55:59,user_0118,add_to_cart,medium,books,very few,No,No
+2025-04-17 00:56:01,user_0163,view,high,books,medium,Yes,Yes
+2025-04-17 00:56:03,user_0113,view,medium,toys,medium,No,No
+2025-04-17 00:56:05,user_0044,click,medium,toys,high,Yes,No
+2025-04-17 00:56:07,user_0087,click,high,home,medium,Yes,Yes
+2025-04-17 00:56:09,user_0179,view,medium,home,medium,No,No
+2025-04-17 00:56:11,user_0032,view,high,electronics,few,No,No
+2025-04-17 00:56:13,user_0108,add_to_cart,high,fashion,high,Yes,No
+2025-04-17 00:56:15,user_0053,add_to_cart,high,electronics,high,Yes,Yes
+2025-04-17 00:56:17,user_0012,click,low,toys,high,No,No
+2025-04-17 00:56:19,user_0019,click,low,electronics,high,No,No
+2025-04-17 00:56:21,user_0131,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:23,user_0103,add_to_cart,low,toys,medium,No,No
+2025-04-17 00:56:25,user_0022,view,medium,home,medium,No,Yes
+2025-04-17 00:56:27,user_0187,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:29,user_0106,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:31,user_0019,click,high,books,high,Yes,Yes
+2025-04-17 00:56:33,user_0044,view,high,home,high,Yes,Yes
+2025-04-17 00:56:35,user_0172,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:37,user_0096,add_to_cart,medium,electronics,medium,Yes,No
+2025-04-17 00:56:39,user_0179,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:56:41,user_0068,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:56:43,user_0173,view,low,electronics,high,No,No
+2025-04-17 00:56:45,user_0149,click,high,books,high,Yes,Yes
+2025-04-17 00:56:47,user_0076,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:49,user_0045,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:56:51,user_0086,click,high,home,high,Yes,Yes
+2025-04-17 00:56:53,user_0061,view,low,electronics,high,No,No
+2025-04-17 00:56:55,user_0118,view,low,toys,very few,No,No
+2025-04-17 00:56:57,user_0038,view,high,books,high,Yes,Yes
+2025-04-17 00:56:59,user_0098,click,low,fashion,medium,No,No
+2025-04-17 00:57:01,user_0165,click,high,electronics,high,Yes,Yes
+2025-04-17 00:57:03,user_0057,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:57:05,user_0029,view,medium,fashion,high,No,Yes
+2025-04-17 00:57:07,user_0057,view,high,toys,high,Yes,Yes
+2025-04-17 00:57:09,user_0133,click,high,home,high,Yes,Yes
+2025-04-17 00:57:11,user_0037,click,high,home,high,Yes,Yes
+2025-04-17 00:57:13,user_0143,view,medium,electronics,medium,No,Yes
+2025-04-17 00:57:15,user_0041,click,low,books,high,No,No
+2025-04-17 00:57:17,user_0007,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:19,user_0054,view,low,electronics,very few,No,No
+2025-04-17 00:57:21,user_0150,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:57:23,user_0193,click,medium,toys,high,Yes,No
+2025-04-17 00:57:25,user_0183,click,high,electronics,few,No,No
+2025-04-17 00:57:27,user_0134,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:29,user_0170,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:31,user_0113,add_to_cart,low,home,medium,No,No
+2025-04-17 00:57:33,user_0044,click,medium,home,high,Yes,Yes
+2025-04-17 00:57:35,user_0067,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:57:37,user_0146,click,high,books,few,Yes,No
+2025-04-17 00:57:39,user_0156,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:41,user_0157,view,low,books,very few,No,No
+2025-04-17 00:57:43,user_0066,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:45,user_0148,add_to_cart,medium,toys,very few,No,No
+2025-04-17 00:57:47,user_0184,view,low,books,high,No,No
+2025-04-17 00:57:49,user_0032,view,medium,home,few,No,No
+2025-04-17 00:57:51,user_0087,view,low,fashion,medium,No,No
+2025-04-17 00:57:53,user_0187,view,low,home,medium,No,No
+2025-04-17 00:57:55,user_0005,view,low,home,high,No,No
+2025-04-17 00:57:57,user_0149,click,high,fashion,high,Yes,Yes
+2025-04-17 00:57:59,user_0179,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:58:01,user_0159,add_to_cart,high,toys,high,Yes,Yes
+2025-04-17 00:58:03,user_0025,view,high,home,few,No,Yes
+2025-04-17 00:58:05,user_0136,click,low,fashion,medium,No,No
+2025-04-17 00:58:07,user_0027,view,medium,toys,very few,No,No
+2025-04-17 00:58:09,user_0002,view,high,toys,very few,No,No
+2025-04-17 00:58:11,user_0149,click,high,home,high,Yes,Yes
+2025-04-17 00:58:13,user_0084,click,high,books,very few,No,No
diff --git a/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005823.csv b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005823.csv
new file mode 100644
index 0000000..c3c8f9b
--- /dev/null
+++ b/src/PrescriptiveAnalysis2/streamlit_data/hoeff_events/events_20250417_005823.csv
@@ -0,0 +1,101 @@
+timestamp,user_id,event_type,session_duration,product_category,user_total_purchases,purchase,prediction
+2025-04-17 00:55:05,user_0050,add_to_cart,low,fashion,high,No,No
+2025-04-17 00:55:07,user_0177,add_to_cart,low,toys,very few,No,Yes
+2025-04-17 00:55:09,user_0120,click,low,fashion,very few,No,No
+2025-04-17 00:55:11,user_0026,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:55:13,user_0049,add_to_cart,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:15,user_0099,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:17,user_0154,click,medium,books,high,No,No
+2025-04-17 00:55:19,user_0143,add_to_cart,high,home,medium,Yes,No
+2025-04-17 00:55:21,user_0023,view,high,electronics,very few,No,No
+2025-04-17 00:55:23,user_0082,view,high,books,medium,Yes,Yes
+2025-04-17 00:55:25,user_0009,add_to_cart,medium,home,medium,Yes,Yes
+2025-04-17 00:55:27,user_0196,add_to_cart,low,toys,high,No,Yes
+2025-04-17 00:55:29,user_0008,view,medium,home,very few,No,No
+2025-04-17 00:55:31,user_0111,click,high,electronics,few,Yes,No
+2025-04-17 00:55:33,user_0121,click,low,electronics,high,No,No
+2025-04-17 00:55:35,user_0192,click,high,fashion,few,Yes,No
+2025-04-17 00:55:37,user_0041,click,high,electronics,high,Yes,No
+2025-04-17 00:55:39,user_0106,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:55:41,user_0184,view,medium,books,high,No,No
+2025-04-17 00:55:43,user_0132,click,medium,fashion,high,Yes,No
+2025-04-17 00:55:45,user_0043,view,low,toys,medium,No,Yes
+2025-04-17 00:55:47,user_0006,view,high,fashion,medium,Yes,Yes
+2025-04-17 00:55:49,user_0164,view,high,electronics,medium,Yes,Yes
+2025-04-17 00:55:51,user_0113,view,medium,fashion,medium,No,Yes
+2025-04-17 00:55:53,user_0197,add_to_cart,medium,toys,high,Yes,No
+2025-04-17 00:55:55,user_0104,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:55:57,user_0008,view,high,books,very few,No,No
+2025-04-17 00:55:59,user_0118,add_to_cart,medium,books,very few,No,No
+2025-04-17 00:56:01,user_0163,view,high,books,medium,Yes,Yes
+2025-04-17 00:56:03,user_0113,view,medium,toys,medium,No,No
+2025-04-17 00:56:05,user_0044,click,medium,toys,high,Yes,No
+2025-04-17 00:56:07,user_0087,click,high,home,medium,Yes,Yes
+2025-04-17 00:56:09,user_0179,view,medium,home,medium,No,No
+2025-04-17 00:56:11,user_0032,view,high,electronics,few,No,No
+2025-04-17 00:56:13,user_0108,add_to_cart,high,fashion,high,Yes,No
+2025-04-17 00:56:15,user_0053,add_to_cart,high,electronics,high,Yes,Yes
+2025-04-17 00:56:17,user_0012,click,low,toys,high,No,No
+2025-04-17 00:56:19,user_0019,click,low,electronics,high,No,No
+2025-04-17 00:56:21,user_0131,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:23,user_0103,add_to_cart,low,toys,medium,No,No
+2025-04-17 00:56:25,user_0022,view,medium,home,medium,No,Yes
+2025-04-17 00:56:27,user_0187,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:29,user_0106,add_to_cart,high,home,medium,Yes,Yes
+2025-04-17 00:56:31,user_0019,click,high,books,high,Yes,Yes
+2025-04-17 00:56:33,user_0044,view,high,home,high,Yes,Yes
+2025-04-17 00:56:35,user_0172,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:37,user_0096,add_to_cart,medium,electronics,medium,Yes,No
+2025-04-17 00:56:39,user_0179,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:56:41,user_0068,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:56:43,user_0173,view,low,electronics,high,No,No
+2025-04-17 00:56:45,user_0149,click,high,books,high,Yes,Yes
+2025-04-17 00:56:47,user_0076,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:56:49,user_0045,add_to_cart,high,toys,medium,Yes,Yes
+2025-04-17 00:56:51,user_0086,click,high,home,high,Yes,Yes
+2025-04-17 00:56:53,user_0061,view,low,electronics,high,No,No
+2025-04-17 00:56:55,user_0118,view,low,toys,very few,No,No
+2025-04-17 00:56:57,user_0038,view,high,books,high,Yes,Yes
+2025-04-17 00:56:59,user_0098,click,low,fashion,medium,No,No
+2025-04-17 00:57:01,user_0165,click,high,electronics,high,Yes,Yes
+2025-04-17 00:57:03,user_0057,add_to_cart,high,fashion,high,Yes,Yes
+2025-04-17 00:57:05,user_0029,view,medium,fashion,high,No,Yes
+2025-04-17 00:57:07,user_0057,view,high,toys,high,Yes,Yes
+2025-04-17 00:57:09,user_0133,click,high,home,high,Yes,Yes
+2025-04-17 00:57:11,user_0037,click,high,home,high,Yes,Yes
+2025-04-17 00:57:13,user_0143,view,medium,electronics,medium,No,Yes
+2025-04-17 00:57:15,user_0041,click,low,books,high,No,No
+2025-04-17 00:57:17,user_0007,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:19,user_0054,view,low,electronics,very few,No,No
+2025-04-17 00:57:21,user_0150,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:57:23,user_0193,click,medium,toys,high,Yes,No
+2025-04-17 00:57:25,user_0183,click,high,electronics,few,No,No
+2025-04-17 00:57:27,user_0134,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:29,user_0170,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:31,user_0113,add_to_cart,low,home,medium,No,No
+2025-04-17 00:57:33,user_0044,click,medium,home,high,Yes,Yes
+2025-04-17 00:57:35,user_0067,add_to_cart,high,electronics,very few,No,No
+2025-04-17 00:57:37,user_0146,click,high,books,few,Yes,No
+2025-04-17 00:57:39,user_0156,click,high,electronics,medium,Yes,Yes
+2025-04-17 00:57:41,user_0157,view,low,books,very few,No,No
+2025-04-17 00:57:43,user_0066,add_to_cart,low,toys,very few,No,No
+2025-04-17 00:57:45,user_0148,add_to_cart,medium,toys,very few,No,No
+2025-04-17 00:57:47,user_0184,view,low,books,high,No,No
+2025-04-17 00:57:49,user_0032,view,medium,home,few,No,No
+2025-04-17 00:57:51,user_0087,view,low,fashion,medium,No,No
+2025-04-17 00:57:53,user_0187,view,low,home,medium,No,No
+2025-04-17 00:57:55,user_0005,view,low,home,high,No,No
+2025-04-17 00:57:57,user_0149,click,high,fashion,high,Yes,Yes
+2025-04-17 00:57:59,user_0179,add_to_cart,high,books,medium,Yes,Yes
+2025-04-17 00:58:01,user_0159,add_to_cart,high,toys,high,Yes,Yes
+2025-04-17 00:58:03,user_0025,view,high,home,few,No,Yes
+2025-04-17 00:58:05,user_0136,click,low,fashion,medium,No,No
+2025-04-17 00:58:07,user_0027,view,medium,toys,very few,No,No
+2025-04-17 00:58:09,user_0002,view,high,toys,very few,No,No
+2025-04-17 00:58:11,user_0149,click,high,home,high,Yes,Yes
+2025-04-17 00:58:13,user_0084,click,high,books,very few,No,No
+2025-04-17 00:58:15,user_0046,view,high,electronics,medium,Yes,Yes
+2025-04-17 00:58:17,user_0164,click,low,fashion,medium,No,No
+2025-04-17 00:58:19,user_0030,click,high,fashion,medium,Yes,Yes
+2025-04-17 00:58:21,user_0064,click,low,fashion,high,No,No
+2025-04-17 00:58:23,user_0026,click,high,books,very few,No,No
diff --git a/tests/test_pytest.py b/tests/test_pytest.py
deleted file mode 100644
index 95c5ab7..0000000
--- a/tests/test_pytest.py
+++ /dev/null
@@ -1,2 +0,0 @@
-def test_example():
-    assert 1 + 1 == 2