diff --git a/Health Monitoring/Health-Monitoring.py.txt b/Health Monitoring/Health-Monitoring.py.txt
deleted file mode 100644
index 684cb10..0000000
--- a/Health Monitoring/Health-Monitoring.py.txt	
+++ /dev/null
@@ -1,117 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split, cross_val_score
-from sklearn.metrics import classification_report, confusion_matrix
-from sklearn.preprocessing import StandardScaler
-from sklearn.cluster import DBSCAN
-
-class HealthMonitoringSystem:
-    def __init__(self, data_size=1000):
-        self.data_size = data_size
-        self.data = None
-        self.model = None
-    
-    # Step 1: Generate Synthetic Data
-    def generate_data(self):
-        np.random.seed(42)
-        
-        # Simulating health metrics
-        heart_rate = np.random.randint(60, 120, self.data_size)  # Normal: 60-100 bpm
-        step_count = np.random.randint(0, 150, self.data_size)   # Normal: 0-150 steps
-        activity = np.random.choice([0, 1, 2], size=self.data_size, p=[0.5, 0.3, 0.2])  # 0: Resting, 1: Walking, 2: Running
-
-        # Combine into a DataFrame
-        self.data = pd.DataFrame({
-            "HeartRate": heart_rate,
-            "StepCount": step_count,
-            "Activity": activity
-        })
-        self.data['Timestamp'] = pd.date_range(start="2024-12-01", periods=self.data_size, freq='T')
-    
-    # Step 2: Data Preprocessing
-    def preprocess_data(self):
-        scaler = StandardScaler()
-        self.data[['HeartRate', 'StepCount']] = scaler.fit_transform(self.data[['HeartRate', 'StepCount']])
-    
-    # Step 3: Train Activity Classification Model
-    def train_model(self):
-        X = self.data[['HeartRate', 'StepCount']]
-        y = self.data['Activity']
-
-        # Train-test split
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
-
-        # Initialize Random Forest Classifier
-        self.model = RandomForestClassifier(n_estimators=100, random_state=42)
-
-        # Cross-validation
-        cv_scores = cross_val_score(self.model, X_train, y_train, cv=5, scoring='accuracy')
-        print(f"Cross-Validation Accuracy Scores: {cv_scores}")
-        print(f"Mean CV Accuracy: {cv_scores.mean():.4f}")
-
-        # Train model
-        self.model.fit(X_train, y_train)
-
-        # Evaluate model
-        y_pred = self.model.predict(X_test)
-        print("Classification Report:\n", classification_report(y_test, y_pred))
-        print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
-    
-    # Step 4: Anomaly Detection using DBSCAN
-    def detect_anomalies(self):
-        dbscan = DBSCAN(eps=0.3, min_samples=10)
-        anomaly_labels = dbscan.fit_predict(self.data[['HeartRate', 'StepCount']])
-        self.data['Anomaly'] = anomaly_labels
-
-        # Visualize anomalies
-        anomaly_data = self.data[self.data['Anomaly'] == -1]
-        print(f"Total Anomalies Detected: {len(anomaly_data)}")
-        
-        plt.figure(figsize=(12, 6))
-        plt.scatter(self.data['Timestamp'], self.data['HeartRate'], label='Heart Rate', alpha=0.7, color='blue')
-        plt.scatter(anomaly_data['Timestamp'], anomaly_data['HeartRate'], color='red', label='Anomalies')
-        plt.title('Heart Rate Over Time with Anomalies')
-        plt.xlabel('Timestamp')
-        plt.ylabel('Normalized Heart Rate')
-        plt.legend()
-        plt.xticks(rotation=45)
-        plt.show()
-    
-    # Step 5: Real-Time Data Simulation
-    def simulate_real_time(self, n=10):
-        print("\nReal-Time Data Simulation:\n")
-        for i in range(n):
-            sample = self.data.sample(1)
-            print(f"Time: {sample['Timestamp'].values[0]} | "
-                  f"Heart Rate: {sample['HeartRate'].values[0]:.2f} | "
-                  f"Step Count: {sample['StepCount'].values[0]:.2f} | "
-                  f"Activity: {self.activity_label(sample['Activity'].values[0])} | "
-                  f"Anomaly: {'Yes' if sample['Anomaly'].values[0] == -1 else 'No'}")
-    
-    # Step 6: Health Metrics Visualization
-    def plot_health_metrics(self):
-        plt.figure(figsize=(12, 6))
-        plt.plot(self.data['Timestamp'], self.data['HeartRate'], label='Heart Rate', color='red', alpha=0.7)
-        plt.plot(self.data['Timestamp'], self.data['StepCount'], label='Step Count', color='green', alpha=0.7)
-        plt.title('Health Metrics Over Time')
-        plt.xlabel('Timestamp')
-        plt.ylabel('Normalized Metrics')
-        plt.legend()
-        plt.xticks(rotation=45)
-        plt.show()
-    
-    # Helper Method: Convert activity codes to labels
-    def activity_label(self, activity_code):
-        labels = {0: 'Resting', 1: 'Walking', 2: 'Running'}
-        return labels.get(activity_code, 'Unknown')
-
-# Instantiate and run the system
-health_system = HealthMonitoringSystem()
-health_system.generate_data()
-health_system.preprocess_data()
-health_system.train_model()
-health_system.detect_anomalies()
-health_system.simulate_real_time()
-health_system.plot_health_metrics()